Meeting Title: Uttam <> Dan-Ben-Q1-24-Plan Date: 2023-12-22 Meeting participants: Daniel Schonfeld, Uttam Kumaran
WEBVTT
1 00:16:08.390 ⇒ 00:16:11.230 Daniel Schonfeld: Yeah. And it was on 80.
2 00:16:12.050 ⇒ 00:16:16.900 Daniel Schonfeld: Oh, okay, I know something else used to be their
3 00:16:17.080 ⇒ 00:16:20.119 not harvest. Then I forget. Hey, Buddy?
4 00:16:20.140 ⇒ 00:16:23.339 Uttam Kumaran: Hey, Dan? Hello, Dan in front, hey?
5 00:16:23.690 ⇒ 00:16:24.770 Uttam Kumaran: How’s it going?
6 00:16:25.050 ⇒ 00:16:30.749 Daniel Schonfeld: Yes. what’s going on, Buddy? How’s everything? Thanks for good.
7 00:16:30.980 ⇒ 00:16:44.089 Uttam Kumaran: Yeah. Yeah. Just going home for for Christmas, going back to the Bay Area. Nothing crazy this year. I think we’re just going to Monterey for the weekend.
8 00:16:44.530 ⇒ 00:16:47.880 Uttam Kumaran: yeah, that’s crazy. How about you guys, how’s it going
9 00:16:48.020 ⇒ 00:17:08.170 Uttam Kumaran: to today? Actually, yeah, today, later, today, it’s just a quick trip. I don’t know. This this year. I’ve I’ve I’ve been. I went back for my birthday in October. But I’m just like enjoying Austin too much. So just spending as much time here as possible. Cool
10 00:17:08.300 ⇒ 00:17:09.430 Daniel Schonfeld: 2 days ago.
11 00:17:11.859 ⇒ 00:17:18.010 Daniel Schonfeld: So how are things going? Just give me like anecdotally like, How how do you feel? Things are going with the reporting, everything else.
12 00:17:18.030 ⇒ 00:17:24.809 Uttam Kumaran: Yeah, overall, and I maybe I’ll even share a little bit. I just kind of like visually mapped out a couple of things.
13 00:17:26.630 ⇒ 00:17:31.100 Uttam Kumaran: so let me know if this is like way too
14 00:17:31.130 ⇒ 00:17:53.459 Uttam Kumaran: big. Pretty much like I went through and just said, Okay, that’s conversation. Here’s here’s generally like all the sources that we’re working with and I think early, I could talk a little bit about getting everything into the vital signs dashboard. But basically we go through and went through almost every single source. Whether it’s
15 00:17:53.540 ⇒ 00:18:18.720 Uttam Kumaran: an Api, or whether it’s more custom. I can talk a little bit about that, so I’ve generally everything settled on there. I think the issue that we’ll talk a little bit about today is some of the sources that are more like stubborn, which is like non Api, things that are custom that I’m I’m like, have to go to almost every vendor and say, Hey, like, what’s the way to kind of engineer this
16 00:18:18.840 ⇒ 00:18:29.529 Uttam Kumaran: so that’s everything on a marketing inventory shipping and sales side. All of these are coming in.
17 00:18:29.700 ⇒ 00:18:59.690 Uttam Kumaran: Yes. So Lto, the of course the stuff from Cj. Cj. Or direct mail that’s in the spreadsheet. But II assume that’s like II consider those like semi-track, because Kim is tracking those, and I’m able to take that from the spreadsheet. But of course I’m not getting that on a daily basis. That’s of course, on her reporting cadence. And then those are those. Those are the big things that are
18 00:19:00.020 ⇒ 00:19:13.149 Uttam Kumaran: like kind of off center Direct Mail, Ltl. And affiliate Amazon ads. We modeled attentive is coming in
19 00:19:14.570 ⇒ 00:19:19.000 Daniel Schonfeld: and you’re talking about just getting a pure Api. But you’re not able to do any kind of scrape.
20 00:19:19.620 ⇒ 00:19:27.949 Uttam Kumaran: I haven’t done a straight ui scrape. I could for Ltl, the site is like
21 00:19:28.380 ⇒ 00:19:34.769 Uttam Kumaran: pretty bad. But I you know we we established a relationship with both
22 00:19:34.880 ⇒ 00:19:42.200 Uttam Kumaran: like the Ups folks and the Fedex folks, and I told both of them I said, there’s no way you don’t have clients that are requesting
23 00:19:42.220 ⇒ 00:20:10.229 Uttam Kumaran: this stuff direct the Api like, can you connect me with someone on your tech team so we can get that? And we could set that up. The Fedex, you know I just need to. I just followed up with a guy yesterday, so I just need to keep pushing on. That ups is all coming through ship station. And and I also have. I actually have the rate sheets as well modeled from that. Pdf, so we’re able to say, like, what is something go shipped for. And then what do we quoted versus what actually gets shipped forward? That’s how we did a lot of the
24 00:20:10.820 ⇒ 00:20:16.190 Uttam Kumaran: ups contract, related Project. I’m just on the shipping side.
25 00:20:17.240 ⇒ 00:20:38.309 Uttam Kumaran: You’re saying that ups is flowing through on Api ups Fedex. Yeah. So I would say, Ltl, separate from Fedex Fedex normal shipments, all their shipping categories and up usps and like, sure post, that stuff is all coming in through shipstation. We have like the thing that’s not coming through. Is he pump free? That’s it.
26 00:20:38.490 ⇒ 00:20:45.980 Daniel Schonfeld: So, Fedex. Yes, consider Ltl. Fedex, which is which is Fedex. Just a separate thing.
27 00:20:46.560 ⇒ 00:20:48.620 Daniel Schonfeld: Wait. Sorry I’m confused. So.
28 00:20:48.820 ⇒ 00:20:52.610 Daniel Schonfeld: Oh, no. So right now we’re connected with
29 00:20:52.780 ⇒ 00:20:56.639 with ship station via Api. And you’re pulling in that data.
30 00:20:57.100 ⇒ 00:21:22.309 Uttam Kumaran: Yeah. So every every single shipment that goes out we have. The problem is, Ltl, freight is like a subsidiary of Fedex, and it’s a get. It’s so heavy emphasis on subsidiary like they seemingly are almost described it has, like a totally different entity. And that the only suggestion I’ve gotten is this, get straight from their website, which they only have 90 day past
31 00:21:22.650 ⇒ 00:21:29.449 Uttam Kumaran: records. And I, I’m I emailed the our Fedex wrap
32 00:21:29.740 ⇒ 00:21:34.069 Uttam Kumaran: pretty much, saying like, II don’t know what like. What do you expect us to do here.
33 00:21:34.510 ⇒ 00:21:40.660 Daniel Schonfeld: We were right now. right now we’re only seeing or knowing how much we pay 90 days later.
34 00:21:41.160 ⇒ 00:21:47.229 Uttam Kumaran: No, we only have 90 days in the past, meaning I can’t. I can’t look at
35 00:21:47.600 ⇒ 00:21:56.610 Uttam Kumaran: I can’t look at historical and and frankly like the process I have to go in and kind of manually go grab that
36 00:21:57.710 ⇒ 00:21:58.760 Uttam Kumaran: So
37 00:21:59.030 ⇒ 00:22:03.569 Daniel Schonfeld: again, III feel confident that I’m gonna be able to get through the Ltl freight.
38 00:22:03.640 ⇒ 00:22:12.290 Uttam Kumaran: Because I don’t. II just think they need to go run a report, and I need to get hooked up with an analyst there who can just send it to us on a daily basis.
39 00:22:12.460 ⇒ 00:22:20.860 Daniel Schonfeld: Okay? And then. So we’re also just something to note. we’re using unis unis for a lot of this stuff.
40 00:22:21.040 ⇒ 00:22:30.589 Daniel Schonfeld: Next year we’re already started using them. And so a good amount of heat pumps as well as variable speed pool pumps are going to be running through. There
41 00:22:30.920 ⇒ 00:22:38.580 Daniel Schonfeld: is that for the shipping side? Or, yeah, we’re S. We’re sending goods to Jacksonville, which we opened up.
42 00:22:40.230 ⇒ 00:22:45.379 Daniel Schonfeld: And there’s another one. because you’re gonna come into Carolina, then go to Jacksonville.
43 00:22:45.580 ⇒ 00:22:52.290 Daniel Schonfeld: I don’t. I don’t know. I’ll get with Chuck and find out exactly what the mix is. We’re gonna be getting on more. It’s UN is
44 00:22:53.050 ⇒ 00:22:55.150 is the company name?
45 00:22:55.340 ⇒ 00:23:10.610 Daniel Schonfeld: I know they have good Apis that already discusses with them. I know we’ve had a lot of problems. Not problems, but integration. It’s taken a while over the last 2 months to get real time receipts on certain shipment confirmations.
46 00:23:10.650 ⇒ 00:23:34.939 Daniel Schonfeld: Ltl, for for other things as well. I do. Yeah, it probably makes sense, for you have an initial conversation with Chuck. Just to delay the land, and he’ll have some good information and then go to Eunice, is Guy Michael Williams, who’s the main point of contact. But Chuck will know better the more technical contact
47 00:23:35.330 ⇒ 00:23:38.899 Daniel Schonfeld: there for ignition. I think they use
48 00:23:39.480 ⇒ 00:23:41.779 Daniel Schonfeld: anyway, some some to pay attention to, because
49 00:23:41.940 ⇒ 00:24:01.860 Daniel Schonfeld: the goal really is to get it off out of Yap Bank, especially the heat pumps. I just wouldn’t wanna absorb an amount of time trying to solve that. Ltl issue. If the plan is to get 90 to 100% of the heat pumps out of Japank. Which would be 100, but it could reduce down
50 00:24:01.860 ⇒ 00:24:13.719 Daniel Schonfeld: you know, to to 90% are going through different Dcs. Through Eunice we get a lot of shipments to Florida, Texas. Move those all down south
51 00:24:13.840 ⇒ 00:24:16.090 Daniel Schonfeld: and have the the hub be down there.
52 00:24:16.270 ⇒ 00:24:32.830 Uttam Kumaran: Yeah, II think the old. The only thing I’m gonna push on is for them to either get me the report manually or to work with me. But I I’m good. Also in parallel. I’ll start the relationship with Unis and figure out how to get that data.
53 00:24:33.660 ⇒ 00:24:41.170 Daniel Schonfeld: yeah, I know they have good Apis. That was one of the first things I asked them is that they do have good Apis, because they do integrate with shopify and others.
54 00:24:41.420 ⇒ 00:24:46.229 Daniel Schonfeld: We may even just be able to pull it right from shopify. Cause I believe we’re already getting the receipts itself.
55 00:24:46.240 ⇒ 00:25:09.129 Uttam Kumaran: Into there, that’s where you need to just talk, chunk. Yeah, that’s the the one thing that it’s been helpful having the ship station is before, especially when we’re on. We’re on like another Etl data movement provider. Nexla. We didn’t have any sort of dimensionality weight, like all the the level of dimensionality we have now, it’s all because I’m pulling direct from ship station. So
56 00:25:09.310 ⇒ 00:25:13.679 Uttam Kumaran: I’m going to hope that we can just get it direct from them, and then we will do the join.
57 00:25:13.880 ⇒ 00:25:25.190 Daniel Schonfeld: You know, on the back end to make sure everything lines up. And that’s kind of like what we’re doing now. Yeah, I think one thing we also need to incorporate at some point is a master sheet, because sometimes the prices
58 00:25:25.610 ⇒ 00:25:31.300 Daniel Schonfeld: this is just on a skew level. As we get to the future is for me to supply you with the master list
59 00:25:31.500 ⇒ 00:25:36.560 Daniel Schonfeld: of all the reasons. The price, the real costing for the year.
60 00:25:36.720 ⇒ 00:25:53.849 Daniel Schonfeld: Wanna track over time is if the cost go up or down on a skew level. This is this is separate of what we’re talking about with the shipping and all that system to have a master sheet on a skew. How does that relate to the data we’re getting from unleashed about the product?
61 00:25:54.140 ⇒ 00:25:59.780 Daniel Schonfeld: It should be. It should be similar. But some things happen like
62 00:26:00.110 ⇒ 00:26:08.009 Daniel Schonfeld: packaging could change and may not get updated and unleash. But yeah, you’re probably right. It should. Probably the master should probably update it and unleash and then
63 00:26:08.040 ⇒ 00:26:10.630 Daniel Schonfeld: spit out to the rest of the the systems
64 00:26:10.950 ⇒ 00:26:18.560 Daniel Schonfeld: a better route. I just didn’t know if you you want like a master truth system.
65 00:26:18.570 ⇒ 00:26:21.689 Uttam Kumaran: Yeah, that would be great to have that. Because
66 00:26:21.990 ⇒ 00:26:32.739 Uttam Kumaran: again, like, that’s the like, the main truth that we’ll we’ll kind of join everything to. And then pretty much again. All the data I’m getting at the skew level. And what I’m doing is actually
67 00:26:32.850 ⇒ 00:27:00.699 Uttam Kumaran: again, we’re we’re selling on 3 different platforms primarily to. I’m now combining those normalizing them and also at the product category level. You’re there’s a lot of variances with product name and a ton of differences. So again for me to both identify those. And then to start to say, Okay, we sell products around all these. But we wanted to say, we don’t. One universal product, category dimension. Right? And same thing like we normalize a lot of the regions and things like that. So
68 00:27:00.700 ⇒ 00:27:05.200 Uttam Kumaran: the other thing I talk to ben a lot about and chuck
69 00:27:05.200 ⇒ 00:27:24.049 Uttam Kumaran: in the past few months was a lot on supply chain, and inventory coming in from the suppliers. One thing that we were able to do is kind of build, like a basic forecast of based on the the manual tracking that we’re doing about supplies. And when they come in
70 00:27:24.050 ⇒ 00:27:46.549 Uttam Kumaran: turnover on inventory, right? And you know 1 one thing I and I guess I don’t know just understanding whether it’s important. I talked to Ben a little bit about. This was just inventory turnover from the from the actual warehouses, and then understanding like when shipments come in, what’s the error bars on that actually happening and where where we’re at risk on certain skews for running out of inventory?
71 00:27:46.970 ⇒ 00:27:55.610 Uttam Kumaran: again, I think that that we just decided was was something to to tackle later. But that was something that we talked about as we were doing a lot of the work for ups.
72 00:27:55.680 ⇒ 00:28:04.680 Daniel Schonfeld: Yeah, super important. You know. I think I think I said, first and foremost is, let’s get as all the data that we absolutely need to make decisions on in
73 00:28:04.820 ⇒ 00:28:17.670 Daniel Schonfeld: timely and accurate manner, and I think it’s still the number one priority getting all of our revenue, all of our all of our major costs in this system, and accurate reporting
74 00:28:17.790 ⇒ 00:28:19.710 Daniel Schonfeld: and then moving to
75 00:28:19.870 ⇒ 00:28:26.289 Daniel Schonfeld: really, next, phases are more predictive and forecasting style.
76 00:28:26.540 ⇒ 00:28:31.249 Daniel Schonfeld: and then really, like the phase 3 is like all the other stuff we talked about, which is like.
77 00:28:31.580 ⇒ 00:28:44.639 Daniel Schonfeld: you know, adding in certain variables, trying to be a little more, using more intelligence or machine. All the buzz words you wanna use to figure out how we optimize the company and the business and the way that we market and
78 00:28:44.750 ⇒ 00:28:47.290 Daniel Schonfeld: products we use. But
79 00:28:48.230 ⇒ 00:29:12.109 Daniel Schonfeld: yeah, II think some. The more manual meetings tasks we can automate the better, such as. And that’s a big one is predicting when we’re gonna run out of inventory where, especially since we’re using Unis this year extra hard to say fuck, we now have to look at we how many units we have left in Jacksonville versus what we typically sell in the South
80 00:29:12.550 ⇒ 00:29:20.149 Daniel Schonfeld: versus what we typically sell in the North in peak season, because we may have a lot of stock on paper if we just look at.
81 00:29:20.170 ⇒ 00:29:22.089 Daniel Schonfeld: But if we look down south
82 00:29:22.500 ⇒ 00:29:45.060 Daniel Schonfeld: we’ll say ship, we don’t have enough there too much up north. Let’s send a trailer load down to Jacksonville, so we can at least be ready for July, and most of the sales will happen in this in the South, south, east, southwest regions, so that can really be a big money saver, or vice versa if we don’t have that right. So I think that is kind of the next phase of this is
83 00:29:45.100 ⇒ 00:30:02.530 Daniel Schonfeld: understanding our inventory capacity. Turnover in by region. Really, yeah. And and I would say, even thinking about that problem on the sales side, we have, you know, pretty much every transaction data maps to those sort of regions. Think the biggest thing is on the where, where the things typically break is
84 00:30:02.530 ⇒ 00:30:26.050 Uttam Kumaran: where everything’s manual. Right? So I’ll hope to see that Unis gives us, like some reliable reporting on active inventory. And then what we do is we map that back to shipments right? And we can say, like, Hey, we have this many sales. This is the shipments, the differences, what’s on hold, and we can kind of be able to tell what the turnover is. And you know, I you know I give a call to some other people that worked in like part of inventory space and other thing they they really looked at was just like
85 00:30:26.080 ⇒ 00:30:38.440 Uttam Kumaran: shelf space and making sure that we have that things are moving off the shelf. And we’re not over ordering and that sort of stuff. I think we have a lot of opportunity to do, I think overall. Now, on the sales side we have
86 00:30:38.700 ⇒ 00:30:45.000 Uttam Kumaran: pretty much across both all the refunds, returns, fees, and discounts shopify fees
87 00:30:45.230 ⇒ 00:31:03.209 Uttam Kumaran: is difficult. Because it’s like a blanket on their shop payments. It’s not like it’s like a 2.6 on the payment side. So I and I’m and they’re not. They don’t like. I’m I may go and grab that manually.
88 00:31:04.120 ⇒ 00:31:12.630 Uttam Kumaran: But on the Amazon side it’s a of course it’s a it’s a lot more broke. We have pretty much on every single type of fee, and that’s all rolled up into profit. So
89 00:31:12.850 ⇒ 00:31:19.480 Daniel Schonfeld: yeah, point where we don’t want to cross over too much from like 0.
90 00:31:19.620 ⇒ 00:31:31.780 Daniel Schonfeld: like, we have 8 8 to X already pulling in a lot of data into 0 software. I’m trying to avoid doing too much duplication.
91 00:31:31.820 ⇒ 00:31:40.300 Uttam Kumaran: where? Like, if I need to go to a true pnl, I’m going to go to 0 and pull all that data. Yeah, I wouldn’t. So yeah, I totally agree. I don’t. I wouldn’t
92 00:31:40.350 ⇒ 00:31:50.190 Daniel Schonfeld: leverage this for the financial reconciliation for Ben and Dan probably once a week to sit down, take a look
93 00:31:50.410 ⇒ 00:31:52.979 Daniel Schonfeld: and say what’s going on? Because
94 00:31:53.170 ⇒ 00:32:05.159 Daniel Schonfeld: there’s the warning signals. Oh, they’re always there. They they form where we need to look for a problem or an optimization. You know, a percentage too high. It’s above where we’d like. And
95 00:32:05.460 ⇒ 00:32:12.939 Daniel Schonfeld: you see, that’s okay. Let me take a look now. For an accounting one. We’re not going to use this, but this is just
96 00:32:13.170 ⇒ 00:32:22.659 Daniel Schonfeld: our marketing, our sales, our refunds, all that, and just find if there’s a situation or an opportunity and shipping and
97 00:32:22.680 ⇒ 00:32:27.670 Daniel Schonfeld: and and overall marketing the basic like Cac and
98 00:32:28.560 ⇒ 00:32:37.410 Daniel Schonfeld: really, Cac is a big one and shipping just to really just like inform us if there’s a problem like we’re finding them from back in May. Now.
99 00:32:38.010 ⇒ 00:32:41.700 Daniel Schonfeld: one skew we lost 150 grand on
100 00:32:41.860 ⇒ 00:32:46.860 Daniel Schonfeld: some some of the months the shipping looks wonky like really bad
101 00:32:47.160 ⇒ 00:33:06.410 Daniel Schonfeld: we need to be able to catch those on a weekly, if not daily basis, but probably weekly, in a Friday meeting. Then I sit down and say, Okay, what was the a and obviously looking at percentages. What was the average shipping? Compared to sale? What was that ratio? And then we can always do a deeper dive into it?
102 00:33:06.740 ⇒ 00:33:18.229 Daniel Schonfeld: But something’s got to blare out at us and say, Holy shit the shipping. The the cost per shipping is now 20 versus the typical average of 10%. We need to dive into this.
103 00:33:18.360 ⇒ 00:33:31.120 Uttam Kumaran: Yeah. So even looking at something like this, like I, I’ve been looking at this, you know, most days and looking for those signals and trying to find like, okay, if I were to look at this, what’s some numbers? And I think it’s been pretty helpful to say, like, Oh, we’ve.
104 00:33:31.120 ⇒ 00:33:48.710 Uttam Kumaran: I think November was a really peak month, so a lot of the metrics were down. But then I think what was helpful with what you mentioned, which is like, we’re same month last year. We’re the same average day of week. And I. It’s really easy for us to actually do these calculations now, and just pretty much plug them in.
105 00:33:50.060 ⇒ 00:33:53.359 Uttam Kumaran: And the other thing is, I want to measure the ups.
106 00:33:53.710 ⇒ 00:34:17.329 Daniel Schonfeld: the improvements. Yeah, the only the only issues I’ve had with looking at this is like, I wanna just see like shipping costs from last week from on. Certain it’s just. Yes, it’s very good at a very high level. But II don’t, I trust, customizable. So whatever you want, yeah, I always try to orient myself. I like, what’s the year over year. What’s the month over month? What’s the week over week
107 00:34:17.409 ⇒ 00:34:23.429 Daniel Schonfeld: we’re should it be, or where was it? And then see if there’s any anomalies in that data that I’m not
108 00:34:23.719 ⇒ 00:34:44.309 Daniel Schonfeld: considering. Maybe the shipping. It doesn’t just tell me, oh, shit our shippings down or or good our shippings down 32. I may say, maybe we’re not selling as many larger items that we typically spend a lot on shipping. I’m more interested actually in the shipping to revenue ratio.
109 00:34:44.330 ⇒ 00:34:46.810 Daniel Schonfeld: Okay versus a certain time period.
110 00:34:46.860 ⇒ 00:34:56.670 Daniel Schonfeld: But I’m not not to diminish what’s here. It’s still important. Then I want to dive deeper. I’m like, I don’t know how to get this actually from this report, and I go into other ones. And
111 00:34:56.940 ⇒ 00:35:22.949 Uttam Kumaran: so let me let me show you one more. That’s a little bit higher level, and then maybe we can see where there’s some room to bridge a gap. I we worked on like a weekly in a monthly dashboard, and so maybe I’ll show you a couple of things there, and you can kind of let me know if anything stands out. It. It this this one was. You know, it’s it’s nicer because it is at a much more like higher level, where you’re able to see
112 00:35:22.950 ⇒ 00:35:53.300 Uttam Kumaran: you know, sales by month. And then also the the same week, last year, the growth that way, you’ll be able to say like, Oh, we had some really killer months versus like, okay, although you may say relative to last month, November was great. Well, I listened to a lot of what you guys mentioned about we wanna look at just versus last year often, and then looking at also like the key kpis, right, which is from my eyes, is like sales marketing, discount, and shipment. I think we could likely add refunds here.
113 00:35:53.300 ⇒ 00:36:10.820 Uttam Kumaran: and being able to see all those 5 or 6 kpis every single one every single month and then also being able to look at it on a weekly basis with some sort of like gradient, which is, hey? There’s some alarming discounts weeks. There’s some alarming, alarming being like
114 00:36:11.140 ⇒ 00:36:12.560 Daniel Schonfeld: this is refund. Yeah.
115 00:36:12.770 ⇒ 00:36:26.270 Uttam Kumaran: this is refund shipment costs. And then again, using some sort of like caller rate to just show like a this is like 3 standard deviations above the average. Right?
116 00:36:26.640 ⇒ 00:36:30.199 Uttam Kumaran: Yeah. So this, the the dark is like, yeah, the
117 00:36:30.380 ⇒ 00:36:42.599 Daniel Schonfeld: let’s say, let’s say, I’m looking at this right now, okay, like that. 19,000 total refund for that week is is, let’s say, Hi, just point to where you are. Okay. How do I? Then.
118 00:36:43.010 ⇒ 00:36:44.989 Daniel Schonfeld: yeah, so refund for.
119 00:36:45.370 ⇒ 00:37:03.909 Uttam Kumaran: So what I would do is and I let’s, I think I have this setup. Let’s just double check. This is drill. Yeah. So what I. What I think I would like to do is say, I wanna go here and say for that given month what for that given week, what would be great is going to see every single discount
120 00:37:03.950 ⇒ 00:37:21.060 Uttam Kumaran: and every single, every single refund. And so right now you’re right. It just comes up with just the week, but what I would, what I can do and I can configure is when you click into this it will expand to every line item in that week. I think that seems like.
121 00:37:22.230 ⇒ 00:37:35.959 Daniel Schonfeld: yeah, it would. It would spit out to one category level. And it would say, Oh, okay, cover pumps was where all the problems were. And then then I can pull the raw data from cover pumps.
122 00:37:36.030 ⇒ 00:37:54.840 Daniel Schonfeld: Just so you understand the discount. One is manipulated right? What we increase. That’s that one signifies a very big cyber. Monday, black Friday sale, or we 30. And we gave a
123 00:37:54.900 ⇒ 00:38:08.260 Daniel Schonfeld: 25% discount. We actually ended up winning. So that doesn’t. That only person that really knows that is like me, Cam and Mike, but that the context is important. But regarding refunds, if we have a bad batch of a product
124 00:38:08.660 ⇒ 00:38:20.379 Daniel Schonfeld: you might not know until we get slammed with returns. So if you open that report and and it’s all cover pumps that are coming back with say, Oh, my God! That truck full of cover pumps was bad.
125 00:38:20.580 ⇒ 00:38:31.879 Daniel Schonfeld: and we can train. So I think we start by class. So you know those you have cover pumps, pumps.
126 00:38:32.310 ⇒ 00:38:43.019 Daniel Schonfeld: you have heat pumps. Yeah. And then if we want, we click into one skew. Suppose the 2 horsepower variables, speed pump. We’ll say, Okay.
127 00:38:43.170 ⇒ 00:38:51.960 Daniel Schonfeld: there was a lot of these, and then you go into the skew and you can see all the transactions there. So I think those 2 layers by class billing into the skew.
128 00:38:52.390 ⇒ 00:39:00.120 Uttam Kumaran: Okay, let’s do that. What I’m gonna do is I’m gonna I’m gonna test out a couple of ways where once you see these figures, when you click into it
129 00:39:00.280 ⇒ 00:39:18.729 Uttam Kumaran: and you hit view underlying data, we’ll be able to kind of get those. And then, additionally, what we could have is, let’s say it’s even deeper, and you want to go deeper. There could be. It could be easy just to go to an explorer that has every single order for that week, and trying to build a chain from this high level metric down so
130 00:39:18.790 ⇒ 00:39:27.439 Uttam Kumaran: right because the gap is like between these that are aggregated at the weekly level from almost every from every single transaction that’s come in. And then
131 00:39:27.540 ⇒ 00:39:31.079 Uttam Kumaran: you’re right. I think the difficulty, even for me, when I go and look at things, is
132 00:39:31.200 ⇒ 00:39:46.760 Uttam Kumaran: it’s there’s a ton of skews, and especially when you break it up by name. All the names are changing. So I will add the product category. And then we can add categories or diminish that as needed somewhere behind the scenes.
133 00:39:46.860 ⇒ 00:40:06.759 Uttam Kumaran: Yeah, I have a product category that I’ve been testing with. But you know, we just started kind of me and Ben started talking about it this week and kinda described it as product class. So II just did it cause I was doing some analysis. And I created like a rough grouping. But typically like 5 to 7 group. Yeah.
134 00:40:06.760 ⇒ 00:40:19.190 Daniel Schonfeld: I was, gonna say, if you can just share with that grouping is, we may want to break it down to in ground pumps above ground pumps and then heat. Let me let me just find where it is. One sec.
135 00:40:41.120 ⇒ 00:40:51.899 Uttam Kumaran: Okay, you can send it after. Don’t worry about it. Okay, it’s that’s exactly it. It’s like a skew mapping.
136 00:40:52.040 ⇒ 00:40:59.430 Uttam Kumaran: or add another sub category. I’m sure it’s pretty. We’re going to keep it pretty high level.
137 00:40:59.770 ⇒ 00:41:01.250 Uttam Kumaran: Okay, okay, great.
138 00:41:01.610 ⇒ 00:41:07.080 Daniel Schonfeld: You got like in general, like, what we’re trying to do is be kind of like a general doctor.
139 00:41:07.110 ⇒ 00:41:15.500 Daniel Schonfeld: you know screening. So the tools we have to screen, and then we can drill down and define where the problem is. That’s really what Dan and I do.
140 00:41:15.680 ⇒ 00:41:18.250 Daniel Schonfeld: And is this sort of alerting?
141 00:41:18.840 ⇒ 00:41:32.570 Daniel Schonfeld: I like this. I didn’t even I didn’t. I’ve again. I just haven’t been spending a lot of time in this until you guys give me the thumbs up that you’re getting all because if it’s incomplete by any one number, it’s not worth me totally
142 00:41:32.630 ⇒ 00:41:51.750 Daniel Schonfeld: as a guide. Yeah, yeah, it’s helpful. It’s helpful. He’s gonna end up with a lot of phone calls to me from Dan saying, You know, oh, my God! Alert! And I’ll say, yes, that’s why I’ll say that’s that’s Black Friday, like the first time I went into it. Remember, I said, email like, this makes no no sense at all.
143 00:41:52.090 ⇒ 00:41:57.420 Daniel Schonfeld: So I just I don’t wanna frustrate you guys and myself by spending too much time until you tell me
144 00:41:57.430 ⇒ 00:42:10.959 Daniel Schonfeld: I’ve gone in. I’ve put in all the revenue, all the costs that we’ve asked for. And I’ve I’ve qaed it throughout the the platform, and you feel good about it. It’s not. It’s like, Okay, Dan, go in now and just spend a lot of time with it.
145 00:42:11.100 ⇒ 00:42:27.439 Daniel Schonfeld: And then I’ll write down all my, the things I’m finding that are anomalies or problems, and then I’ll give it back to you guys and you’ll keep refining it. Yeah, heat pumps. Shipping is the only missing piece at this point. I don’t think there’s any data that’s not accurate outside of that. But that’s that’s like, literally like.
146 00:42:27.710 ⇒ 00:42:35.560 Daniel Schonfeld: basically the biggest cost we have. II don’t need to be a dickhead. But you guys said that last time, and within 40 major problem
147 00:42:35.840 ⇒ 00:42:42.990 Daniel Schonfeld: makes sense. No, no, you’re you’re totally right. There’s a lot of numbers. So there’s gonna there’s going to be a lot of problems.
148 00:42:43.010 ⇒ 00:43:03.030 Uttam Kumaran: Yeah, you know. And but I think the you know the other thing that I think we did. There’s some stuff behind the scenes. Is we migrated everything from Nexla to 5 Tran, and now we like, you know, given like Amazon. I not only can see all of the different runs that have happened like when
149 00:43:03.220 ⇒ 00:43:10.019 Uttam Kumaran: but I’ll we also have access to way more data stuff like, I’m actually like not bringing in, but
150 00:43:10.160 ⇒ 00:43:30.060 Uttam Kumaran: like tons of sophistication. So I’m really happy with the way like this, migration is gone. And additionally, I’ve used this tool for a long time, and I know a lot of the people there. So, for example, for unleashed, I’m gonna work with them to build an unleashed tool so we can move that same with Walmart. So that’s why I you know, I, we tried to say, Okay.
151 00:43:30.110 ⇒ 00:43:44.319 Daniel Schonfeld: it’s like going by my line. Okay, data. Stay. Okay, great. Let’s like, nip that. Okay, there’s all these random sources to the side. Let’s find a way to bring that to some sort of automated poll and then it’s up on the on the dashboarding side. We we should have
152 00:43:44.440 ⇒ 00:43:46.659 Daniel Schonfeld: abandon next law on day. One
153 00:43:47.680 ⇒ 00:43:56.429 Uttam Kumaran: it yeah, it’s and II you know, we I think we save some money getting rid of them. And they they were comp really like not very responsive. But
154 00:43:56.760 ⇒ 00:44:13.319 Uttam Kumaran: I’m glad it worked out. And we actually we again, we from moving from there, we not only just moved one for one, it’s like a one for 10. We got so much more data, especially on the ship station side that we really needed for that ups contract stuff so high, level overview. What you guys did to figure out the shipping.
155 00:44:13.430 ⇒ 00:44:16.429 Daniel Schonfeld: the zones and all that. What was the process you went through to do that?
156 00:44:16.950 ⇒ 00:44:37.080 Uttam Kumaran: Yeah, I would say part of it was going through and looking at every single. So the first thing was, I went through and just said, Do we have all the shipping costs for every single package that leaves and the shipment provider right? And so so the kind of the ask from Ben was, Hey, let’s
157 00:44:37.080 ⇒ 00:44:53.349 Uttam Kumaran: are we getting enough discounts? And if we’re able to get really negotiate discounts, we can move stuff to the best provider. So we talked a little bit about picking the best provider based on price. But additionally, what I said is, I need to know what their quoted rating prices are versus what we’re actually getting billed.
158 00:44:53.350 ⇒ 00:45:08.740 Uttam Kumaran: The problem is like they don’t provide us with like. Here’s the quotes. It’s like a contract where it’s like, if the length is this and the width is this, then use this. So I go in. And I literally code all that rules, all those rules. And I get the zone.
159 00:45:08.820 ⇒ 00:45:23.549 Uttam Kumaran: They give you a zone by weight pricing, and then they also give you like. If it’s if the box is like abnormally sized, there’s a separate pricing. So all those get written in SQL. And then what I’m able to do is for every package. I normally see what we were.
160 00:45:23.570 ⇒ 00:45:33.679 Uttam Kumaran: what we paid for shipping. I can also see what the quoted price was based on our existing contract. Then what I did is we called Kelly, and you know we pretty much said like.
161 00:45:33.700 ⇒ 00:45:44.239 Uttam Kumaran: we want to move a lot of volume to you. Like, let’s talk about discounts. She gave us an initial quote. I took that coded that. So now we can compare
162 00:45:44.390 ⇒ 00:45:54.650 Uttam Kumaran: existing to new this new quote. And then I can also forecast. So I can say, let’s say we do 20% volume next year. What’s the impact. If we
163 00:45:54.660 ⇒ 00:46:12.070 Uttam Kumaran: leverage the new quote. Given that we don’t, we don’t change anything. And then that that way, we were able to see like, okay. And then the the other thing I found out was there was these fees right? So even when we did that, I was like, well, it’s still not lining up and then there was like additional handing fees, large pick packaging so pretty much like.
164 00:46:12.210 ⇒ 00:46:16.389 Uttam Kumaran: So almost go by line, line by line and find out. Okay, I need you to slash
165 00:46:16.530 ⇒ 00:46:23.160 Daniel Schonfeld: our fees in half. I need to get rid of these fees. And so we kind of like were able to do that did you
166 00:46:23.480 ⇒ 00:46:30.470 Daniel Schonfeld: back test or figure out what the cost savings was over the course of the X amount of time going backwards?
167 00:46:30.610 ⇒ 00:46:54.069 Daniel Schonfeld: Yes, I think I shared some of that with Ben. We were just yeah. I mean, yeah. It was supposed it was enormous, because if you even look at our previous contract. There was no we. We now get a like a plethora of discounts on so much stuff, which is great, you know we really push for so is that across the entire skew base or certain package sizes and zones.
168 00:46:55.090 ⇒ 00:47:05.270 Daniel Schonfeld: everything, everything but yeah. And then the thing is like, when you get to the when you get to the higher zones like
169 00:47:05.310 ⇒ 00:47:21.189 Uttam Kumaran: when you get to like zone 7, 8. The prices are just really high, so you won’t see the impact. We saw the impact in the middle zones like 3 to 6 is where we saw a ton of impact. And you know, we? We have like $40,000 with the fees that we’ve that we’ve now pretty much like
170 00:47:21.260 ⇒ 00:47:24.009 Uttam Kumaran: basically eliminated
171 00:47:24.370 ⇒ 00:47:31.070 Daniel Schonfeld: So we we spend like 2 million bucks already year to date on shipping. Are you mean to tell me that we could have saved 600 k.
172 00:47:32.570 ⇒ 00:47:38.729 Uttam Kumaran: Well, the thing the thing is, it’s just just based on our ups. Allocated spend? Right? So
173 00:47:38.870 ⇒ 00:47:40.370 Daniel Schonfeld: ups. Only
174 00:47:41.200 ⇒ 00:47:51.810 Daniel Schonfeld: so, not he pumps. So you pull. He pumps out. And then that’s what you’re like, was ups what we were solely using for all shipping or yapping. Not Fedex also. So what total was ups?
175 00:47:52.380 ⇒ 00:48:10.800 Uttam Kumaran: I don’t think it was. It was that. Okay? I mean, like, here’s an example. This is what I kinda use to look at by skew. Here’s like the ship, the pretty much the per shipping cost, and hold on one sec. Aria. What am I looking at? Skew year? This is year to date.
176 00:48:11.260 ⇒ 00:48:35.160 Uttam Kumaran: So this is for every single month by zone, what we’ve paid for shipping. And then this is also like what what the ups rate at the time was, and then what the Fedex rate at the time was the reason why we didn’t go with ups. One, I think it was a like we were primarily on Fedex, and second is, the the discount structure wasn’t comparable.
177 00:48:35.170 ⇒ 00:48:47.070 Uttam Kumaran: W. We weren’t getting better rates with ups, so it didn’t make sense at the time. It was the actual new and I and I wish I just had.
178 00:48:47.330 ⇒ 00:49:11.550 Daniel Schonfeld: I’m still not oriented just yet. So zone. Just give me a hypothetical zone. One is the closest. And so how are we using ups for Fedex?
179 00:49:11.550 ⇒ 00:49:20.159 Daniel Schonfeld: How do we make a decision on which one to use at any given time? Well, in, for the most part we’ve been using Fedex, because we’ve got a reduction from them volume.
180 00:49:20.170 ⇒ 00:49:33.480 Daniel Schonfeld: yeah, like a year and a half ago. Why would we have used ups in any shipment versus for that we wouldn’t have. And then I had Chuck get a discussion with them about variable speed pumps, and they they offer to go lower.
181 00:49:33.960 ⇒ 00:49:48.190 Daniel Schonfeld: That was basically it. And then we started using that use them. And I said, my plan was that was always basically to dangle all of our shipping and say we do X 1 million with Fedex. Can you repeat them? That was my plan with them.
182 00:49:48.680 ⇒ 00:49:53.669 Daniel Schonfeld: They didn’t, but we ended up doing it on a huge double whammy, because we had with Tom
183 00:49:53.920 ⇒ 00:50:04.940 Daniel Schonfeld: prove, with data that they were tearing us apart. I was just gonna do it like, you know, you’re getting a tiny piece of the business. The rest of the pie is over with Fedex. Just beat them. But how long
184 00:50:05.280 ⇒ 00:50:15.000 Daniel Schonfeld: we were always using Fedex right through as our sole provider of of non heat pumps. When do we start using ups?
185 00:50:15.080 ⇒ 00:50:21.849 Daniel Schonfeld: 6 months. I’m guessing 6 months ago. Okay? And that’s okay. And then.
186 00:50:22.020 ⇒ 00:50:24.009 Daniel Schonfeld: so that’s in here.
187 00:50:24.360 ⇒ 00:50:27.119 Daniel Schonfeld: But so then why, if you go all the way left
188 00:50:30.540 ⇒ 00:50:39.489 Daniel Schonfeld: again, I’m just trying to orient myself, not questioning. Just scroll all the way to the left. There the other way. Give me one sec. It’s just loading.
189 00:50:40.060 ⇒ 00:50:42.559 Uttam Kumaran: That might be my laptop. Yeah.
190 00:50:45.580 ⇒ 00:50:54.649 Daniel Schonfeld: anyways. So so just even just looking at it. What are you trying to understand? That’s the question. Why is, if we just started using Uvs 6 months ago? Why in January.
191 00:50:54.780 ⇒ 00:51:19.279 Daniel Schonfeld: were we spending more on ups? I’m not understanding this. This isn’t. This isn’t what we’re spending. This is the rate of which we, if we spent with ups. Oh, so we weren’t actually this, these aren’t actual thi, these are these are me comparing. If on our, on our existing contract, if we were to move the volume over what the totals would be.
192 00:51:19.350 ⇒ 00:51:31.860 Uttam Kumaran: and then that’s the baseline. So then, when we go to Kelly, it’s pretty much like I need the new figures to be able to say now that we have 2024, quoted Price, what does the impact look like?
193 00:51:32.170 ⇒ 00:51:41.749 Daniel Schonfeld: Okay? But what just just breakdown zone? One. The top. 3 lines. What? What does this represent? The total shipping those 52921. Where? What is that number? Where’d that come from?
194 00:51:42.580 ⇒ 00:51:43.900 So that’s
195 00:51:43.910 ⇒ 00:51:49.640 Uttam Kumaran: so. This would be an example of how much we spent on packages for zone. One
196 00:51:49.840 ⇒ 00:51:52.250 Daniel Schonfeld: went to Zone one.
197 00:51:53.140 ⇒ 00:52:15.870 Uttam Kumaran: This is what we did spend. Okay, we spend 529. Who did we spend that with? But that that gets us sorted through Fedex, partly for ups partly for usps. This is like if we move all of our packages to and whatever that period of time is, and we use for
198 00:52:15.970 ⇒ 00:52:19.410 Daniel Schonfeld: for currently or brushes usps.
199 00:52:19.850 ⇒ 00:52:22.180 Daniel Schonfeld: and then Fedex ups
200 00:52:22.680 ⇒ 00:52:39.569 Daniel Schonfeld: as needed out of that 5, 29, and then the ups rate you have there is now, with the assumption of their new rates that they were quoting you. If we’d use them for all of that, everything went through them it would have been 414.
201 00:52:39.950 ⇒ 00:52:44.539 Uttam Kumaran: So this was for just the packages.
202 00:52:45.550 ⇒ 00:53:15.030 Uttam Kumaran: and I don’t even know whether at this point we have sure post specific ups quote. So I maybe it may be not considering sure post. But the analysis we did initially was looking at. What did we spend on packages and aggregate? If we moved 100% of that volume to ups on our current contract. What’s the price? If we 100% that to Fedex, what’s the price? And then I was able to say they’re not competing at all. And so when we go to them where it’s like
203 00:53:15.500 ⇒ 00:53:28.049 Uttam Kumaran: I, you need to be aggressive in them. But what I could do at that point is, say, give me the new rate sheet and the new calculation. So they did a host of different things. And it’s just like II was like. So it’s crazy. How much
204 00:53:28.200 ⇒ 00:53:54.859 Uttam Kumaran: like weird pricing they have. But they were able to do. Not only like per box discounts based on type of box. There’s like a ramp up period discount. There’s an admiral box discount. There’s a reduction of fees. So as they come back to us with those changes I’m able to measure impact. I this isn’t the right chart to show that II will. I’ll take a screenshot of what we were discussing at the time. Between me, Ben and Chuck.
205 00:53:54.870 ⇒ 00:54:02.240 Daniel Schonfeld: I have a host of stuff. That’s okay. I just want to worry myself. So again, just to recap the top one.
206 00:54:02.360 ⇒ 00:54:27.620 Daniel Schonfeld: The the total shipping cost is what we actually paid during that month. The next 2 rows or columns ups, and the Fedex rate were, if we put a hundred percent of that, 5, 29 shippings into just ups is would represent the 41498. Or if we put a hundred percent of whatever that 5 2921 was into Fedex, it would have been 377 34.
207 00:54:27.770 ⇒ 00:54:36.959 Daniel Schonfeld: Is that accurate? So that’s what I’m looking at. And what you’re saying is, you were able to get a 30% reduction
208 00:54:37.220 ⇒ 00:54:42.459 Daniel Schonfeld: from ups off of what? Off of that? 414
209 00:54:44.000 ⇒ 00:54:47.209 Uttam Kumaran: on. On an actual, on almost on a per
210 00:54:47.840 ⇒ 00:55:05.609 Uttam Kumaran: like. If we were to take yeah, I would say, the way to explain it is, if we were to ship everything with ups. It’s a 30% discount on what was previously quoted. So I, of course, like I, yeah, which is 98. What was previously quoted.
211 00:55:06.170 ⇒ 00:55:13.470 Daniel Schonfeld: yes, that was our existing contract. Okay, so it’s a 30 reduction from the 448.
212 00:55:14.140 ⇒ 00:55:29.350 Uttam Kumaran: And you, you see where I’m looking at right zone one. II see where you’re looking at. Yeah, I would. I would say, that’s that’s accurate, because we basically had no discounts. So it would. It would all it would apply that way to basically 30 to 40% discount on ups quoted. And
213 00:55:29.820 ⇒ 00:55:39.880 Daniel Schonfeld: sorry. Not you text me about nonsense. The garage door.
214 00:55:39.970 ⇒ 00:55:45.069 Daniel Schonfeld: don’t get married.
215 00:55:45.510 ⇒ 00:55:49.889 Daniel Schonfeld: So okay, so the ups rate would be reduced by a hundred $20.
216 00:55:50.470 ⇒ 00:56:19.140 Daniel Schonfeld: Yeah, I’m gonna I’m gonna send. I’m gonna send you a per package and then also a total, and I’ll I’ll put it into a little dash. I have it scattered everywhere. But just let me just say one more point. I just wanna see if I’m correct on this. Yeah, you receive a 30 reduction on ups rate from the 414. It’s significantly, it’s almost a 40 plus percentage
217 00:56:19.200 ⇒ 00:56:22.049 Daniel Schonfeld: cut on the total that we were paying.
218 00:56:23.440 ⇒ 00:56:28.610 Daniel Schonfeld: Yes, that. But that assumes that we move the volume over
219 00:56:28.700 ⇒ 00:56:32.149 Uttam Kumaran: right? Because it’s not apples to apples. Yeah.
220 00:56:32.470 ⇒ 00:56:34.750 Daniel Schonfeld: the white. Okay.
221 00:56:35.490 ⇒ 00:56:45.609 Daniel Schonfeld: okay. But that’s the whole point of the whole thing, for Tom is is give them as much as possible, so we can get as much as possible. Have we gone back and say, Hey, look what ups gave us that? Yeah. But Fedex wasn’t
222 00:56:45.750 ⇒ 00:56:51.379 Daniel Schonfeld: Ups wanted it more. That was clear. They made that clear in every way possible.
223 00:56:51.540 ⇒ 00:57:04.290 Uttam Kumaran: the person answered. When they’re on vacation, Fedex was, you know, answering slow. They weren’t giving the same level of Yeah, we we we’re still in contact with the Fedex folks. I but again I would say, there
224 00:57:04.410 ⇒ 00:57:10.930 Uttam Kumaran: they’re the part of the business that they have a real stranglehold on is the freight. So that is where I think we need to
225 00:57:11.420 ⇒ 00:57:13.619 Daniel Schonfeld: attack hopefully.
226 00:57:13.890 ⇒ 00:57:19.370 Daniel Schonfeld: Yeah, they’re using local. But II just have a couple more questions with us.
227 00:57:19.920 ⇒ 00:57:26.500 Daniel Schonfeld: was really for both you guys. is there any difference on service
228 00:57:27.000 ⇒ 00:57:31.920 Daniel Schonfeld: time to delivery between Ups and Fedex and or insurance
229 00:57:32.150 ⇒ 00:57:50.059 Daniel Schonfeld: that they cover things when they’re broken like any other. Besides price? Are there any other factors that would make ups or Fedex more favorable for our shipment. Specifically, quality of service. There’s no difference. They’re both responsible. Well, they’re equally responsible, irresponsible
230 00:57:50.140 ⇒ 00:58:03.279 Daniel Schonfeld: insurance. I think they’re competitive with each other, meaning if if there’s damage and we go through a claim, it’s same same sort of situation, cause I’ve never I get checked. So I come into this office and I see a Fedex
231 00:58:03.340 ⇒ 00:58:12.180 Daniel Schonfeld: notice, and I open it sometimes 16, grand, sometimes 20, sometimes 2 never received the ups. One ups has not had all our business until days ago.
232 00:58:12.270 ⇒ 00:58:14.789 Daniel Schonfeld: Okay? And I also think if I can pay for this.
233 00:58:15.250 ⇒ 00:58:19.049 Daniel Schonfeld: which is part of the perhaps they can charge nice
234 00:58:19.280 ⇒ 00:58:24.230 Daniel Schonfeld: and so where would those refunds be going? They’d be going directly to the account.
235 00:58:24.530 ⇒ 00:58:32.200 Daniel Schonfeld: Okay, that’s something we should. We pay them digitally. So it’s it’s just not okay. So Dan, I have. I think I have
236 00:58:33.150 ⇒ 00:58:38.089 Daniel Schonfeld: sorry. Yeah, I have one more. So this is actually the accurate view of
237 00:58:38.200 ⇒ 00:58:58.240 Uttam Kumaran: our existing ups orders that we shipped through ups the quoted price based on our previous contract and the impact of our new contract. So let’s take, let’s take August, for example, we shipped 82 orders with them. This is the total weight. This is the total shipment cost
238 00:58:58.520 ⇒ 00:59:17.360 Uttam Kumaran: this. This is like actually what we paid, so there will be some variation between what’s quoted. But generally we we have 4,000 and just the rate charges and an additional amount in additional handling charges. And then what? Based on our new contract, we actually would have almost
239 00:59:17.710 ⇒ 00:59:31.809 Uttam Kumaran: half of that in additional handling charges, and nearly half of that in just the rate itself. So if you look at total shipping costs, we moved around $133,000 with them.
240 00:59:31.920 ⇒ 00:59:52.240 Uttam Kumaran: That would have been reduced almost in half. And this doesn’t. This doesn’t consider a couple of things one, as we move more volume with them, that they have that little rate chart in the contract. We actually get additional discounts. So it’s like, there’s like a volume based discount. And then we’re also got some favorable things, I think around sure post usps.
241 00:59:52.700 ⇒ 00:59:55.109 Daniel Schonfeld: So this was
242 00:59:56.170 ⇒ 00:59:58.670 Uttam Kumaran: that’s for brushes.
243 00:59:59.750 ⇒ 01:00:04.340 Daniel Schonfeld: that’s for brushes and less than a pound. Yeah.
244 01:00:05.670 ⇒ 01:00:06.570 Daniel Schonfeld: awesome.
245 01:00:07.400 ⇒ 01:00:20.160 Uttam Kumaran: So so this is primarily what we looked at. We did some stuff on zones, and they they weren’t able to. They did some stuff on the Zone side on the share post, but we pushed every, I think tried to push almost every button we could. But
246 01:00:20.320 ⇒ 01:00:29.050 Daniel Schonfeld: yeah, I we pretty much went with a screenshot. I was like, this is what we’re looking at. This is amazing. This is a great job. There. I mean, you save us hundreds of thousands of dollars on this.
247 01:00:30.060 ⇒ 01:00:37.910 Uttam Kumaran: Yeah, I, you know I think it was. It was really great, and you know I hope we can go back to Fedex and push them too. But I’m glad also, when we go to Unis
248 01:00:38.100 ⇒ 01:00:39.560 Uttam Kumaran: we have a lot of this
249 01:00:40.080 ⇒ 01:00:44.619 Daniel Schonfeld: set up to be able to.
250 01:00:46.940 ⇒ 01:00:50.500 Daniel Schonfeld: I don’t. I don’t know. Probably like $600,000 just on this exercise.
251 01:00:52.050 ⇒ 01:00:54.979 Uttam Kumaran: Yeah. And I think the last thing we also have a really, I think, though
252 01:00:55.400 ⇒ 01:00:59.140 Daniel Schonfeld: next week will be
253 01:00:59.370 ⇒ 01:01:08.490 Daniel Schonfeld: north of 7 days of ups like with ups. Now listen. Business is slow. We’re not in a good time here for the pool stuff, but
254 01:01:08.640 ⇒ 01:01:11.610 Daniel Schonfeld: I’d love a report, probably next Friday.
255 01:01:12.210 ⇒ 01:01:20.380 Daniel Schonfeld: with how we did. Well, continue on, and just keep feeding into actuals. Now.
256 01:01:20.530 ⇒ 01:01:23.069 Uttam Kumaran: yeah. So I will actually remove
257 01:01:23.270 ⇒ 01:01:44.490 Uttam Kumaran: I. So do you? The to 2024 rate will become our standard rate, and then we have actuals. And then what I’ll do is, yeah. I’ll try to normalize the last weeks just by looking at skew by skew from last year and try to get a sense of like if we’re what sort of savings we’re realizing
258 01:01:45.440 ⇒ 01:01:53.829 Uttam Kumaran: And we can just go through every shipment almost and kind of look at, based on our old quote versus the the new actuals that that actually will be the best
259 01:01:53.950 ⇒ 01:02:03.990 Daniel Schonfeld: comparison. And, by the way, this Kelly is so good, Kelly Kelly is our rep at us. Let’s do a week. Let’s set up a weekly call for 10 min.
260 01:02:04.330 ⇒ 01:02:13.539 Daniel Schonfeld: and we’ll just at least for the first next month, or starting after January, when we get back to just do a quick 5, 10 min review on this.
261 01:02:13.650 ⇒ 01:02:38.229 Daniel Schonfeld: and just make sure that where we where we are, where we say we need to be. By the way, if we see something is a skew which will happen. Probably we can get right to Kelly and say, by the way, this you know, things been zone, or don’t look quite right, and she’ll handle it. She’s she was, I mean. I talked to her a lot privately, not involved with. She’s a pleasure, and she’s hungry, hungry, hungry.
262 01:02:38.380 ⇒ 01:02:46.179 Uttam Kumaran: Yeah, that’s what I that’s why I totally, I think not. Only that. Like the guy from Fedex. Yeah, I don’t. It was gonna be.
263 01:02:46.230 ⇒ 01:03:06.519 Uttam Kumaran: Yeah, Kelly, she she went back, and again we we, I think we pushed like pretty much to the bone on on getting ton of discounts and even moving some some items within the contract that II think II pretty much went back and said, Hey, we’re we’re a seasonal business. Go check with, you know all of your other seasonal business customers, and see
264 01:03:06.740 ⇒ 01:03:23.619 Daniel Schonfeld: what sort of interesting discount structure. And so they really worked with us on that which is great. You guys did an awesome job. This is. This is our single biggest outlandish expense is shipping. I knew it so been out of whack. But this this and figuring out the inventory ordering and mix
265 01:03:23.670 ⇒ 01:03:26.540 Daniel Schonfeld: are the 2 biggest levers we have right now in the poll.
266 01:03:27.040 ⇒ 01:03:35.169 Daniel Schonfeld: To make instant changes to our bottom line. This is, gonna make an instant change and then figure out what inventory we need when and where
267 01:03:35.270 ⇒ 01:03:44.120 Daniel Schonfeld: is the second one? And then the third one’s gonna be getting into all the marketing. But these are the. This is the number one lever. This is the biggest one.
268 01:03:44.440 ⇒ 01:03:52.480 Daniel Schonfeld: I wish we could have done it sooner. But the problem was, we didn’t have the volume and the track record until this year. Yeah, where we proved another big year.
269 01:03:52.740 ⇒ 01:03:59.079 Daniel Schonfeld: Yeah, I remember the first couple of times I talked to Fedex. They were just kind of like, yeah, like, you guys are tiny like, we can’t give you any, you know.
270 01:03:59.480 ⇒ 01:04:15.969 Daniel Schonfeld: but I think the other thing that was really great was, you know, I talked to Ben, I said. Let’s even throw out a forecast for next year. And that’s what I’m gonna tell them, I said, look, we’re gonna do X amount next year. We’re we’re we did X amount this year across a bunch of different providers
271 01:04:16.630 ⇒ 01:04:27.139 Uttam Kumaran: like. And and I think also for her, she’s probably hitting some sort of end of year quote. I think so. You know. I think you know what they say. Hungry dogs run faster.
272 01:04:27.300 ⇒ 01:04:31.560 Daniel Schonfeld: She I mean. She was hungry.
273 01:04:32.280 ⇒ 01:04:33.350 Daniel Schonfeld: Alright good!
274 01:04:35.260 ⇒ 01:04:36.370 Daniel Schonfeld: We’ll go ahead next.
275 01:04:36.930 ⇒ 01:04:43.199 Uttam Kumaran: I think those are the main things. So what I’m gonna do is focus on
276 01:04:43.930 ⇒ 01:04:50.390 Daniel Schonfeld: can you? Dan probably hasn’t seen. Could we go take a look at marketing? I just want.
277 01:04:50.430 ⇒ 01:04:55.049 Daniel Schonfeld: Let’s look at the campaigns and stuff just so we can see how we have it set here.
278 01:04:55.220 ⇒ 01:04:56.679 Daniel Schonfeld: This is gonna be.
279 01:04:57.640 ⇒ 01:05:03.889 Daniel Schonfeld: This is gonna come more and more and more into focus. Now that we’re getting other things dialed in, we need to control here.
280 01:05:04.350 ⇒ 01:05:14.550 Uttam Kumaran: Yeah. So basically, we’re able to look at every single platform. I don’t have a specific dashboard, actually, actually, actually, let’s go back to
281 01:05:14.650 ⇒ 01:05:21.440 Uttam Kumaran: The like vital signs. I can show you exactly on the paid marketing side. What we’re looking at
282 01:05:23.550 ⇒ 01:05:24.630 Uttam Kumaran: so
283 01:05:24.890 ⇒ 01:05:47.899 Uttam Kumaran: similarly kind of like we do a lot of marketing across a various number of sources. I am not looking at conversion to revenue at the moment I think me and Ben spoke a lot about attribution, and, like the nightmare of like dealing with that. And instead, we wanna just look at apples, apples. How much do we spend?
284 01:05:47.900 ⇒ 01:06:11.670 Uttam Kumaran: And we can look at impact on revenue? So basically, for any given, you know, add source. We have both the cost, the amount of clicks it drove, and at the bottom here. I have, based on each platform which Google direct. Mail Facebook, Amazon affiliate and SMS, we have the cost.
285 01:06:11.710 ⇒ 01:06:13.689 Uttam Kumaran: And
286 01:06:13.850 ⇒ 01:06:33.469 Uttam Kumaran: we can now begin to bring in things like impressions, clicks and do Cpm analysis. Of course, Facebook and Google, we have the highest. What would be helpful, I think, is again for me to add more like this is a red flag moment is things around total spend budgets as well as
287 01:06:33.620 ⇒ 01:06:41.490 Uttam Kumaran: cost per click. Those, I think it’s easy for us to say, Hey, if cost per click goes above $3
288 01:06:41.830 ⇒ 01:06:52.119 Uttam Kumaran: where we’re having a tough time or Cpm goes really high, we need to talk, or if the budgets go beyond, there may be in a campaign that’s still running.
289 01:06:52.720 ⇒ 01:06:56.810 Daniel Schonfeld: Why do I care what the cost per click is
290 01:06:56.880 ⇒ 01:06:59.770 Daniel Schonfeld: if I’m getting a return that’s acceptable.
291 01:07:01.400 ⇒ 01:07:11.129 Uttam Kumaran: so that the tough part is is we can’t do. The attribution to the bottom line is like the 1 million dollar problem.
292 01:07:11.180 ⇒ 01:07:13.480 Uttam Kumaran: Meaning
293 01:07:13.770 ⇒ 01:07:27.990 Uttam Kumaran: if you wanna look at every if you consider a a candidate from every single source the same, then you shouldn’t worry too much about the contribution of revenues. That being said, if we do have a saying that if we do have a sense of like hey Facebook customers
294 01:07:28.080 ⇒ 01:07:30.629 Uttam Kumaran: on average convert higher.
295 01:07:30.910 ⇒ 01:07:59.010 Uttam Kumaran: then we can kind of factor that in. But the problem is, is, these people not only come from one Ad. But then they make purchase at a different time doing that level of attribution. I would frankly would rather I can provide some other tooling, or to to to make that happen. And just look at the pie. II just wanna say one thing about attribution there
296 01:08:00.410 ⇒ 01:08:03.560 Daniel Schonfeld: is. Is there any way on here that I can see the total
297 01:08:03.690 ⇒ 01:08:08.590 Daniel Schonfeld: in just one line? Item, what our total Cac was for any day or week or month.
298 01:08:10.330 ⇒ 01:08:16.630 Uttam Kumaran: So even if we were to define Cap, you would have to say customers acquired on the day of spend.
299 01:08:16.930 ⇒ 01:08:18.249 Uttam Kumaran: Is that correct?
300 01:08:18.950 ⇒ 01:08:22.010 Daniel Schonfeld: Yeah. So that looks like it yesterday. And I say.
301 01:08:22.560 ⇒ 01:08:26.869 Daniel Schonfeld: how many? Co, what did we spend? Total? And how many customers came in?
302 01:08:27.080 ⇒ 01:08:38.539 Uttam Kumaran: Yeah, so I would like to this, I would, I would say, like in a given day, right. The marketing costs was roughly 3 grand. I would then I could add a customer here and add a daily Ca.
303 01:08:38.960 ⇒ 01:08:41.659 Daniel Schonfeld: how many customers came in purchased.
304 01:08:42.160 ⇒ 01:08:53.340 Daniel Schonfeld: and how much did we spend, and how much did we make? And so the thing with attribution is, yes, you’re never gonna get it down to a science of what came from where?
305 01:08:53.410 ⇒ 01:09:07.760 Daniel Schonfeld: But you can look at trends. So the one thing I would be able to look at is, say, what’s the total cost to acquire that customer over one day, 7 days, 30 days. whatever the attribution window you want to use for the total just for one number.
306 01:09:07.939 ⇒ 01:09:18.330 Daniel Schonfeld: and I can look at a running total of that, and then say. compared to 2 weeks ago last week this week last year. The total cack is down, and then
307 01:09:18.370 ⇒ 01:09:35.690 Daniel Schonfeld: we could do is yes, you can put in Facebook and say, here’s the total number that Facebook’s report, whatever you want to do, I don’t really care, actually. And say, here’s here’s what Facebook’s reporting as the revenue, the cost, the total customers from them.
308 01:09:35.870 ⇒ 01:09:39.509 Daniel Schonfeld: The one thing II can do is
309 01:09:39.520 ⇒ 01:09:46.079 Daniel Schonfeld: look at their reporting over time and say their reported Cac is down.
310 01:09:46.520 ⇒ 01:09:59.790 Daniel Schonfeld: Google’s is up, whatever it is. And then just look at the overall to see, to kind of triangulate and look for those signals of where things are headed within that isolated platform. I understand I’ll never be able to understand
311 01:10:00.030 ⇒ 01:10:21.460 Daniel Schonfeld: exactly where it came from. Or if Google just did the retargeting. But Facebook brought it in. I get all that. But at least I can see a trend line number one across the entire holistically overall on the business, and then I can isolate within its own platform what the Cac is for just specific channels, and then start to figure out
312 01:10:21.870 ⇒ 01:10:23.900 Daniel Schonfeld: which one of those
313 01:10:23.940 ⇒ 01:10:42.429 Daniel Schonfeld: cause. If all the if if my tag is, is is the same for a week straight, and all the platforms show me on a daily basis, it’s the same also for that week. and then the next week it goes up my kak. All the other platforms are the same except Google’s reporting a much lower one.
314 01:10:42.590 ⇒ 01:10:48.620 Daniel Schonfeld: I could then start to say, Okay, based on all of these trends and based on the total, Google seems to be having a problem.
315 01:10:49.190 ⇒ 01:10:50.110 Uttam Kumaran: Yeah.
316 01:10:50.240 ⇒ 01:11:13.679 Uttam Kumaran: that’s II would honestly prefer that as well. I think the the decisions to make is what’s the granularity you wanna look on if it’s like we have a 7 day attribution for spend. This is the this is the tough part is like I more variables, you add, the more complicated it gets, and the more. Oh, well, we’re writing up a campaign, or like we’re not saturated yet, like I don’t know. I’ve just. I’ve seen a lot of
317 01:11:13.680 ⇒ 01:11:23.050 Daniel Schonfeld: those conversations kind of fizzle out. So yeah, I don’t. Wanna. I don’t know the answer to that yet. And I would say, just keep it high level, almost to the level that I’m talking about.
318 01:11:23.110 ⇒ 01:11:24.270 Daniel Schonfeld: Okay, because
319 01:11:25.100 ⇒ 01:11:38.729 Daniel Schonfeld: all that’s gonna happen is when Ben or I go into weekly a weekly meeting with the team, it at least gives us an orientation to say, Okay, the total Cac for this week for forget going into the individual channels that people manage.
320 01:11:38.980 ⇒ 01:11:51.680 Daniel Schonfeld: Ben can say, Okay, what’s the Cac for this week? And it says whatever number is. And you could just look back real quickly by toggling some numbers, and he’ll say, Okay, the the average hasn’t gone down, so I don’t have. I don’t have anything like crazy in this meeting. I need to like.
321 01:11:51.930 ⇒ 01:11:58.830 Daniel Schonfeld: like, really figure out. But if it’s out of whack for one week and every other week for the last
322 01:11:58.870 ⇒ 01:12:01.959 Daniel Schonfeld: 6 months has been stagnant, has been the same.
323 01:12:02.030 ⇒ 01:12:20.309 Daniel Schonfeld: He then can go into the meeting or get a little more granular and say, let me just look at Facebook’s trending average for Cac over the last few months. Let me look at Google described it, and he at least can go into the meeting and say, whoever’s running the Ppc. And say, Hey, this is what I found. The Cac’s been normal for 6 months.
324 01:12:20.950 ⇒ 01:12:32.409 Daniel Schonfeld: Everything on Facebook looks to be normal, everything on email. The only thing that’s looking out of whack is Google. So can you go into the campaigns and see if anything’s changed, and at least he knows where to start the conversation.
325 01:12:32.480 ⇒ 01:12:43.229 Daniel Schonfeld: Our reporting system doesn’t have to do all that, and try to figure it out, and attribution all that. It just wants a conversation that he tells the Ppc. Guy, you need to do a deeper dive into campaigns and figure out if
326 01:12:43.320 ⇒ 01:12:46.589 Daniel Schonfeld: the data is telling me that it’s coming from Google. Can you look into that?
327 01:12:47.020 ⇒ 01:12:56.699 Daniel Schonfeld: Great? No, Tom, I kinda do that already, but it’s it’s harder because I have to look in all these. There’s too many places to look, but I don’t look at
328 01:12:56.710 ⇒ 01:13:02.510 Daniel Schonfeld: attribution. Anyone don’t trust it. So where I do is I look at overall, spend
329 01:13:02.590 ⇒ 01:13:19.000 Daniel Schonfeld: overall sales, did the do I feel? And this is a a important distinction that I’m going on feel is, do I feel that we sold an appropriate amount against how much we spent? So if we spent 1 million dollars, I’m sorry 100,000 did we make.
330 01:13:19.320 ⇒ 01:13:30.739 Daniel Schonfeld: you know a lot more? And then I said, Okay. who ate most of the spend? I’ll look at Google and say, Okay, Google spent a lot, and I’ll look at a couple of different boarding signs. And
331 01:13:30.820 ⇒ 01:13:34.680 Daniel Schonfeld: I’ll talk to the Google guy and say, you know what I think you’re you’re just spending.
332 01:13:35.520 ⇒ 01:13:47.610 Daniel Schonfeld: you know, aimlessly. Yeah. By the way, this report is great, and what the only missing here is revenue, that that that that channel is reporting, and what I see is at the top. Above. This
333 01:13:47.880 ⇒ 01:13:56.670 Daniel Schonfeld: is similar to that dashboard. The the initial Home Dashboard is at the top, just an aggregate, and say not of this data
334 01:13:56.800 ⇒ 01:14:23.370 Daniel Schonfeld: of the total, spend the true aggregate of total spend the true aggregate of total revenue, the true aggregate of actual customers, unique customers that bought. And just say, Here’s the Cac. For this time. And then if I looked at that right here, I would know everything I need to know going into a meeting, your meeting, if it makes sense. So imagine you you came in here. You’re about to have your weekly meeting with the team at the top.
335 01:14:23.530 ⇒ 01:14:29.839 Daniel Schonfeld: You can toggle it to. You could say, Let the last 7 days last 30 days, whatever it is.
336 01:14:30.130 ⇒ 01:14:34.560 Daniel Schonfeld: Ben, you go, and you say, the last 7 days since our last meeting or custom date
337 01:14:34.940 ⇒ 01:14:37.210 Daniel Schonfeld: at the top, it would say, total revenue.
338 01:14:37.730 ⇒ 01:14:40.939 Daniel Schonfeld: total marketing spent total, total.
339 01:14:41.220 ⇒ 01:14:47.539 Daniel Schonfeld: total, new, unique customers that bought this product, and then the Cac. So you have that.
340 01:14:47.730 ⇒ 01:14:59.469 Daniel Schonfeld: and you can do a comparison underneath that from the previous week. The same way Google does it where it says, compare to. So he can look at what he discussed with them last week. And all of a sudden, if the Cac. Is up by 50%.
341 01:14:59.600 ⇒ 01:15:10.690 Daniel Schonfeld: All he’s gotta do is look at the these rows you have here. This chart you have here below it. and it would have 2 more things here. It would have the revenue next the Cpm.
342 01:15:10.830 ⇒ 01:15:12.539 Daniel Schonfeld: yeah. And then that’s the cost.
343 01:15:13.010 ⇒ 01:15:19.019 Daniel Schonfeld: 3 things, the revenue, the total customers that that platform is saying what?
344 01:15:19.130 ⇒ 01:15:20.360 Daniel Schonfeld: And then the Cac.
345 01:15:20.510 ⇒ 01:15:26.380 Daniel Schonfeld: Right next to it, and he would just quickly eyeball it and say, Okay, for the same timeframe
346 01:15:26.570 ⇒ 01:15:36.169 Daniel Schonfeld: over the last 2 weeks doesn’t show a 40% increase in Cac. Google affiliates doesn’t direct. Mail doesn’t. Amazon doesn’t. But wow, Google does.
347 01:15:36.740 ⇒ 01:15:41.419 Daniel Schonfeld: And he can then direct his conversation, saying, Here’s what the data is telling me.
348 01:15:41.670 ⇒ 01:15:48.550 Daniel Schonfeld: Mike. I think it’s worth you looking into Google, because it’s the only platform saying that the cack went up.
349 01:15:48.600 ⇒ 01:15:51.250 Daniel Schonfeld: I know, for a fact. Our overall went up
350 01:15:51.480 ⇒ 01:16:08.499 Daniel Schonfeld: so that’s a good place for us to start looking into this, and he might come back and say, Oh, that the reason why it’s up is I run 4 new campaigns and you’re seeing Facebook get the benefit from it, whatever it is. But at least they’ll have an answer, and also the data at his fingertips to make that sound. Analysis.
351 01:16:09.900 ⇒ 01:16:38.129 Uttam Kumaran: Yeah. And and you know, I think similarly on on the things where we may not have revenue. Again. I we have spreadsheet revenue coming in for affiliates and direct mail. But yeah, II can totally ha! Add the customers in here and add the Cac. And then, yeah, we could see overall. And I think it’s great to just do like day by day. And I think it’ll normalize. And we can see. I mean even seeing this. I think it’s helpful to say, like, Oh, although cost per click on Google is really low. Maybe it’s like a different sort of customer. And
352 01:16:38.430 ⇒ 01:17:02.320 Daniel Schonfeld: and it also, by the way, a lot of what Kim does is manual into a spreadsheet. If you want, it’s easier. It might be just easier for her to plug it in like, forget the spreadsheets if we built her a little gooey or interface for her before the meeting, to just plug in the numbers or upload a spreadsheet, or whatever she does. So she doesn’t have to duplicate efforts, and the system doesn’t have to. It’s probably direct, and she’ll just plug it right in
353 01:17:02.800 ⇒ 01:17:25.039 Daniel Schonfeld: from the system. Human error, like where she actually has to manually look at it, and then put it in. No, and there and there was. There was some stuff in the spreadsheet that was that duplicated, and you know II have to go back to her to figure out. But like this is pretty much exactly like what we are talking about spreadsheet into a dashboard. I mean this spreadsheet.
354 01:17:25.100 ⇒ 01:17:28.860 Daniel Schonfeld: I mean, look at the bottom. You know the the tab. It’s ridiculous, you know.
355 01:17:29.020 ⇒ 01:17:53.760 Daniel Schonfeld: Yeah, bringing it in. But I’m bringing it in from here, and I would much rather provide her with like a Google form that she could just type all these things into stuff that she’s pulling from from the dashboard this. But, for example, I don’t need. Yeah, I don’t need this. I already have play video
356 01:17:53.760 ⇒ 01:17:59.630 Daniel Schonfeld: online. This I already I have through all the different paid channels.
357 01:17:59.850 ⇒ 01:18:03.200 Uttam Kumaran: It’s Frank. It’s frankly only this, that’s
358 01:18:03.420 ⇒ 01:18:15.790 Daniel Schonfeld: that I don’t, that I’m that I’m actually getting right now. Okay, so like for that you could just create until you figure out how to how to connect it. You could just build, or a little gui or or something. You just input those numbers.
359 01:18:16.010 ⇒ 01:18:22.319 Daniel Schonfeld: Yeah, do we show in our system? Currently, the campaigns like this, if she wanted to. Just
360 01:18:23.030 ⇒ 01:18:30.880 Uttam Kumaran: yeah. So if we were to, I’ll just give you. Yeah, I mean, if let’s say, we just want to go with
361 01:18:31.090 ⇒ 01:18:37.130 Uttam Kumaran: like Facebook campaigns. I guess this isn’t probably the
362 01:18:37.880 ⇒ 01:19:04.980 Daniel Schonfeld: yeah. Let me. Just let me just show exactly where we can get almost everything from Facebook, just like the actual nomenclature, and how you find stuff is to be a little more intuitive.
363 01:19:05.220 ⇒ 01:19:12.469 Uttam Kumaran: Yeah, I agree, it’s a it’s not so much like it has to be like so simple like, I can go drill down like I use Google. And
364 01:19:12.870 ⇒ 01:19:17.399 Daniel Schonfeld: I’m fine going deep into thanks. I just don’t know where to go for something I need.
365 01:19:18.880 ⇒ 01:19:23.549 Uttam Kumaran: So in here, what we can do is run like a day by day.
366 01:19:24.380 ⇒ 01:19:36.110 Daniel Schonfeld: campaign, just for just for Facebook, using this soon. Once he gives you the okay, all the meetings here.
367 01:19:36.200 ⇒ 01:19:38.979 Daniel Schonfeld: Yeah, so basically, yeah.
368 01:19:39.020 ⇒ 01:19:54.380 Uttam Kumaran: because everything’s coming in live. And so again, when the time it’s taking for I’m sure for them to map these things out, I think this is honestly what I like to read. Literally type up an email and
369 01:19:54.530 ⇒ 01:19:56.280 Daniel Schonfeld: just
370 01:19:56.350 ⇒ 01:20:02.150 Daniel Schonfeld: in the future, we can build in notes the ability to annotate. And things like that. Those are. Those are
371 01:20:02.170 ⇒ 01:20:18.419 Daniel Schonfeld: I’d rather be in a in a form, anyway. So if I tell you one day I’m like, Hey, we need. We need to now create a report where we can spit out what are the most successful email campaigns we’ve ever run. I’ll open right? It’ll already be database in our system, and she doesn’t have to go back through this spreadsheet and click on all of these apps
372 01:20:18.580 ⇒ 01:20:38.710 Daniel Schonfeld: and figure that out, also reviewing historical performance. So you know, think 2 years from now in this dashboard, and we can actually see Ben’s notes. Kim’s notes. Yeah, I’m even more interested in like it, being more intuitive. Like to tell her like, I feel like sometimes
373 01:20:38.840 ⇒ 01:20:47.639 Daniel Schonfeld: I have to ask if, like we’re doing you know that sometimes a little slower we’re doing SMS. Are we doing a National Pool day? Whatever the calendar is.
374 01:20:47.910 ⇒ 01:20:51.429 Daniel Schonfeld: it’d be better if something just pulled up and and
375 01:20:51.780 ⇒ 01:21:04.890 Daniel Schonfeld: showed her for this week in the last X amount of years. These are the campaigns you ran during this time period that were the best. So she can just push a button, and it’ll say you ran cyber. Monday, 1127, 23,
376 01:21:05.030 ⇒ 01:21:14.630 Daniel Schonfeld: over the last 5 years. This was the best performing one, or she could just analyze it and say, Okay, this is one we’re gonna use. Or we’re gonna create a new. We’re gonna ab test off that winner.
377 01:21:15.730 ⇒ 01:21:21.290 Daniel Schonfeld: That’s where that phase 3 that I was talking about with the marketing performance. That’s how you get smarter
378 01:21:21.500 ⇒ 01:21:25.890 Daniel Schonfeld: on the marketing performance, because she doesn’t have to go figure it out manually. You’ll just tell her
379 01:21:26.390 ⇒ 01:21:27.760 Daniel Schonfeld: the data’s already in there.
380 01:21:28.260 ⇒ 01:21:36.730 Uttam Kumaran: Yeah, like, we would look at it. Or we can set yeah, like a specific. Let’s just do even a specific add, you can go through and say, like, what are the top? What’s the top
381 01:21:37.080 ⇒ 01:21:53.629 Daniel Schonfeld: like? Clicked. Add, I don’t know some of these don’t have add names. Maybe it’s like, add set. But yeah, II totally get where you’re. I totally get like the systems we don’t want to build clavios reporting system out like we can run these reports, but at a very high level.
382 01:21:53.810 ⇒ 01:21:58.000 Daniel Schonfeld: I want this system to be able to guide us on what we
383 01:21:58.610 ⇒ 01:22:08.210 Daniel Schonfeld: unlike the obvious things that we’re missing or things that we should be doing like prompting we don’t need to get is to drill down as far as Clavia’s reporting.
384 01:22:08.670 ⇒ 01:22:12.610 Daniel Schonfeld: She just needs one place where it’s all compact.
385 01:22:12.870 ⇒ 01:22:19.799 Uttam Kumaran: So my next step here really is II just wanted a one for one on one given week.
386 01:22:19.940 ⇒ 01:22:24.259 Daniel Schonfeld: having that in light dash. And then I’m gonna say.
387 01:22:24.340 ⇒ 01:22:42.769 Uttam Kumaran: okay, for the stuff. That is a little bit more manual that I’m not getting. Now. There’s gonna be a little Google form that you can just fill out, and it’ll just have these fields. It’ll come through a database, and then I’ll get it in, and then let’s see if we can transition to leveraging one dashboard, which each of these, for
388 01:22:43.010 ⇒ 01:22:48.139 Uttam Kumaran: you know, the next reviewer for her use, and I’ll try to set up a weekly with her to look at this
389 01:22:48.550 ⇒ 01:23:02.779 Daniel Schonfeld: alright awesome and then the obviously make keep making sure that all the data is coming in. That’s the first priority is getting all of that expense data back in there or in here, whether it’s scraped, whether it’s, you know. Obviously the most direct route is the is the best
390 01:23:03.080 ⇒ 01:23:07.620 Daniel Schonfeld: yeah one. And then working on the skew. So
391 01:23:07.920 ⇒ 01:23:28.699 Daniel Schonfeld: we should. Then we’ll share with you like the stuff we looked at for Amazon and stuff we’re looking at now for shopify on a skew level to see ones are successful. But there’s multi. There’s other factors like we can’t look at a skew and say, Okay, the variable speed suck this year. Let’s let’s remove the one and a half force power. We really need to understand seasonal thing.
392 01:23:28.750 ⇒ 01:23:41.919 Daniel Schonfeld: If it’s a a logistics thing, we shipped it from the wrong place. So this. This you level reports are extremely important.
393 01:23:42.870 ⇒ 01:23:46.299 Daniel Schonfeld: and being able to go find the data to, to validate
394 01:23:46.340 ⇒ 01:23:48.270 Daniel Schonfeld: whether something’s working or not.
395 01:23:49.790 ⇒ 01:23:58.280 Daniel Schonfeld: And that’s not as easy as it sounds, but numbers will guide us, and we’ll figure out what else we need. Yeah.
396 01:23:59.620 ⇒ 01:24:07.829 Daniel Schonfeld: So I just need to be able to say, hey? Either by class, that you guys call it or skew. So I might say, Hey, in ground pumps. I need to do a full review on them for every week
397 01:24:08.870 ⇒ 01:24:14.500 Daniel Schonfeld: I go into report, and I say inground pumps, show me, and it’ll say on the left side.
398 01:24:15.310 ⇒ 01:24:17.170 Daniel Schonfeld: and then it’ll start to break out.
399 01:24:17.370 ⇒ 01:24:22.379 Daniel Schonfeld: How much revenue we did, how many customers bought it? What was the
400 01:24:23.360 ⇒ 01:24:34.279 Daniel Schonfeld: The shipping cost? We’re gonna have to play around with marketing costs in the long term. But we’re not going to tackle that at the moment. But just give me a general sense of how it’s doing.
401 01:24:34.370 ⇒ 01:24:49.190 Daniel Schonfeld: Okay? Yeah. And then again we’ll we’ll also have the region roll up and by state and by zone zone is huge, and I can just say zone 2 zone, 3. That’s really good. You’re gonna have those chuck because those zones are gonna change based on location.
402 01:24:49.260 ⇒ 01:24:55.050 Daniel Schonfeld: Yeah. So then, so it’s gonna be based on DC, but we’re doing a Zip code.
403 01:24:55.070 ⇒ 01:25:02.500 Uttam Kumaran: We calculate our own zone. So we we take the Zip code of the DC. And we know the shipping, and then we can do that.
404 01:25:02.600 ⇒ 01:25:10.659 Uttam Kumaran: So but one thing, yeah, I wanna get in contact with Chuck about you. Nice to set up there.
405 01:25:16.470 ⇒ 01:25:17.780 Daniel Schonfeld: Very good.
406 01:25:20.800 ⇒ 01:25:25.530 Uttam Kumaran: And then yeah. The Ltl data from Fedex.
407 01:25:25.690 ⇒ 01:25:40.260 Daniel Schonfeld: Well, one thing I would say about the Lco. As of now, you reach back 90 days, which is enough for me and Dan to take a look at. Heap the heat pump program and make some. you know. you know, decisions. But at least we know what’s what.
408 01:25:40.390 ⇒ 01:25:50.229 Daniel Schonfeld: I just want you to be able to scrape those like once a day, going, you know, starting yesterday, so that so th. And then at that point.
409 01:25:50.480 ⇒ 01:25:56.899 Daniel Schonfeld: it doesn’t matter whether or not they have 90 days. You you we always have what we need. So
410 01:25:57.080 ⇒ 01:26:03.420 Daniel Schonfeld: if we could just do that, that’s probably good enough. I don’t really want you to burn like a ton of time having them come back from
411 01:26:03.630 ⇒ 01:26:11.170 Daniel Schonfeld: 1998. If you can just scrape and get us information going forward, the heat pump
412 01:26:11.520 ⇒ 01:26:14.180 Daniel Schonfeld: our intention is to reduce cost.
413 01:26:15.170 ⇒ 01:26:23.039 Daniel Schonfeld: I’m shipping by being smarter by where we are shipping from. Obviously, there’s moments where we’re gonna ship, the app hank
414 01:26:23.130 ⇒ 01:26:29.160 Daniel Schonfeld: and as long as we just know what we’re paying that’s good enough. you know.
415 01:26:29.610 ⇒ 01:26:35.769 Daniel Schonfeld: Put in the Friday meeting for what? What I was saying before, just at least the starting in the New Year.
416 01:26:36.490 ⇒ 01:26:40.660 Daniel Schonfeld: January fourth or fifth. Whatever it is that we
417 01:26:40.790 ⇒ 01:26:48.269 Daniel Schonfeld: it it literally could be a 5 min meeting, but I just want to start the cadence of it of just looking at how this new
418 01:26:48.420 ⇒ 01:27:00.489 Daniel Schonfeld: shipping things going if you do come across data, just share with us. But of looking at it all of us, I think there’s 2 things with the shipping. I wanna verify that
419 01:27:00.780 ⇒ 01:27:11.570 Daniel Schonfeld: we are getting what we are expecting? And the second one is, is there any surprise that we have a talk with Kelly about an additional reduction of some kind
420 01:27:13.530 ⇒ 01:27:20.329 Daniel Schonfeld: which I think will happen. I think they’re still gonna got. They’re gonna gotcha one or 2 times I’m expecting.
421 01:27:20.570 ⇒ 01:27:25.230 Uttam Kumaran: Okay. So that’s yeah, we’ll those will be the 2 big topics on Friday.
422 01:27:25.670 ⇒ 01:27:30.369 Daniel Schonfeld: Alright cool. Oh, Tom, you’re the man. Thank you. This is this has been great so far.
423 01:27:30.650 ⇒ 01:27:38.209 Daniel Schonfeld: even more. This was unexpected for even you guys to figure out the shipping stuff. And I think, huge.
424 01:27:38.330 ⇒ 01:27:49.320 Uttam Kumaran: Yeah, I’m really glad. And you know, I think now we again we have. We have the little box, length, width, and height all the way rolled up, I think, where that’s really nice to see.
425 01:27:49.410 ⇒ 01:28:03.940 Daniel Schonfeld: you know, and I think again, we’re just filling out from there. It would be nice to get a lot of the data on the supply chain, and we’ll get to inventory turnover and stuff like that. But I’m happy that we have the weekly, and I think we’ll go from there.
426 01:28:04.100 ⇒ 01:28:05.899 Uttam Kumaran: Yeah, thank you so much.
427 01:28:06.040 ⇒ 01:28:09.000 Daniel Schonfeld: Alright, we’ll talk to you soon.
428 01:28:09.170 ⇒ 01:28:11.140 Uttam Kumaran: Okay, great makes sense.