Meeting Title: Review-Aug-10th-Meeting-Materials Date: 2024-08-02 Meeting participants: Uttam Kumaran, Daniel Schonfeld, Nicolas Sucari, Bencohen
WEBVTT
1 00:00:17.880 ⇒ 00:00:18.870 Nicolas Sucari: Hi, Ben!
2 00:00:19.760 ⇒ 00:00:21.580 bencohen: What’s up, man? How are you?
3 00:00:22.930 ⇒ 00:00:25.019 Nicolas Sucari: All good here. How are you?
4 00:00:25.220 ⇒ 00:00:26.120 Uttam Kumaran: Hey! Ben!
5 00:00:26.290 ⇒ 00:00:28.889 bencohen: Hey? All good, no complaints.
6 00:00:30.420 ⇒ 00:00:31.739 bencohen: no complaints.
7 00:00:32.780 ⇒ 00:00:34.310 bencohen: The world is ending.
8 00:00:35.310 ⇒ 00:00:40.489 Uttam Kumaran: Yeah, yeah, I guess in a couple of different ways.
9 00:00:44.230 ⇒ 00:00:45.820 bencohen: The world is ending.
10 00:00:47.310 ⇒ 00:00:50.410 bencohen: If you read the new, if you read the news. Yes.
11 00:00:50.410 ⇒ 00:00:52.799 Uttam Kumaran: Yeah, I just want to fast forward like.
12 00:00:53.170 ⇒ 00:00:54.977 Uttam Kumaran: 7 months, maybe.
13 00:00:55.580 ⇒ 00:00:56.670 bencohen: Yeah, it would be nice.
14 00:00:56.670 ⇒ 00:01:04.620 Uttam Kumaran: I I tend to read like I don’t know. I I’m always used to reading like the journal in the morning, like I’ve done for years.
15 00:01:05.190 ⇒ 00:01:08.443 Uttam Kumaran: and usually the journal is not as like,
16 00:01:08.990 ⇒ 00:01:23.829 Uttam Kumaran: everything’s on fire like the New York Times, and Washington Post will be like today is the day we all die like Get ready. Internal is like it tends to be. It’s like these days. It’s something.
17 00:01:24.150 ⇒ 00:01:24.820 Uttam Kumaran: Well.
18 00:01:24.820 ⇒ 00:01:27.870 bencohen: Reports, generally speaking, facts.
19 00:01:28.490 ⇒ 00:01:29.230 Uttam Kumaran: Yeah.
20 00:01:29.230 ⇒ 00:01:31.310 bencohen: It’s not taking a side. It doesn’t take.
21 00:01:31.310 ⇒ 00:01:38.159 Uttam Kumaran: It’s Tip. It’s typically, yeah. And I like it because I especially, most of it talks about money, and it’s just facts. And then
22 00:01:38.180 ⇒ 00:01:47.090 Uttam Kumaran: they’ll give some commentary. But then you can also basically be like, Oh, I saw the numbers, too. And I but yeah, I feel like they’re also
23 00:01:47.160 ⇒ 00:01:50.595 Uttam Kumaran: a little bit like I mean it is. Things are kind of nuts.
24 00:01:50.860 ⇒ 00:01:54.460 bencohen: But Twitter Twitter is the only place I get information really.
25 00:01:54.460 ⇒ 00:01:59.610 Uttam Kumaran: Me, too, I mean, for work. Actually, Twitter has changed like my
26 00:01:59.890 ⇒ 00:02:01.589 Uttam Kumaran: my whole career, like
27 00:02:01.620 ⇒ 00:02:04.320 Uttam Kumaran: the reason we find a lot of these tools. The reason.
28 00:02:04.320 ⇒ 00:02:05.220 bencohen: Yeah.
29 00:02:05.220 ⇒ 00:02:10.219 Uttam Kumaran: You moved pretty fast and like, I’ve learned so much data stuff
30 00:02:11.034 ⇒ 00:02:20.590 Uttam Kumaran: on Twitter that the problem with Twitter is that you get everything, you know. It’s it’s good and bad, but.
31 00:02:20.590 ⇒ 00:02:31.739 bencohen: My new thing. Now I’m starting to. I’m gonna buy a drone. But like I don’t want to get like an ordinary one, I want to get the one where you have the headset so like, control it by like moving your face.
32 00:02:31.740 ⇒ 00:02:32.400 Uttam Kumaran: Yeah.
33 00:02:32.710 ⇒ 00:02:35.659 bencohen: I’m gonna do most of that diligence with with Twitter.
34 00:02:35.720 ⇒ 00:02:38.959 bencohen: There’s really no other way. People that actually use this stuff.
35 00:02:38.960 ⇒ 00:02:41.564 Uttam Kumaran: It’s either that or or reddit right like
36 00:02:42.810 ⇒ 00:02:45.040 Uttam Kumaran: And I think, Reddit 2.
37 00:02:45.650 ⇒ 00:02:57.937 Uttam Kumaran: Sometimes it’s really negative. I feel like Twitter. You get both sides, and sometimes you’ll be like some guys like, Hey, CEO! Of the biggest drone company here, and he’ll have like 500 followers on Twitter.
38 00:02:58.270 ⇒ 00:03:07.012 bencohen: Yeah, no, yeah. But that’s the thing is is like the alternative was like going to Buzzfeed where they would say, like, Here’s your top 10 drones. And it’s a listicle that’s paid for.
39 00:03:07.970 ⇒ 00:03:10.259 bencohen: Only Internet. People know this stuff.
40 00:03:10.420 ⇒ 00:03:11.030 Uttam Kumaran: Yeah, like.
41 00:03:11.030 ⇒ 00:03:13.580 bencohen: I’ve never but like my mom.
42 00:03:14.180 ⇒ 00:03:17.019 bencohen: she thinks like Cnn. And these things is.
43 00:03:17.640 ⇒ 00:03:28.090 Uttam Kumaran: Oh, I know well, they have no idea how, but it’s so many layers to explain to them like well, they have the viewers, and then they just get money, and then they put them as number one.
44 00:03:28.430 ⇒ 00:03:29.070 bencohen: My mom was.
45 00:03:29.070 ⇒ 00:03:29.690 Uttam Kumaran: Over letters.
46 00:03:29.690 ⇒ 00:03:34.509 bencohen: Right. And she was like, Ben, you’re brainwashed. And I was like what
47 00:03:36.840 ⇒ 00:03:41.320 bencohen: I was like, yeah, I was like you, that’s rich coming from you. You watch Cnn.
48 00:03:41.320 ⇒ 00:03:42.866 Uttam Kumaran: Yeah, yeah.
49 00:03:43.890 ⇒ 00:03:47.450 bencohen: Alright. I think it’s just us. I don’t think Dan’s coming on as my guest.
50 00:03:47.450 ⇒ 00:03:49.170 Uttam Kumaran: Okay. Alright. Well, let’s
51 00:03:49.710 ⇒ 00:03:52.300 Uttam Kumaran: or I’m happy to send a little like loom.
52 00:03:52.300 ⇒ 00:03:54.939 bencohen: Yeah, definitely send a wrap up after.
53 00:03:54.940 ⇒ 00:04:12.390 Uttam Kumaran: Cool, big things. I just wanted to go through this. I’m happy to go through real and talk through whatever else we want. But I wanted to show you this. It’s in a good place for revisions. There’s some obvious things that I want to change but in a pretty good place. So we added 2 pages here. And we’ll
54 00:04:12.889 ⇒ 00:04:18.390 Uttam Kumaran: maybe, Nico, if you want to take some notes, we’ll also make this like little homepage. Look a little better. The 1st one.
55 00:04:18.390 ⇒ 00:04:19.019 Nicolas Sucari: Yep.
56 00:04:19.310 ⇒ 00:04:24.880 Uttam Kumaran: I’ll show is a little bit about how we are going to display the center of gravity analysis.
57 00:04:26.520 ⇒ 00:04:30.750 Uttam Kumaran: part of the the things you’re going to see here are, viz, and you’re gonna see text.
58 00:04:32.180 ⇒ 00:04:45.120 Uttam Kumaran: I I wanted to leave heavy on biz, because you’re gonna be like, just like pulling this up and like you’re gonna talk through it. Some people are gonna look at the base. Some people are gonna read even the text I’ve tried to make just like read.
59 00:04:45.596 ⇒ 00:04:47.500 bencohen: It’s, it’s perfect. Yeah.
60 00:04:47.500 ⇒ 00:04:47.825 Uttam Kumaran: Yeah.
61 00:04:48.564 ⇒ 00:04:52.710 bencohen: Like a like a medium medium article. You’re trying to.
62 00:04:52.710 ⇒ 00:05:12.400 Uttam Kumaran: What we’re going for is like an internal medium post about like, here’s what we wanted to go for. Here’s what we tried. Here’s where we ended up. Here’s next steps, which is, which is it? And so basically, it’s like we tried to reduce costs. We’re trying to do center of gravity to open a new warehouse. Here’s our existing warehouses.
63 00:05:12.440 ⇒ 00:05:15.660 Uttam Kumaran: Here are where they are on a map, this map
64 00:05:15.690 ⇒ 00:05:25.246 Uttam Kumaran: as grids. Maybe I should just remove the grids. Basically, we were like cool. We initially were looking at all orders and skews for the past
65 00:05:26.020 ⇒ 00:05:43.670 Uttam Kumaran: 2 years. And and then instead, we decided on just looking at the the stuff we were shipping with Eunice and add a chart here to just show a little bit of that and then, basically, we were able to show that, like cool we were looking at from New York to Texas and from Florida to Texas.
66 00:05:44.100 ⇒ 00:05:54.660 Uttam Kumaran: basically looking at what the cost per pound. The average shipping cost was to ship there and notice that there is like a good amount of cost savings.
67 00:05:55.073 ⇒ 00:06:00.576 Uttam Kumaran: This is just text about like, kind of the the States. We looked at.
68 00:06:01.510 ⇒ 00:06:10.330 Uttam Kumaran: like what the tech what the top cities in Texas are. What are the 3 non brush skews that we’re looking at. Basically, we pick
69 00:06:10.340 ⇒ 00:06:29.720 Uttam Kumaran: around. Memphis is where our like optimal warehouse location is. We looked at the Unis locations. Dallas was the closest one. And basically we’re like cool, we should be able to save about like 25% to some of these key markets and then basically have a last chart with, like.
70 00:06:30.040 ⇒ 00:06:32.606 Uttam Kumaran: where these 4 are now.
71 00:06:33.760 ⇒ 00:06:36.319 Uttam Kumaran: a cup, I guess, like, what’s your gut instinct?
72 00:06:36.510 ⇒ 00:06:42.060 bencohen: 10 out of 10. It’s good because I don’t actually need you. I can just read this, and I know what.
73 00:06:42.340 ⇒ 00:06:42.810 Uttam Kumaran: Yeah.
74 00:06:42.810 ⇒ 00:06:52.320 bencohen: The intended purposes, and and also if I wanted to pass it to somebody, they could, too. This is perfect, because it explains what we have how you arrived at it. It’s perfect.
75 00:06:52.320 ⇒ 00:06:55.910 Uttam Kumaran: I’m I’ll just. I’m gonna bold a couple of things. So that in case you’re like.
76 00:06:56.080 ⇒ 00:07:07.275 Uttam Kumaran: I mean, I just pretty much tried to do this live, which is like, walk through it. So I’ll just hold a couple of things, so that in case you’re just like looking for something, you’ll see one or 2 lines that are clear.
77 00:07:07.720 ⇒ 00:07:10.710 Uttam Kumaran: and but otherwise I think this is like decent
78 00:07:11.395 ⇒ 00:07:22.047 Uttam Kumaran: the other thing we were looking at is the customer segmentation, which is a pro versus consumer. Very similarly, we and you’re not gonna see any of these. This is just
79 00:07:22.380 ⇒ 00:07:23.769 bencohen: I got it. Yeah.
80 00:07:24.164 ⇒ 00:07:31.269 Uttam Kumaran: You’re basically, we’re looking at trying to understand our customer base. We had, like 3 key data points.
81 00:07:31.330 ⇒ 00:07:35.159 Uttam Kumaran: the self identification email address, the purchase behavior.
82 00:07:35.630 ⇒ 00:07:37.939 Uttam Kumaran: I need to make this pump orders.
83 00:07:38.791 ⇒ 00:07:41.598 Uttam Kumaran: And then basically, we found that like
84 00:07:42.963 ⇒ 00:07:45.380 Uttam Kumaran: we’re basically looking at the
85 00:07:46.770 ⇒ 00:07:51.649 Uttam Kumaran: the percent of total sales between. I think this is
86 00:07:52.150 ⇒ 00:08:01.219 Uttam Kumaran: this percent of total customers. But then the percent of total sales. So you can see, although pros are a small amount. They’re a high amount of sales.
87 00:08:01.220 ⇒ 00:08:01.790 bencohen: Yeah.
88 00:08:01.960 ⇒ 00:08:17.270 Uttam Kumaran: Maybe this is confusing. Okay, let me. I’ll just keep going. But you can look at the average transaction value is really high. 3,000 versus 800. The purchase frequency and the items for order. So basically in a row, we’re trying to show that like.
89 00:08:17.660 ⇒ 00:08:21.639 Uttam Kumaran: bang, bang, bang! Like these guys are, but are valuable. Here’s like
90 00:08:21.710 ⇒ 00:08:31.569 Uttam Kumaran: 3 or 4 different ways. We proved it. And here’s what we’re thinking about doing. One is like targeted marketing, basically trying to market to them a little bit differently.
91 00:08:32.063 ⇒ 00:08:46.806 Uttam Kumaran: Other things we’re we’re thinking about. We just kind of like I was using kind of chat, gpt to like, think about other things to say. Basically, one of them said inventory optimization. The other thing that we were looking at was geographically where to target folks.
92 00:08:47.470 ⇒ 00:09:12.369 Uttam Kumaran: one thing, I may add, is a little bit of what we talked with Cody about, which is basically just like thinking about if they need different materials or a sales strategy. That’s kind of around targeted marketing. But I’ll put a line about that there. Yeah, actually, that is here under, yeah, sorry out of that. And then product development and sourcing. Basically looking at what people are ordering across these different segments and potentially
93 00:09:12.370 ⇒ 00:09:20.169 Uttam Kumaran: like doing something with with that information about what they’re ordering. And the last thing is on customer experience, which is like loyalty programs. Yeah, price.
94 00:09:20.290 ⇒ 00:09:24.899 Uttam Kumaran: things like that. So this is mostly bar charts, which.
95 00:09:25.790 ⇒ 00:09:30.609 Uttam Kumaran: like I’m I don’t know. I may just turn these into like simple numbers.
96 00:09:30.670 ⇒ 00:09:32.220 Uttam Kumaran: so you should see them.
97 00:09:33.040 ⇒ 00:09:33.500 Uttam Kumaran: But.
98 00:09:33.500 ⇒ 00:09:36.600 bencohen: I understand I am not struggling with these.
99 00:09:36.600 ⇒ 00:09:40.380 Uttam Kumaran: I guess again I just wanted to get like gut instinct on.
100 00:09:40.890 ⇒ 00:09:44.840 bencohen: No, you nailed it, you nailed it with the stuff. This is. It’s
101 00:09:45.120 ⇒ 00:09:50.160 bencohen: scholarly reporting. It’s very good, and but it has odd. It has visual. It has text.
102 00:09:51.060 ⇒ 00:09:53.549 bencohen: It’s well chunked. It’s it’s very good.
103 00:09:53.780 ⇒ 00:10:05.759 Uttam Kumaran: Yeah. And then, of course, we have, like, we have our other pages, which is about like our refund program. Looking at like refunds by platform days to return.
104 00:10:05.990 ⇒ 00:10:07.609 Uttam Kumaran: So like again, these are.
105 00:10:08.470 ⇒ 00:10:12.301 Uttam Kumaran: you could just pull up any of these charts. And there’s there’s a story around it.
106 00:10:12.850 ⇒ 00:10:26.441 Uttam Kumaran: Nico, let’s know there’s some. There’s a bar chart issue on this one. Yeah. And then the warranty side to this one. Actually, I would say, it’s probably the best looking one where we looking, we’re looking at
107 00:10:26.870 ⇒ 00:10:34.560 Uttam Kumaran: warranty claims and like days to warranty claim. Which skews have the highest warranty claims.
108 00:10:35.257 ⇒ 00:10:38.250 Uttam Kumaran: Again, probably something nice to flash up.
109 00:10:38.590 ⇒ 00:10:39.350 bencohen: Yeah.
110 00:10:39.540 ⇒ 00:10:46.370 bencohen: no, I think you did a great job. I would say, this is, gonna be. This is gonna go over, really? Well, I agree the 1st page.
111 00:10:46.740 ⇒ 00:11:00.069 bencohen: once you take this off local, obviously. But make it look a little more like a site. Yeah, you get it. And then, yeah, like this this stuff, just hide it, or throw to the bottom the the pre loading things. But.
112 00:11:00.070 ⇒ 00:11:00.700 Uttam Kumaran: Yeah, yeah.
113 00:11:00.700 ⇒ 00:11:12.030 bencohen: It’s very good. I mean, I can. You know there’s not much. There’s 4 or 5 things. It’s very clear they’re titled clearly, so it’s there’s no drama here, and they all are very business focused
114 00:11:12.410 ⇒ 00:11:16.740 bencohen: actionable. They’re very strong. It’s all the stuff we talk about. Just
115 00:11:17.120 ⇒ 00:11:19.989 bencohen: it applies numbers with what we talk about.
116 00:11:19.990 ⇒ 00:11:41.209 Uttam Kumaran: Yeah, basically, it’s this is like, you know, and even how I you know me and Nico talk about this is like everything we do starts is just like talking about hypotheses. We find the data, or we build the data models we work towards like an analysis, like in a however scrappy way. But this is the end state. This is like the document that comes out of that whole thing right and like.
117 00:11:41.530 ⇒ 00:11:47.089 Uttam Kumaran: otherwise, it lives in Snowflake, or it lives like somewhere we can see it. This is like the.
118 00:11:47.510 ⇒ 00:11:48.610 bencohen: No, this is this is.
119 00:11:49.360 ⇒ 00:11:50.110 Uttam Kumaran: So.
120 00:11:50.110 ⇒ 00:11:53.949 bencohen: Is key, because every time we take an initiative, whatever it might be.
121 00:11:55.950 ⇒ 00:12:03.120 bencohen: we need to like, memorialize it somehow. Like, for example, did it make sense to open up Jacksonville? Eunice?
122 00:12:03.210 ⇒ 00:12:14.160 bencohen: Yeah, there needs to be a post mortem after we activate that center and let it go for whatever 100 days to say, did this work, and you need to prove it like this. And then then
123 00:12:14.630 ⇒ 00:12:18.109 bencohen: you build on that. So it’s the perfect thing, because we need to send this
124 00:12:18.190 ⇒ 00:12:23.959 bencohen: internally. So like when we basically give our staff a colonoscopy. Yeah.
125 00:12:24.450 ⇒ 00:12:31.459 bencohen: here’s why we gave you the colonoscopy. It’s all with the intent of winning some way. So it’s perfect.
126 00:12:31.460 ⇒ 00:12:44.970 Uttam Kumaran: You know, typically even in other situations, you’re like, typically, we’re like, Oh, it’s in an Excel model somewhere, right? And the the like. The Amazon woman you mentioned came in and did some stuff in in excel like, it’s just the medium actually matters
127 00:12:45.000 ⇒ 00:12:48.860 Uttam Kumaran: and think a lot about that, because a lot of our work
128 00:12:48.930 ⇒ 00:13:06.820 Uttam Kumaran: like we can get to 90% of it. But it’s still code somewhere, like it doesn’t matter until. But even though this is like the last 10%. This is the real like, this is an asset that can get shared, that can be used, that can be. If, hey, how do we do that warranty thing like we’re getting some questions about warranty
129 00:13:07.020 ⇒ 00:13:11.300 Uttam Kumaran: like in English, it’s here. And then also, we have, like Spanish, which is
130 00:13:11.570 ⇒ 00:13:12.400 Uttam Kumaran: the background.
131 00:13:12.400 ⇒ 00:13:19.699 bencohen: No, no, it’s it’s a complete no brainer. I’m dyslexic. So like there’s there’s certain things that like I just can’t quite grasp.
132 00:13:19.750 ⇒ 00:13:21.179 bencohen: And this.
133 00:13:22.080 ⇒ 00:13:36.237 bencohen: This is like I like, I actually, when I want to learn something, I usually try to find it on Twitter, and then a medium article by the expert is usually my path, because when it’s done like this, I can focus for 30 min, and I understand it, 100% when it’s done
134 00:13:36.560 ⇒ 00:13:45.529 bencohen: when it’s just looking at a dashboard or spreadsheet. I’m like, all right, or like a call like guys. I don’t see it, you know, there’s not enough so for me. It’s perfect.
135 00:13:45.530 ⇒ 00:13:54.439 Uttam Kumaran: And this requires like the whole understanding. Right? It’s like, where do we start again? What do we find? And of course it’s like, where do we go? So okay, cool. I think
136 00:13:54.540 ⇒ 00:13:57.060 Uttam Kumaran: we, Nico, we just have a couple of changes to make.
137 00:13:57.060 ⇒ 00:14:01.119 bencohen: Yeah, yeah, you’re almost there. I think you know what to do. Just yeah.
138 00:14:01.130 ⇒ 00:14:07.330 bencohen: Wrap it up. Bring it from local onto some site. And then I can. Do you want me to grab a domain that you you want to throw it onto.
139 00:14:07.730 ⇒ 00:14:08.930 Nicolas Sucari: Yeah, I was.
140 00:14:09.340 ⇒ 00:14:13.919 Uttam Kumaran: We have it under this bullparts to go evidence, netlify site.
141 00:14:14.510 ⇒ 00:14:15.319 bencohen: That’s fine!
142 00:14:15.320 ⇒ 00:14:24.940 Uttam Kumaran: If that’s okay, there’s just there’s a simple like password login that I’ll just give you the password to. It’s a 1 password. It’s literally just put in. It’s like a just a password. So.
143 00:14:24.940 ⇒ 00:14:25.620 bencohen: Perfect.
144 00:14:26.160 ⇒ 00:14:30.759 Uttam Kumaran: We can move this to an internal site like it’s, but this is pretty easy.
145 00:14:30.950 ⇒ 00:14:33.299 bencohen: This is fine. There’s no no issue with this.
146 00:14:33.470 ⇒ 00:14:34.270 bencohen: Yeah, okay.
147 00:14:34.440 ⇒ 00:14:36.775 bencohen: alright guys, great stuff.
148 00:14:37.740 ⇒ 00:14:39.519 bencohen: I appreciate it. Yeah.
149 00:14:39.520 ⇒ 00:14:40.429 Daniel Schonfeld: Guys think.
150 00:14:40.430 ⇒ 00:14:43.210 Uttam Kumaran: I think the only other stuff is the unit stuff.
151 00:14:43.475 ⇒ 00:14:44.270 bencohen: There he is!
152 00:14:44.730 ⇒ 00:14:47.869 bencohen: You missed the whole. You missed. You missed the whole show.
153 00:14:47.870 ⇒ 00:14:50.030 Daniel Schonfeld: Oh, God! Did you record it?
154 00:14:50.473 ⇒ 00:14:51.590 bencohen: Yeah, actually. And it’s very.
155 00:14:51.590 ⇒ 00:14:56.229 Uttam Kumaran: Yeah, I I think we record it. Yeah, I guess I was. Gonna I mean, I could walk through.
156 00:14:56.360 ⇒ 00:14:59.289 Daniel Schonfeld: I’ll get some popcorn and and watch it. Yeah.
157 00:14:59.290 ⇒ 00:15:00.060 Uttam Kumaran: Okay.
158 00:15:00.160 ⇒ 00:15:00.710 Uttam Kumaran: there’s a.
159 00:15:00.710 ⇒ 00:15:01.530 Daniel Schonfeld: Sorry I was on a.
160 00:15:01.944 ⇒ 00:15:02.359 bencohen: Utah.
161 00:15:02.360 ⇒ 00:15:04.090 Daniel Schonfeld: Pulled into a last minute call. Sorry.
162 00:15:04.090 ⇒ 00:15:08.970 bencohen: Give Dan a 5 min, because this is this is just great stuff.
163 00:15:09.550 ⇒ 00:15:10.669 Uttam Kumaran: I’m asking.
164 00:15:12.690 ⇒ 00:15:40.070 Uttam Kumaran: okay, 5 min. Basically, we. I think you guys have seen this before. We’re basically, this is like the long tail of where all of our analysis and modeling kind of ends up as like a document about what we did. 2 things we’ve added. One is on center of gravity center. Gravity is basically, when we were looking at what is the next warehouse to open at the moment? Before this analysis we had 3 warehouses. You can see it. You can see in this table, we’re looking at
165 00:15:40.070 ⇒ 00:15:51.680 Uttam Kumaran: order quantities, product weights where our current warehouses are, where the demand is. Over the last 2 years. Basically, we also, we then instead looked at just exactly the skews we’re shipping with Eunice.
166 00:15:52.132 ⇒ 00:16:14.139 Uttam Kumaran: We basically found that there is a big cost savings shipping to Texas. And so Texas in this Midwest area is where there’s a really good opportunity to put another warehouse. We use this method called K means. Basically, it’s another statistical approach to this thing called an assignment problem. Basically, assignment problem is like.
167 00:16:14.476 ⇒ 00:16:32.629 Uttam Kumaran: let’s say you have 4 drivers. And they’re all kind of driving around to deliver food. Who who needs to deliver what to where? That’s the this, this like kind of like math problem. We basically found that in Texas there’s a couple of cities that would really benefit from optimized
168 00:16:32.630 ⇒ 00:16:44.879 Uttam Kumaran: shipping. In particular. The shipping. These items in particular is these black and Decker pumps, the non brush skews and then we found that in our analysis, basically, Memphis
169 00:16:44.900 ⇒ 00:16:47.930 Uttam Kumaran: is where there’s an ideal spot for
170 00:16:48.140 ⇒ 00:16:49.200 Uttam Kumaran: our 4th
171 00:16:49.250 ⇒ 00:17:01.220 Uttam Kumaran: and kind of final warehouse at this time and Eunice mentioned that there’s 1 in Dallas which is in the vicinity, and so we went ahead, and we’re recommending us to put
172 00:17:01.280 ⇒ 00:17:05.379 Uttam Kumaran: move, move our 4th warehouse into Dallas. And then here’s kind of the
173 00:17:05.410 ⇒ 00:17:07.809 Uttam Kumaran: the map view of all of our locations.
174 00:17:08.050 ⇒ 00:17:09.079 bencohen: Go ahead and.
175 00:17:09.089 ⇒ 00:17:09.709 Uttam Kumaran: Yeah.
176 00:17:10.811 ⇒ 00:17:19.480 Daniel Schonfeld: Just a question. Did you pre before doing the analysis? Did you overlay unis fulfillment centers, or you did this independent of knowing where their facilities were.
177 00:17:19.480 ⇒ 00:17:24.589 Uttam Kumaran: We did it independent. And then we then asked, Where are all your facilities? And then we
178 00:17:24.599 ⇒ 00:17:26.410 Uttam Kumaran: we’re constrained by
179 00:17:26.619 ⇒ 00:17:29.182 Uttam Kumaran: you know where they have options.
180 00:17:31.562 ⇒ 00:17:34.130 Daniel Schonfeld: Quick question. I’m shocked.
181 00:17:34.591 ⇒ 00:17:52.409 Daniel Schonfeld: I I those are probably the top 3, those variable speeds that that move the needle. Wh, what are the what are the variables input into that analysis to determine which skews would come out on top, like I like Ben. And I know that. Say, for example, booster pumps predominantly are going to be sold in
182 00:17:52.520 ⇒ 00:17:57.719 Daniel Schonfeld: Texas area. I’m curious why that didn’t show up there, is it? Because you just said, pull out. Top 3.
183 00:17:58.420 ⇒ 00:18:05.300 Uttam Kumaran: Yeah, I assume, I think it’s just because of our skew limit. But I can actually get these into like a table with.
184 00:18:05.350 ⇒ 00:18:07.609 Uttam Kumaran: like, maybe the top 10 and
185 00:18:07.650 ⇒ 00:18:11.139 Uttam Kumaran: the individual items, the the individual like sales.
186 00:18:11.860 ⇒ 00:18:14.380 Uttam Kumaran: basically showing what the benefit would be.
187 00:18:14.380 ⇒ 00:18:19.879 Daniel Schonfeld: Okay? And what? What was the input for data? If you can just reiterate again, was it?
188 00:18:19.910 ⇒ 00:18:24.080 Daniel Schonfeld: Yeah, what’s the timeframe? And how many skews were were the inputs.
189 00:18:24.080 ⇒ 00:18:42.920 Uttam Kumaran: Yeah, we looked at we looked at the last 24 months. And we looked at all data, like all of our shipments, basically. And again, the game is like where to ship something from. We’re not looking at like exactly where the inventory is. We’re just assuming that inventory can be made available. We’re actually just looking at
190 00:18:44.210 ⇒ 00:19:12.189 Uttam Kumaran: for example, if something ships to Texas from Florida versus New York versus California, what are the prices? Right? And we, we’ve been shipping from both Florida and New York recently. So we can do that comparison. And basically, we’re able to find that like, here’s what the cost decrease. We just saw from here. And then, ideally, if we’re going to see a linear cost decrease, where should our next warehouse be? And again. We have pockets of concentration, right? So it’s not like
191 00:19:12.270 ⇒ 00:19:37.119 Uttam Kumaran: you. Just put things in the corners and then you end up with everything covered. We’re not have much going on in some areas. So really, here is where the the kind of I was surprised. Initially, my gut reaction is like, Oh, why are we putting one so close to Florida. But actually, it’s this is where, like we have a ton of additional sales. And this is where, if we were to put one, it would really move the needle. Because we basically have this area covered.
192 00:19:37.120 ⇒ 00:19:43.629 Uttam Kumaran: And this area covered. And another way of thinking about this is, we’re optimizing for the minimum zone to any customer.
193 00:19:43.630 ⇒ 00:19:45.300 Uttam Kumaran: That’s the big thing. The big thing.
194 00:19:45.300 ⇒ 00:19:51.750 bencohen: The big thing I said was, I don’t wanna like like I like zone. We should never travel more than 3 zones.
195 00:19:52.400 ⇒ 00:19:54.959 bencohen: because that the price gets crazy. That was.
196 00:19:55.870 ⇒ 00:19:57.290 Daniel Schonfeld: Yes, sorry, Ben, go ahead.
197 00:19:58.560 ⇒ 00:19:59.589 Daniel Schonfeld: Are you done.
198 00:19:59.870 ⇒ 00:20:12.879 bencohen: And and you know, so how do you make it? So that there’s never really there’s some exceptions, because there’s random outliers like a Washington State. But there’s not a lot of volume. But how do you make it so that you never really have a zone for zone travel
199 00:20:13.160 ⇒ 00:20:14.369 bencohen: for something that.
200 00:20:15.210 ⇒ 00:20:17.160 bencohen: you know, is a good candidate.
201 00:20:17.210 ⇒ 00:20:18.250 bencohen: and we didn’t live.
202 00:20:18.250 ⇒ 00:20:25.670 Daniel Schonfeld: Question. Sorry to interrupt you, Tom. Because I’m I’m going to Vegas. The whole point of this is, we may or may not
203 00:20:26.070 ⇒ 00:20:38.119 Daniel Schonfeld: partner with a company called Uag, and this is all confidential information. Which also may do an investment into the company regardless there’s a good opportunity for a partnership with them. And there’s about.
204 00:20:38.680 ⇒ 00:20:42.310 Daniel Schonfeld: I think today it’s like 350 retailers and builders.
205 00:20:44.530 ⇒ 00:20:51.349 Daniel Schonfeld: my question is, if I get their location address their addresses.
206 00:20:51.440 ⇒ 00:21:05.940 Daniel Schonfeld: The big thing for them is they’re going to be buying domestic. These are not going to be import orders where they’re going to send us an order in September, and we ship them a container. We’re going to need to put stock in 3 pls. Or buy a warehouse, etc.
207 00:21:06.130 ⇒ 00:21:11.770 Uttam Kumaran: Well, could you clarify Ua Uag? They’re they’re local retailers or their local.
208 00:21:11.770 ⇒ 00:21:13.579 bencohen: They’re a buying group.
209 00:21:13.750 ⇒ 00:21:19.639 bencohen: So imagine a collective of pool store owners, and they all are saying.
210 00:21:19.670 ⇒ 00:21:23.539 bencohen: Let’s form an alliance, and we can buy in tremendous bulk.
211 00:21:23.710 ⇒ 00:21:24.640 Uttam Kumaran: Okay, okay, makes sense.
212 00:21:24.640 ⇒ 00:21:28.170 bencohen: To to draw down costs. That’s the idea.
213 00:21:28.170 ⇒ 00:21:31.319 Daniel Schonfeld: Exactly right. And we’re going to need to replenish them. So
214 00:21:31.880 ⇒ 00:21:39.370 Daniel Schonfeld: is using this this analysis. Can can we do that? The biggest thing for them is going to be same day
215 00:21:39.560 ⇒ 00:21:41.920 Daniel Schonfeld: or next day at the worst.
216 00:21:41.970 ⇒ 00:21:45.779 Daniel Schonfeld: delivery to all stores. We’re going to have to figure that out at some point
217 00:21:46.320 ⇒ 00:21:50.589 Daniel Schonfeld: again, don’t don’t worry about inventory at the moment these guys have
218 00:21:50.890 ⇒ 00:21:53.100 Daniel Schonfeld: an unusual amount of capital
219 00:21:53.160 ⇒ 00:21:56.657 Daniel Schonfeld: behind them. So let’s just imagine,
220 00:21:57.620 ⇒ 00:22:03.709 Daniel Schonfeld: yeah, can we do this analysis with this? If I gave you, I don’t know 20 of the top skews.
221 00:22:03.870 ⇒ 00:22:13.089 Daniel Schonfeld: and I gave you with an overlay of all their stores addresses. Could we do an analysis to say, Okay, now, if you number one, we can fulfill it with Unis.
222 00:22:13.190 ⇒ 00:22:17.050 Daniel Schonfeld: and at most one day and figure out strategically where to place goods.
223 00:22:17.060 ⇒ 00:22:21.089 Daniel Schonfeld: Could we do some analysis even before next week, and show them that in a slide.
224 00:22:21.530 ⇒ 00:22:27.239 Uttam Kumaran: Yeah, I mean, if you can get me the locations. And then an estimation of like
225 00:22:27.410 ⇒ 00:22:34.070 Uttam Kumaran: what volume needs to go where basically, we could look at it with our, with our 4 locations that we’re planning
226 00:22:34.150 ⇒ 00:22:43.489 Uttam Kumaran: like, what does it look like? And then again, ideally, talk about what? What’s once, like, we’re really confident versus other locations where
227 00:22:44.031 ⇒ 00:22:51.669 Uttam Kumaran: we may not be confident again. Really, the areas that we’re going to have trouble is like this upper band.
228 00:22:52.554 ⇒ 00:22:56.009 Uttam Kumaran: But again, like, that’s not where we sell. So that’s where
229 00:22:56.190 ⇒ 00:23:01.879 Uttam Kumaran: like, if we were to have locations to fulfill, we would need to consider. But
230 00:23:01.990 ⇒ 00:23:04.979 Uttam Kumaran: this has been purely on our customer. Demand.
231 00:23:04.980 ⇒ 00:23:05.640 bencohen: Right.
232 00:23:06.030 ⇒ 00:23:07.320 bencohen: Right, right.
233 00:23:07.320 ⇒ 00:23:21.420 Uttam Kumaran: Well, yeah, I mean again, though, that’s how we did. Is we basically, we 1st started by looking at where locations we have now, where the demand is, and where often they should be. The same thing we would do is like we would consider them the customers
234 00:23:21.510 ⇒ 00:23:25.579 Uttam Kumaran: with X amount of demand per place, and then ideally say.
235 00:23:25.790 ⇒ 00:23:33.420 Uttam Kumaran: like, Where should our warehouses be? And then I, the nice thing is, we actually have the list of all the units, locations, too. So.
236 00:23:33.790 ⇒ 00:23:38.999 bencohen: We could overlay it on top of it. Yeah, I mean, it’s different, because they’re gonna have stores all over the place.
237 00:23:39.030 ⇒ 00:23:42.970 bencohen: places that like. They’ll have stuff in like Montana.
238 00:23:43.860 ⇒ 00:23:52.570 bencohen: Idaho, probably where we really have nothing going on. But they, you know, have a stranglehold over that county that they’re in, or whatever
239 00:23:53.260 ⇒ 00:23:54.100 bencohen: you know. So.
240 00:23:54.100 ⇒ 00:23:57.859 Uttam Kumaran: And it’s like, I mean, the nice, the nice thing, and the
241 00:23:57.970 ⇒ 00:24:03.474 Uttam Kumaran: the not nice thing is like we in theory, should be able to just do this with Eunice.
242 00:24:03.780 ⇒ 00:24:04.770 bencohen: Yeah.
243 00:24:06.040 ⇒ 00:24:06.880 Uttam Kumaran: So.
244 00:24:07.360 ⇒ 00:24:08.330 Daniel Schonfeld: One second.
245 00:24:18.230 ⇒ 00:24:21.527 Daniel Schonfeld: Okay, sorry it was. It was those guys. Actually.
246 00:24:23.200 ⇒ 00:24:24.125 Daniel Schonfeld: okay.
247 00:24:25.780 ⇒ 00:24:29.949 Daniel Schonfeld: sorry to interrupt. That was just my my question. This one that came up while I was like.
248 00:24:29.950 ⇒ 00:24:35.120 bencohen: And let’s show utan. Let’s show the pro versus consumer. Page. This.
249 00:24:36.190 ⇒ 00:24:38.559 bencohen: It’s very easy to understand.
250 00:24:39.570 ⇒ 00:24:51.479 Uttam Kumaran: Yeah. So this is our page on basically like starting to go down the road of customer segmentation. Basically, a thing. We’ve, you know we’ve talked about since the beginning. But really we took action on recently was
251 00:24:52.018 ⇒ 00:24:58.710 Uttam Kumaran: finding out who our professional customers are versus our consumers and using a set of different
252 00:24:59.505 ⇒ 00:25:00.170 Uttam Kumaran: indicators.
253 00:25:00.500 ⇒ 00:25:13.519 Uttam Kumaran: To basically have a really confident understanding of who our consumers are versus who our professional buyers are. First, st we looked at like their self identification. We looked at their email addresses. And then we also looked at like purchase behavior.
254 00:25:14.056 ⇒ 00:25:28.369 Uttam Kumaran: There’s some text changes here to just make this accurate because it’s actually 2 pump orders. What we found is, although pros are just 8% of total customers. They account for 24% of total sales.
255 00:25:28.440 ⇒ 00:25:32.840 Uttam Kumaran: And again, this is in the last rolling 12 months. So
256 00:25:32.910 ⇒ 00:25:47.800 Uttam Kumaran: of course, like what that means is they spend more on the average transaction values are way higher. Their average annual orders are higher. You know, by about like 40%. And their items per order are higher.
257 00:25:47.820 ⇒ 00:26:17.219 Uttam Kumaran: So all indicators show that this is a valuable segment. They spend more. They come back more frequently, and they buy more items for order. And really, that’s probably something that we’ve had a hunch on. But I think the data really really proves that. And the the benefit of actually having these segmented is now we can track it and understand as the segment grows. And so we’re working with Kim. We spoke with Cody last week just about how to target these guys? What is their path
258 00:26:17.340 ⇒ 00:26:41.569 Uttam Kumaran: towards? Not only marketing to them. But do we need to have a different sort of sales strategy around them? And we learned a lot from those conversations. Basically, we have a path towards marketing directly to the existing pro customers. The folks that we already have sales from as well as targeting new. We have some ideas on where, specifically, these pool pros are coming from, and so where to target
259 00:26:42.331 ⇒ 00:26:45.210 Uttam Kumaran: and then also, we we also know
260 00:26:45.340 ⇒ 00:26:49.199 Uttam Kumaran: where what kind of products they’re buying and what kind of products they’re not buying
261 00:26:49.591 ⇒ 00:26:56.528 Uttam Kumaran: and so we have a couple of specific steps that we’re going to kind of walk through to try to grow that segment.
262 00:26:59.290 ⇒ 00:27:04.200 bencohen: The thing, Dan, that I think that Uag will like with this is this represents
263 00:27:04.470 ⇒ 00:27:14.059 bencohen: couple of things, one probably an unintended consequence of direct to consumer and doing it a good job of it, because we got the attention of the pro community and their.
264 00:27:14.080 ⇒ 00:27:15.980 bencohen: So that’s that. And 2,
265 00:27:16.680 ⇒ 00:27:19.700 bencohen: they’re worth a ton. Obviously, these people know that.
266 00:27:20.340 ⇒ 00:27:22.536 bencohen: and we know how to get them.
267 00:27:24.100 ⇒ 00:27:28.420 bencohen: they’ll love this. It’s a huge growth opportunity. It’s it’s separate from like
268 00:27:28.650 ⇒ 00:27:31.619 bencohen: we were talking about with activating platforms.
269 00:27:32.030 ⇒ 00:27:32.950 bencohen: It’s a whole nother.
270 00:27:32.950 ⇒ 00:27:42.410 Daniel Schonfeld: And are we going to be able to show them the remember, we talked about the having something actually kind of a working model where we can actually manipulate it on us on a website.
271 00:27:43.040 ⇒ 00:27:46.220 bencohen: I think the only real is the one for that. I don’t.
272 00:27:46.400 ⇒ 00:27:47.040 Uttam Kumaran: Yeah.
273 00:27:47.240 ⇒ 00:27:48.580 bencohen: I don’t like. I wouldn’t
274 00:27:48.850 ⇒ 00:27:53.289 bencohen: make anything more. I mean, this is, this was the point of this was to show them
275 00:27:54.980 ⇒ 00:27:59.639 bencohen: how we’re using data to unlock huge value and grow.
276 00:28:01.000 ⇒ 00:28:12.419 Daniel Schonfeld: Okay. But we, you and I discussed making a an actionable site that we can go in and fill with kind of dummy data and show them kind of wow them with a presentation. I, the data is, gonna be great. But
277 00:28:12.450 ⇒ 00:28:19.050 Daniel Schonfeld: you and I discussed making some kind of a working site where you can go in and show them that we’re pulling in data, etc, even if it’s
278 00:28:19.340 ⇒ 00:28:22.099 Daniel Schonfeld: some of the historical data.
279 00:28:22.100 ⇒ 00:28:24.290 bencohen: I mean, I don’t think it’s more valuable than this.
280 00:28:24.740 ⇒ 00:28:26.170 bencohen: This is real.
281 00:28:26.740 ⇒ 00:28:29.340 bencohen: this trumps that because this is real
282 00:28:30.430 ⇒ 00:28:31.490 bencohen: and
283 00:28:31.700 ⇒ 00:28:34.000 bencohen: it has value that they can understand.
284 00:28:34.690 ⇒ 00:28:37.160 Daniel Schonfeld: Yeah, I’ll talk to you about that offline. But that that was the.
285 00:28:37.160 ⇒ 00:28:44.429 bencohen: We have to. I mean, his guys are going to be making that. So if if you want something, it has to be discussed here, I mean that’s what I don’t know.
286 00:28:44.430 ⇒ 00:28:48.920 Daniel Schonfeld: Discussed with you was having being able to go in there and have a web page.
287 00:28:49.070 ⇒ 00:28:58.219 bencohen: I know. But we need. There needs to be very clear, like, what are we actually making, or what are we trying to show them what we’re making like. It has to be very clear.
288 00:28:58.610 ⇒ 00:29:01.900 Daniel Schonfeld: You and I discussed this 2 weeks ago about what we were. Gonna do.
289 00:29:02.600 ⇒ 00:29:04.419 Daniel Schonfeld: Alright, let’s just keep going through this.
290 00:29:04.420 ⇒ 00:29:17.589 Uttam Kumaran: Yeah, real and real exists as well. So if you guys can let me know, like, if there’s something beyond that or something in between that can think of the think through that. I mean, like, this doesn’t have any interactivity, but we can add some
291 00:29:18.770 ⇒ 00:29:19.730 Uttam Kumaran: But
292 00:29:20.220 ⇒ 00:29:24.210 Uttam Kumaran: those are the 2 major things we’ve we’ve changed recently. Of course we have.
293 00:29:24.210 ⇒ 00:29:30.309 bencohen: We, Tom? We talked about it. It’s you know. How do we show some smoke and mirror, some like completely outrageous kind of
294 00:29:31.190 ⇒ 00:29:34.289 bencohen: data? I’m not a huge fan of.
295 00:29:34.590 ⇒ 00:29:39.870 bencohen: generally speaking, doing that, especially when we have data that’s as strong as we have
296 00:29:39.980 ⇒ 00:29:43.739 bencohen: rail here. All the reports that we send each other.
297 00:29:45.330 ⇒ 00:29:46.580 bencohen: I don’t know
298 00:29:46.740 ⇒ 00:29:51.849 bencohen: what else we can do like this is asking you to do something that might not have any value whatsoever.
299 00:29:52.260 ⇒ 00:29:56.269 bencohen: I know, that’s not something you’re trying to focus on. But
300 00:29:56.800 ⇒ 00:29:58.629 bencohen: what kind of crazy
301 00:29:58.760 ⇒ 00:30:01.780 bencohen: reports can we create is basically the question
302 00:30:02.520 ⇒ 00:30:08.779 bencohen: I’m like you. I’m utility. Only you know what will actually move the needle. But in this case
303 00:30:09.310 ⇒ 00:30:12.820 bencohen: it’s asking something not abstract, but
304 00:30:13.000 ⇒ 00:30:15.229 bencohen: like fantastic. You know.
305 00:30:20.910 ⇒ 00:30:23.229 bencohen: I think it’s tough, because we actually have
306 00:30:23.410 ⇒ 00:30:25.679 bencohen: very high quality real data.
307 00:30:26.640 ⇒ 00:30:29.249 bencohen: It’s not like a Vc pick.
308 00:30:29.430 ⇒ 00:30:30.459 Daniel Schonfeld: Where you have nothing to turn.
309 00:30:30.460 ⇒ 00:30:32.399 bencohen: Trying to make it look like it, like AI.
310 00:30:32.400 ⇒ 00:30:35.360 Daniel Schonfeld: Is great and useful, and we’ll go in a deck.
311 00:30:35.690 ⇒ 00:30:44.949 Daniel Schonfeld: But that’s not the point of the presentation. It’s to wow them with what could be, and what we’re capable of that was the point of this
312 00:30:45.010 ⇒ 00:30:54.150 Daniel Schonfeld: exercise was for me to go in there and show them an interactive site that we can go pull data instantly, using historical now.
313 00:30:54.260 ⇒ 00:30:58.929 Daniel Schonfeld: but showing them the potential for the future to draw in other variables. And we had discussed
314 00:30:59.050 ⇒ 00:31:02.620 Daniel Schonfeld: bringing in weather patterns we had discussed pulling in
315 00:31:03.113 ⇒ 00:31:16.130 Daniel Schonfeld: different fake dummy store data versus online and figuring out the Omni Channel aspect of it. Those are all the things you and I discussed separately, and you said you understood what the objective was. This is all great.
316 00:31:16.330 ⇒ 00:31:19.839 Daniel Schonfeld: and I can use it, and it’s very important for what we’re doing.
317 00:31:20.110 ⇒ 00:31:26.780 Daniel Schonfeld: But I’m trying to create a vision of the future to these people of what could be
318 00:31:26.910 ⇒ 00:31:30.730 Daniel Schonfeld: and what we already know. So this would be part of the presentation of saying
319 00:31:30.880 ⇒ 00:31:32.880 Daniel Schonfeld: we really understand our business
320 00:31:32.930 ⇒ 00:31:34.669 Daniel Schonfeld: and how to optimize it.
321 00:31:34.710 ⇒ 00:31:44.509 Daniel Schonfeld: But the future, this is what it looks like when we’re really using modern technology to predict and understand what the future is going to be.
322 00:31:45.000 ⇒ 00:31:48.209 Daniel Schonfeld: And it gets smarter with time as we add data to it.
323 00:31:48.380 ⇒ 00:31:52.499 Daniel Schonfeld: And so what I’m looking for is something a bit interactive
324 00:31:52.860 ⇒ 00:31:54.559 Daniel Schonfeld: at the very least
325 00:31:55.020 ⇒ 00:31:56.810 Daniel Schonfeld: screenshots. But
326 00:31:57.470 ⇒ 00:32:02.519 Daniel Schonfeld: I’ve pulled together things like this like I did a pitch for an enterprise system for Verizon
327 00:32:02.720 ⇒ 00:32:04.530 Daniel Schonfeld: when it was just 3 people.
328 00:32:04.740 ⇒ 00:32:08.190 Daniel Schonfeld: and we almost won a hundred 1 million dollar contract.
329 00:32:08.350 ⇒ 00:32:10.250 Daniel Schonfeld: But I went in against
330 00:32:10.710 ⇒ 00:32:15.352 Daniel Schonfeld: a billion dollar company. And all we did was we created a A
331 00:32:16.413 ⇒ 00:32:20.799 Daniel Schonfeld: basically a website. It had all the pull downs, drag and drops. We did this in 4 days.
332 00:32:22.080 ⇒ 00:32:25.510 Daniel Schonfeld: And we were able to show what an enterprise system could be.
333 00:32:25.970 ⇒ 00:32:30.329 Daniel Schonfeld: And I told them, this is not a fully functioning system, but these are the bare bones of it.
334 00:32:30.400 ⇒ 00:32:35.110 Daniel Schonfeld: and we pulled in all sorts of crazy overlay data and variables about mobile customers.
335 00:32:36.370 ⇒ 00:32:41.450 Daniel Schonfeld: and reporting was the biggest part of it. We built a reporting module that they were blown away by
336 00:32:42.870 ⇒ 00:32:43.890 Daniel Schonfeld: anyways.
337 00:32:44.010 ⇒ 00:32:46.010 Daniel Schonfeld: it’s basically a facade.
338 00:32:46.527 ⇒ 00:32:57.439 Daniel Schonfeld: Of what we’re trying to build. It’s like. You know, Bill Gates sold Microsoft and did all that stuff before he even had it. He just showed a presentation of it. Kind of like that.
339 00:32:57.590 ⇒ 00:32:58.680 Daniel Schonfeld: I mean the problem.
340 00:32:58.680 ⇒ 00:32:59.580 Uttam Kumaran: Like.
341 00:32:59.690 ⇒ 00:33:02.560 Uttam Kumaran: yeah, probably where we could land is
342 00:33:02.840 ⇒ 00:33:11.059 Uttam Kumaran: again. I I mean, when I was at Floca, we we when we started the reporting like product area. Yeah, basically, it was all ui first, st
343 00:33:11.080 ⇒ 00:33:14.319 Uttam Kumaran: I mean, we could try to throw together. I mean again. It would.
344 00:33:14.340 ⇒ 00:33:20.769 Uttam Kumaran: I won’t. I don’t think I. I don’t know about interactivity, but it could just be branded with like
345 00:33:20.960 ⇒ 00:33:23.300 Uttam Kumaran: we think about 5 or 8 charts that look.
346 00:33:23.300 ⇒ 00:33:26.079 bencohen: Yeah, looks real. Just has to look real. Doesn’t have to work.
347 00:33:26.080 ⇒ 00:33:28.340 Daniel Schonfeld: Let me ask you this as a stopgap
348 00:33:28.800 ⇒ 00:33:30.570 Daniel Schonfeld: when you got this data.
349 00:33:31.700 ⇒ 00:33:42.730 Daniel Schonfeld: was there an interactive screen where you had to put the data in like, could we do a video of it? And I can show them a short video and pass it off as our proprietary system, not our technology.
350 00:33:42.750 ⇒ 00:33:50.299 Daniel Schonfeld: But our data, how we manipulate it. Is there a way to take videos of that process of how you extracted this data and then show it populate.
351 00:33:50.480 ⇒ 00:33:53.200 Uttam Kumaran: It’s pretty boring. It’s nothing. It’s like coding.
352 00:33:53.200 ⇒ 00:33:54.420 bencohen: Boring is good, but.
353 00:33:54.420 ⇒ 00:33:57.329 Uttam Kumaran: I mean, it’s like it’s all. It’s all like code.
354 00:33:57.480 ⇒ 00:33:58.990 Uttam Kumaran: It’s not like a.
355 00:33:58.990 ⇒ 00:34:00.299 Daniel Schonfeld: Yeah, it’s not a screen.
356 00:34:00.300 ⇒ 00:34:02.470 Uttam Kumaran: It’s not like a screen, or it’s it’s.
357 00:34:02.470 ⇒ 00:34:05.650 Daniel Schonfeld: You let me ask you this? Do you know a good ui.
358 00:34:05.650 ⇒ 00:34:06.170 Uttam Kumaran: Yes.
359 00:34:06.170 ⇒ 00:34:08.069 Daniel Schonfeld: That person have no.
360 00:34:08.429 ⇒ 00:34:09.727 Uttam Kumaran: The woman I
361 00:34:10.229 ⇒ 00:34:20.259 Uttam Kumaran: who is our head of design at flow code who I did it with. I still work with her like she did our website. She runs a design agency. Now she’s who I’d call and be like. We need to put together
362 00:34:20.509 ⇒ 00:34:27.149 Uttam Kumaran: what we called it, like flow lytics at that was our reporting suite. I basically were like, I call her. And basically say.
363 00:34:27.159 ⇒ 00:34:33.229 Uttam Kumaran: we need a 1 screen of like flow edits for this client, which is basically like a ui of like.
364 00:34:33.449 ⇒ 00:34:42.229 Uttam Kumaran: maybe we can even put their logo like they log in or someone logs in, it looks like you can select weather data like I have to think a little bit about what it looks like.
365 00:34:42.581 ⇒ 00:34:48.699 Uttam Kumaran: But it would look like a 1 screen, or maybe a figma prototype, basically that you can try to
366 00:34:49.900 ⇒ 00:34:54.870 Daniel Schonfeld: That’s what I’m trying to do, even if it’s hacked together. And it’s a video, or I call you
367 00:34:55.100 ⇒ 00:34:58.079 Daniel Schonfeld: or her from the from the conference room.
368 00:34:58.110 ⇒ 00:35:12.379 Daniel Schonfeld: and we share a screen, and they show our internal system. I could just make up a thing that I don’t want to share our IP on their network. Whatever I can come up with anything. The point is to wow them. With our understanding of analytics. AI put all the buzz words in. These are good.
369 00:35:12.380 ⇒ 00:35:18.149 bencohen: Re real might blow them away because of the speed that you can change the graphs. And it’s it’s
370 00:35:18.280 ⇒ 00:35:24.770 bencohen: it looks so easy anybody can do it. No one even really knows about real. That might give us the most mileage of all the things.
371 00:35:25.650 ⇒ 00:35:27.270 Daniel Schonfeld: And they’re retail.
372 00:35:27.270 ⇒ 00:35:27.890 bencohen: Data.
373 00:35:28.340 ⇒ 00:35:33.389 Daniel Schonfeld: Yeah, they’re retailers. So making the connection between how we look at data.
374 00:35:33.925 ⇒ 00:35:36.599 Daniel Schonfeld: even the center of gravity and
375 00:35:38.350 ⇒ 00:35:49.490 Daniel Schonfeld: making the connection between how the retail we can optimize their retail stores and give them information in the future to better optimize their inventory, understand weather patterns, buying patterns.
376 00:35:49.490 ⇒ 00:35:54.759 Uttam Kumaran: Yeah. And the thing is with center of gravity. You know, I talked to a friend who worked at Flex Port and like, this is.
377 00:35:54.970 ⇒ 00:36:00.920 Uttam Kumaran: this is stuff that like I’d be surprised that they’re doing so. I think even the fact that we’re doing
378 00:36:01.050 ⇒ 00:36:03.679 Uttam Kumaran: this isn’t is really impressive.
379 00:36:04.380 ⇒ 00:36:12.240 Uttam Kumaran: And like. And again, I’ve talked to a bunch of people that worked in like warehouse optimization. They’re like, Oh, you’re really at the you’re really doing like serious stuff.
380 00:36:12.540 ⇒ 00:36:23.600 Uttam Kumaran: But I see where you’re coming from. But that’s that would be like, okay, let’s just come up with a ui mock of like a reporting suite, and it’s like a 1. Page one page ui mock with like
381 00:36:23.840 ⇒ 00:36:31.220 Uttam Kumaran: in a best case. There’s like a couple of dials. You can click in the worst case, it’s just something you can scroll through, and it just shows
382 00:36:31.340 ⇒ 00:36:41.819 Uttam Kumaran: weather overlay. It shows like some sort of something about warehouse and inventory speed and shipping and customer service like each. There’s modules about
383 00:36:42.200 ⇒ 00:36:45.168 Uttam Kumaran: each of the different areas we’ve gone through.
384 00:36:46.360 ⇒ 00:36:49.589 Uttam Kumaran: and then we just, I, basically will just put a deadline of like.
385 00:36:49.840 ⇒ 00:36:52.340 Uttam Kumaran: We have 4 or 5 days to do that.
386 00:36:53.040 ⇒ 00:36:53.779 Uttam Kumaran: Yes, but.
387 00:36:53.780 ⇒ 00:37:01.020 bencohen: I think it’s important. We, the thing is, we’ve talked about this, and we’ve made stuff that’s very useful to the business. This stuff
388 00:37:01.560 ⇒ 00:37:07.549 bencohen: could be useful, but it’s probably never going to happen. So we need to take like a real departure from things that are like
389 00:37:07.970 ⇒ 00:37:11.300 bencohen: reasonably actionable anytime soon. To.
390 00:37:12.000 ⇒ 00:37:16.069 bencohen: you know, a lot of air. And but we need to be very specific on what
391 00:37:16.570 ⇒ 00:37:23.219 bencohen: this person produces, because a ton of time. That’s why I was telling you to send me this stuff earlier.
392 00:37:24.840 ⇒ 00:37:26.220 bencohen: you know. So
393 00:37:27.450 ⇒ 00:37:29.469 bencohen: that’s mostly Dan.
394 00:37:30.140 ⇒ 00:37:32.549 bencohen: You know what picture he wants to paint here.
395 00:37:32.980 ⇒ 00:37:34.030 Daniel Schonfeld: Well, I’m thinking about the.
396 00:37:34.030 ⇒ 00:37:37.919 bencohen: Call like there needs to be a call like today with this person so they can.
397 00:37:38.310 ⇒ 00:37:52.910 Daniel Schonfeld: Let’s let’s just all of us talk. Okay, let’s just talk logically. If you’re a retail store, what are the things you don’t have? And what are the things you do have? They’re relying on Uag the the members relying on Uag to place the orders.
398 00:37:53.640 ⇒ 00:37:54.509 Daniel Schonfeld: A lot of.
399 00:37:54.510 ⇒ 00:37:55.990 Uttam Kumaran: Any customer data.
400 00:37:56.030 ⇒ 00:38:09.370 Uttam Kumaran: I mean, yeah, I again, I can. Even you’re not gonna have any data on where, how your customers know you any demographic data, any information, if they’ve like, you’re just not gonna have anything about
401 00:38:09.510 ⇒ 00:38:20.959 Uttam Kumaran: the customers themselves. I doubt you have anything really great understanding about you may have good understanding of like inventory, and if your customer is like returned again.
402 00:38:22.320 ⇒ 00:38:23.880 Uttam Kumaran: but you won’t have their email
403 00:38:23.940 ⇒ 00:38:26.570 Uttam Kumaran: like. You can’t do retargeting, remarketing.
404 00:38:27.080 ⇒ 00:38:34.260 bencohen: They’re not gonna have purch a lot the the level of granularity on the purchase information like. So we know that somebody that buys this
405 00:38:34.630 ⇒ 00:38:35.630 bencohen: product.
406 00:38:35.900 ⇒ 00:38:39.129 bencohen: They might in 9 months buy this this next product.
407 00:38:39.500 ⇒ 00:38:44.390 bencohen: That’s really where they’re gonna that’s their biggest blind spot. They don’t. They’re, you know.
408 00:38:44.440 ⇒ 00:38:47.960 bencohen: They don’t know any of that stuff, and they can’t easily figure it out either.
409 00:38:48.100 ⇒ 00:39:11.880 Daniel Schonfeld: What? What are the? I think we all just need to think right. What? What can we walk in there? And then they’ll say, wow! This is fucking valuable. And I think we need to look at our what are the big D to C’s. Let’s take like Vwari, or one of those companies that I keep the big D to C companies. That’s an athletic. Do you guys know that brand? So they I know they set up a physical storefront in West here in Westport, and I asked them.
410 00:39:12.130 ⇒ 00:39:21.619 Daniel Schonfeld: I was like, Why did you set up a store here? They’re like, well, we have a shit load of fucking buyers here, and we have all the data, and we backed it into a retail store, and it just makes sense for them to come into returns.
411 00:39:21.620 ⇒ 00:39:34.229 Uttam Kumaran: Same thing that Casper did like. They, they started primarily online. And then they were like, cool. We need to go to retail because we’re missing some audience. But again the retail is way more. There’s a different set of challenges.
412 00:39:35.361 ⇒ 00:39:37.820 Uttam Kumaran: Right? So there is like a great
413 00:39:38.390 ⇒ 00:39:51.429 Uttam Kumaran: margin and fees and ease of ease of like going online is buying this and having it delivered. That’s different than going in retail. But I would say that if they’re not online at all. Then we have all this online data
414 00:39:51.750 ⇒ 00:40:03.710 Uttam Kumaran: about where they should open their next retail location like what should be the the copy and marketing in the place. How to move people from offline to online, like, you know.
415 00:40:03.710 ⇒ 00:40:22.880 Uttam Kumaran: making sure that they could get warranties or customer service and all that stuff I doubt they have like this is a lot of the stuff we did at at flow code. When we talked to offline, we talked offline or retailers about. Why, putting QR. Codes on all your stuff was important. Because that’s the only way you retarget and own these customers. You know, long.
416 00:40:22.880 ⇒ 00:40:30.089 Daniel Schonfeld: I just thought of something else. They’re part of their grand vision is to expand. I gotta get my headphones. The fucking landscapers
417 00:40:47.600 ⇒ 00:40:49.229 Daniel Schonfeld: make sure it’s connected to here.
418 00:40:50.050 ⇒ 00:40:50.960 bencohen: Can hear you.
419 00:40:51.480 ⇒ 00:40:53.710 Daniel Schonfeld: I know. I I it’s connected.
420 00:40:55.570 ⇒ 00:40:56.830 Daniel Schonfeld: Okay, talk.
421 00:40:57.230 ⇒ 00:40:58.330 bencohen: Can hear you now.
422 00:40:58.490 ⇒ 00:41:16.309 Daniel Schonfeld: Okay, great. One of the things I just I just recalled is that they want to grow Uag membership. That’s part of their grand vision. And they want to leverage our black and Decker brand and our ability to manufacture, to do that and to increase their margins by switching over to our products.
423 00:41:16.756 ⇒ 00:41:33.320 Daniel Schonfeld: One of the things I was just thinking about was similar to what we just discussed, which was Casper and or any of these brands. They have intelligence and data on the consumer online, and then they pass it over to the offline and say, it’s a good idea to set up a store or sell these products here.
424 00:41:33.870 ⇒ 00:41:36.579 Daniel Schonfeld: Perhaps we could make the stretch and say.
425 00:41:37.138 ⇒ 00:41:52.050 Daniel Schonfeld: We’re gonna be able to help you bring on new Uag members, because we’ll have the intelligence of which consumers, or where there’s a higher density of sales for black and decker products. And then we can go to those stores in those areas
426 00:41:52.210 ⇒ 00:42:02.870 Daniel Schonfeld: and convince them with actionable data and real data insights that if you switch over to Black and Decker, you’re what number one, the audience and the the consumer there knows the brand
427 00:42:03.320 ⇒ 00:42:18.067 Daniel Schonfeld: 2. You’re gonna gain higher margins and more profit per store. It creates a more compelling sales pitch now than just join Uag. So you get the buying power and insurance and all these other things. So I think that’s interesting.
428 00:42:19.510 ⇒ 00:42:21.010 Daniel Schonfeld: let me talk to I mean.
429 00:42:21.270 ⇒ 00:42:30.699 Uttam Kumaran: If they’re doing blanket marketing and they have no like, where should we go? Target? Then? It’s a great way to say like, Oh, there’s like a couple of zips in North Florida. You guys need to own
430 00:42:30.780 ⇒ 00:42:32.180 Uttam Kumaran: right? Like that’s
431 00:42:32.220 ⇒ 00:42:49.039 Uttam Kumaran: perfect. Or there, like, you guys don’t have a store in this area. We have a huge concentration of buyers there and or like, in the last year we’ve seen the segment grow. A lot of stuff around that I I mean, I think there’s a ton of stuff to learn from us about just how to do marketing
432 00:42:49.280 ⇒ 00:42:57.749 Uttam Kumaran: and the copy, and like the narrative. But of course, like, if they’re just targeting blanket and they don’t know where to go to find these members. Then.
433 00:42:59.550 ⇒ 00:43:12.220 Daniel Schonfeld: It’s what’s also what’s also interesting is a big problem for these guys. Is they over inventory? And then they’re fucked. Yeah, we might be able to say, since we do have consumer demand in that area, let’s say you ordered 50 more pumps than you should have.
434 00:43:12.260 ⇒ 00:43:23.189 Daniel Schonfeld: We can redirect traffic or augment traffic in that area to sell and have the customer pick up in store. So you never have to worry about over over indexing on any one skew. That’s powerful.
435 00:43:23.190 ⇒ 00:43:25.680 Uttam Kumaran: Yeah, I think the pickup in store.
436 00:43:25.780 ⇒ 00:43:40.249 Uttam Kumaran: Yeah, I mean, I actually don’t know what the economics of that are. But I mean Amazon offers that now a lot of the big big retailers they have home depot target. They all offer that which is like pick up in store, because I think it also gets you in the store. Yeah.
437 00:43:40.250 ⇒ 00:43:40.819 Daniel Schonfeld: That’s right.
438 00:43:41.060 ⇒ 00:43:42.730 bencohen: Marketing thing, too. Yeah.
439 00:43:42.730 ⇒ 00:43:56.760 Daniel Schonfeld: That’s exactly that’s exactly right. And might you could ask my father-in-law this is why he gives away chlorine and all sorts of stuff. He’s got to get him in the store. He knows the Ltv. Of of a customer when they step foot in the door. That’s why he does not sell online at all.
440 00:43:58.162 ⇒ 00:44:07.850 Daniel Schonfeld: Which is quite brilliant. It’s the exact opposite of what everybody does. But for him it works for his model. Yeah, you have to go pick it up in the store to buy it, or to get the deal.
441 00:44:07.850 ⇒ 00:44:12.299 Uttam Kumaran: And and the last thing is these, like there still may be a need for
442 00:44:12.800 ⇒ 00:44:23.880 Uttam Kumaran: like a full service, professional or something else right like to have an in store presence on like a complex product like that where they have a professional or like someone to to chat with.
443 00:44:24.720 ⇒ 00:44:25.950 Uttam Kumaran: You know.
444 00:44:25.950 ⇒ 00:44:26.679 Daniel Schonfeld: That’s right.
445 00:44:28.760 ⇒ 00:44:30.829 Daniel Schonfeld: So let me think about.
446 00:44:31.100 ⇒ 00:44:33.720 bencohen: The the one thing I’m going to state is
447 00:44:34.260 ⇒ 00:44:40.309 bencohen: real will blow their minds. It blew my mind. And I’m like an Internet native.
448 00:44:41.700 ⇒ 00:44:44.189 Daniel Schonfeld: Yeah, but we have to make the connection because they’ll
449 00:44:44.490 ⇒ 00:44:58.180 Daniel Schonfeld: number one. Yes, we want to impress it like, Oh, we have all these gadgets and widgets to understand. Shit, they’re gonna be like, okay, that’s cool. I don’t get it. But the second we make the connection between how it affects their long term value, which is to grow membership and to increase margins.
450 00:44:58.420 ⇒ 00:45:01.640 Daniel Schonfeld: That’s when they’ll make that connection. So we have to figure out what is that
451 00:45:01.830 ⇒ 00:45:21.689 Daniel Schonfeld: 1st thing, we’ll just blow them away and confuse the fuck out of them with a lot of slides and shit. We know these guys are really smart and tech tech. Savvy? That’s the that was my initial goal with what I was telling you. I want to be like these guys know. Shit, I don’t even understand. But you gotta connect the value to it. So at the end of it, they’re like, Okay, they. They’re wizards with all this stuff. But they do get the business, and how
452 00:45:21.870 ⇒ 00:45:23.770 Daniel Schonfeld: I can see how all this wizardry
453 00:45:24.190 ⇒ 00:45:26.540 Daniel Schonfeld: adds value
454 00:45:26.770 ⇒ 00:45:29.700 Daniel Schonfeld: to our membership and to our business, and therefore
455 00:45:29.780 ⇒ 00:45:33.160 Daniel Schonfeld: it’s worth it, to invest and to partner with this company.
456 00:45:33.630 ⇒ 00:45:36.569 bencohen: I think the biggest thing that they can glean from
457 00:45:36.660 ⇒ 00:45:39.799 bencohen: our operation is probably
458 00:45:42.020 ⇒ 00:45:47.860 bencohen: it’s it’s probably sharing data. So all of their customers that buy stuff. We start
459 00:45:48.260 ⇒ 00:45:50.690 bencohen: measuring them within our
460 00:45:51.050 ⇒ 00:45:52.939 bencohen: our product, and then
461 00:45:53.000 ⇒ 00:46:06.670 bencohen: the share would be, here’s like, you know, here’s the list of your like best customers you can sell to them online, you can sell to them in store. But here’s their information. They have a tendency to do this and this.
462 00:46:07.010 ⇒ 00:46:08.280 bencohen: That’s where I think
463 00:46:08.580 ⇒ 00:46:11.500 bencohen: the same exercise we do on our stuff we have to
464 00:46:12.030 ⇒ 00:46:15.160 bencohen: everyone that they sell to in retail because
465 00:46:15.510 ⇒ 00:46:16.559 bencohen: we don’t.
466 00:46:17.220 ⇒ 00:46:20.470 bencohen: We don’t have relationships with pool stores.
467 00:46:20.720 ⇒ 00:46:25.669 bencohen: That’s essentially what they’re trying to on board. Right? Membership to them is a store.
468 00:46:25.990 ⇒ 00:46:26.710 Daniel Schonfeld: Yeah.
469 00:46:26.710 ⇒ 00:46:28.621 bencohen: Or a a group of stores.
470 00:46:31.050 ⇒ 00:46:38.389 bencohen: We don’t have a ton ton there. Not that we can’t. And we we have relationships. But that’s probably the biggest blind spot we have
471 00:46:39.070 ⇒ 00:46:40.100 bencohen: truthfully
472 00:46:40.180 ⇒ 00:46:41.389 bencohen: just among us.
473 00:46:41.660 ⇒ 00:46:47.920 Uttam Kumaran: But it’s like also thinking for them what the value prop to them to the next new store should would be
474 00:46:48.060 ⇒ 00:46:52.979 Uttam Kumaran: beyond. Maybe they’re like discount, whatever their group, buying programs, or whatever
475 00:46:53.431 ⇒ 00:47:02.769 Uttam Kumaran: and like, who to go after like who maybe they go after people who don’t succeed as a uag member right like, how do you go getting customers that will actually succeed.
476 00:47:02.960 ⇒ 00:47:07.169 Uttam Kumaran: and who who already don’t exist again just like spitballing. But.
477 00:47:07.370 ⇒ 00:47:19.770 Daniel Schonfeld: Yeah. By the way, huge value we just saw we did that deal with Pool City. I did a wholesale deal with them for Black and Decker they had never done in ground pools before, only historically above ground.
478 00:47:19.800 ⇒ 00:47:24.070 Daniel Schonfeld: They took a chance, and we told them our our inground pumps are fantastic. Why don’t you try it?
479 00:47:24.660 ⇒ 00:47:27.979 Daniel Schonfeld: They didn’t have crazy success, but they had success.
480 00:47:27.980 ⇒ 00:47:28.320 bencohen: I saw.
481 00:47:28.320 ⇒ 00:47:31.030 Daniel Schonfeld: They didn’t. They didn’t know that until we told them.
482 00:47:31.220 ⇒ 00:47:35.749 Daniel Schonfeld: Then we we push them. So if we have intelligence, saying, Hey, look.
483 00:47:35.850 ⇒ 00:47:39.180 Daniel Schonfeld: you can bring on a new Uag member. We’ll make the sales pitch to them.
484 00:47:39.240 ⇒ 00:47:53.009 Daniel Schonfeld: We’ve got black and decker products. We know people buy them in your area plus our data tells us that X percent of the people that buy in that area buy in ground stuff. Therefore, your skew mix should be 5% in ground.
485 00:47:53.050 ⇒ 00:47:56.650 Daniel Schonfeld: and then maybe in another state, we say it’s 13%, and.
486 00:47:56.650 ⇒ 00:47:56.970 Uttam Kumaran: When I.
487 00:47:56.970 ⇒ 00:48:04.839 Daniel Schonfeld: That data that data gets smarter and smarter because we can take the final sell through from that store, dump it back into the system.
488 00:48:04.950 ⇒ 00:48:13.490 Daniel Schonfeld: and it’ll say it’ll recalculate and say, Okay, we were off by 2%, and it’ll make a recommendation for the next year in a in a potential order for them.
489 00:48:13.500 ⇒ 00:48:18.059 Daniel Schonfeld: So I think that kind of intelligence to me is interesting and could add value
490 00:48:18.090 ⇒ 00:48:27.279 Daniel Schonfeld: and bring on new customers, and say we? We kind of have a cheat sheet for what the skew mix is for what you should be ordering for the next season, or the or the opening season.
491 00:48:27.680 ⇒ 00:48:29.390 Uttam Kumaran: Yeah, and we open up.
492 00:48:29.390 ⇒ 00:48:29.800 Daniel Schonfeld: Value.
493 00:48:29.800 ⇒ 00:48:36.059 Uttam Kumaran: Like we’re looking at skews by states, and which who orders what we, we, we definitely have that already.
494 00:48:36.370 ⇒ 00:48:40.242 Daniel Schonfeld: That’s that’s very interesting. I think I’ll I’ll run it by that. Even
495 00:48:40.690 ⇒ 00:48:43.309 Daniel Schonfeld: pull pro connection which
496 00:48:43.790 ⇒ 00:48:47.719 Daniel Schonfeld: skews to order based on prior
497 00:48:48.400 ⇒ 00:48:51.830 Daniel Schonfeld: consumer buying behavior, consumer behavior
498 00:48:52.330 ⇒ 00:48:53.660 Daniel Schonfeld: from online
499 00:48:54.630 ⇒ 00:48:56.349 Daniel Schonfeld: and translating
500 00:48:56.430 ⇒ 00:48:57.790 Daniel Schonfeld: to
501 00:48:58.470 ⇒ 00:48:59.890 Daniel Schonfeld: UHE.
502 00:49:00.310 ⇒ 00:49:01.510 Daniel Schonfeld: Membership.
503 00:49:02.330 ⇒ 00:49:05.600 bencohen: And I have a question for you. Have they indicated?
504 00:49:05.830 ⇒ 00:49:12.580 bencohen: Have they indicated that their group stores? So like the whole Alliance? Do they use any shared software in store.
505 00:49:14.310 ⇒ 00:49:17.149 Daniel Schonfeld: Shared software installed. I mean, all the Uig members.
506 00:49:17.150 ⇒ 00:49:23.400 bencohen: Like like my thought is like one of the bigger value adds, we could provide is say, here’s like a horizontal
507 00:49:24.450 ⇒ 00:49:32.220 bencohen: customer management situation. But Crm or whatever, so that we can start to measure your customers. I think that if there’s anything we can offer, it’s
508 00:49:32.400 ⇒ 00:49:41.419 bencohen: because you’re saying that’s how I see meeting the world so like you show them real and say, this is on e-commerce. This is how we measure everybody and everything. And this is how we make decisions into
509 00:49:41.540 ⇒ 00:49:43.310 bencohen: logistics. And whatever
510 00:49:44.026 ⇒ 00:49:45.300 bencohen: you guys.
511 00:49:45.380 ⇒ 00:49:48.640 bencohen: you know, let’s let’s just say there’s 500 stores.
512 00:49:49.330 ⇒ 00:49:52.040 bencohen: And there’s 300 operators. And
513 00:49:52.310 ⇒ 00:49:55.039 bencohen: there’s 47 different types of software.
514 00:49:55.710 ⇒ 00:49:58.129 bencohen: That’s a mess. We say
515 00:49:58.300 ⇒ 00:50:06.969 bencohen: we need to marry all of this. We’re going to be like the de facto software, you know, arm of this whole thing.
516 00:50:07.260 ⇒ 00:50:11.219 bencohen: But we want to start helping you guys measure stuff. And then it could help us to.
517 00:50:11.780 ⇒ 00:50:17.429 bencohen: Here’s what everybody should use to capture order, you know, Pos and and Crm combined.
518 00:50:17.860 ⇒ 00:50:23.510 bencohen: Yeah, I think that’s probably the biggest thing we can offer them, because they won’t be able to make sense of customer data. Ever.
519 00:50:23.670 ⇒ 00:50:42.980 Uttam Kumaran: Another another thing is like white labeled online storefronts for every group or every customer. You could say, you could white label our entire shopify site. And basically it all goes to one central management. But everyone can have, like their logo, their branding. And then, you know, I don’t. I don’t know if they’re already doing that. But.
520 00:50:42.980 ⇒ 00:50:51.770 bencohen: I think that’s how we connect the worlds, because I think I understand the idea of of wanting to wow them with tech that they’re like so impressed that they’re confused. And they’re like
521 00:50:52.280 ⇒ 00:50:57.999 bencohen: that. That Ben guy in the meeting. What a nerd! But Whoa! I I understand the desire for that.
522 00:50:58.660 ⇒ 00:51:00.979 bencohen: and maybe we have something for it. But
523 00:51:01.820 ⇒ 00:51:09.029 bencohen: if you say to them, what’s 1 thing? You probably have a blind spot of all the operators would say, we don’t know enough about our customers
524 00:51:09.340 ⇒ 00:51:10.530 bencohen: in data.
525 00:51:10.540 ⇒ 00:51:16.410 bencohen: Maybe they think qualitatively, they do. But quantitatively, they definitely can’t. So
526 00:51:16.930 ⇒ 00:51:18.460 bencohen: I think that’s interesting.
527 00:51:18.850 ⇒ 00:51:30.420 Daniel Schonfeld: Yeah, alright. Well, I’m not gonna make any assumption about what they know. Don’t know a lot of these guys. When I meet them they think they know everything. And think technology is just stupid. And Amazon’s fucking everything up. You know how these guys are.
528 00:51:30.690 ⇒ 00:51:31.909 bencohen: I love my favorite.
529 00:51:31.910 ⇒ 00:51:58.349 Daniel Schonfeld: I don’t wanna. I don’t wanna come in and insult them and say, you guys don’t know shit. We know everything, but I think that the spirit could be is, we could potentially help with marketing, adding value, understanding customers. We have to create a broad swath of opportunity by including us into the mix here, and we have could add value in so many different areas. Whether it be inventory, whether it be marketing consumer insights, there’s opportunity for for developing a platform that everyone could be on.
530 00:51:58.350 ⇒ 00:52:03.100 Daniel Schonfeld: That’s lower cost, easier management. Opportunity to be online. If you’re not
531 00:52:04.600 ⇒ 00:52:11.850 Daniel Schonfeld: for us, it’s a huge win, but obviously a massive undertaking. We’d have to spend a lot of money to build out a system like that. So I promise.
532 00:52:11.850 ⇒ 00:52:18.250 Uttam Kumaran: Connecting, connecting our online customers to some retail storefront. Right? Like, I think.
533 00:52:18.250 ⇒ 00:52:28.450 Daniel Schonfeld: Pick up in store. I love all that. If customer comes in and say, there’s a store here, it’s a Uag member, you know. If you can get a 15% discount. If you just go into the store, we don’t have to go ship it
534 00:52:28.520 ⇒ 00:52:39.360 Daniel Schonfeld: from fucking Texas if they’re in Arizona, and the store is a mile from them. It just makes sense. So we could map all their stores into our system. They go pick up in the store today, and now they have. They get more sales
535 00:52:39.410 ⇒ 00:52:47.450 Daniel Schonfeld: and they’ll order more product. It’s it’s a what Amazon does. It’s why they bought whole foods, etc. It creates more
536 00:52:47.900 ⇒ 00:52:49.949 Daniel Schonfeld: stickiness with the customer.
537 00:52:50.562 ⇒ 00:52:57.080 Daniel Schonfeld: And it just makes a lot of sense, especially for this industry. No one’s doing that Leslie’s is trying to do it, but they don’t do it. Well.
538 00:52:57.540 ⇒ 00:53:00.975 Daniel Schonfeld: okay, I think I think we just figured out what the value was.
539 00:53:01.800 ⇒ 00:53:06.330 Daniel Schonfeld: more or less. It’s how. Now, how do we say that? Or show that?
540 00:53:07.890 ⇒ 00:53:09.430 Daniel Schonfeld: in a simple manner?
541 00:53:09.760 ⇒ 00:53:15.429 Daniel Schonfeld: I think I need to do a tiny bit of homework. I have got a guy I have a call with in 1 h.
542 00:53:16.680 ⇒ 00:53:24.540 Daniel Schonfeld: Yeah, 1 30. Who’s the perfect person to ask this question to? He’s been in the industry 30 years. He’ll build out teams, help build
543 00:53:26.155 ⇒ 00:53:38.089 Daniel Schonfeld: product in our industry, and is a consultant for all the major companies, including us. We’re not Major, but he’s a consultant, and he’ll know the answer to this in 2 seconds. What’s going to be the optimal value out of all these things, or
544 00:53:38.190 ⇒ 00:53:42.250 Daniel Schonfeld: they won’t get it. He’ll tell me in 2 seconds. I have abilities to say it’s fucking brilliant.
545 00:53:42.300 ⇒ 00:53:43.859 Daniel Schonfeld: It’s exactly what they need.
546 00:53:43.870 ⇒ 00:53:46.920 Daniel Schonfeld: We’re just gonna have to put our minds together and figure out, how do we?
547 00:53:48.120 ⇒ 00:53:49.289 Daniel Schonfeld: How do we
548 00:53:49.370 ⇒ 00:53:55.570 Daniel Schonfeld: say this in a simple way that’s understandable in slides and or
549 00:53:56.070 ⇒ 00:54:00.503 Daniel Schonfeld: an interactive screen. I don’t think it’s feasible to do it in one week.
550 00:54:01.230 ⇒ 00:54:04.579 Daniel Schonfeld: but there might be a simpler way to just say it.
551 00:54:04.580 ⇒ 00:54:15.429 Uttam Kumaran: And another thing is, you just walk through a customer journey. You say there’s Joe Schmo he buys from us. He buys from you. Here’s how the current path looks. Here’s the current problems. Here’s like what
552 00:54:15.890 ⇒ 00:54:17.359 Uttam Kumaran: you know and like. That’s
553 00:54:17.580 ⇒ 00:54:19.519 Uttam Kumaran: I don’t know. I mean, I feel like I’ve.
554 00:54:19.520 ⇒ 00:54:20.979 Daniel Schonfeld: Could be as simple as that. Yeah.
555 00:54:21.120 ⇒ 00:54:34.449 Uttam Kumaran: But it could be just. You have a person, and it literally is arrows. And you just narrate like, here’s the here’s the way it goes, you know, again, it seems here it’s actually the focus is the customer. But actually, the focus is the members. So everything goes around like.
556 00:54:34.460 ⇒ 00:54:53.959 Uttam Kumaran: I don’t know what metrics they care. The only metric I’ve heard is they care about adding on new members right? But are there any other things about those member programs that they care about just layering that on. So whatever, maybe that’s a good question for the guy later is like, what are, what do you think they care about these numbers, for beyond just like adding more
557 00:54:55.380 ⇒ 00:55:00.169 Daniel Schonfeld: Yeah, I actually love that. The simple approach, too, is like, really dub it down with fucking stick figures.
558 00:55:00.425 ⇒ 00:55:01.190 Uttam Kumaran: Check. That’s like.
559 00:55:01.190 ⇒ 00:55:01.970 Daniel Schonfeld: Yeah.
560 00:55:01.970 ⇒ 00:55:03.369 Uttam Kumaran: Said I, I.
561 00:55:03.370 ⇒ 00:55:04.739 Daniel Schonfeld: The Airbnb Approach.
562 00:55:04.740 ⇒ 00:55:25.659 Uttam Kumaran: Here’s the current path. Here’s where this person is lost, and like we, we lose margin, and here’s where they go to us, and they come back to a store, they go to a store. They come back online later here. And then also the thing I I somehow at flow code. They were literally like, write the data points that we get that we didn’t get before. Like, literally write the columns that we now get.
563 00:55:25.700 ⇒ 00:55:30.410 Uttam Kumaran: And I’m like, that’s insane. They’re like, No, no, just say, like, we get 10 additional data points
564 00:55:30.450 ⇒ 00:55:34.429 Uttam Kumaran: just saying to say that I’m like, Okay.
565 00:55:34.430 ⇒ 00:55:38.579 bencohen: It’s that call. Dan is important. Because even with that
566 00:55:39.140 ⇒ 00:55:40.680 bencohen: we’re still far apart.
567 00:55:40.960 ⇒ 00:55:47.389 bencohen: because we’re direct to consumer. And we’re good at getting customers to buy stuff and some service professionals which
568 00:55:47.520 ⇒ 00:55:52.490 bencohen: might address a little bit of their need to add members. But adding members is not a discipline that we have
569 00:55:53.460 ⇒ 00:55:55.579 bencohen: any competency in really.
570 00:55:55.910 ⇒ 00:55:56.560 Daniel Schonfeld: Yeah.
571 00:55:57.030 ⇒ 00:55:58.100 bencohen: So how to use.
572 00:55:58.100 ⇒ 00:56:16.395 Daniel Schonfeld: A presentation of how we can display. I don’t even care if you and I have to walk around the room or have fucking little model stores in front of us. I think it’s the whole reason I wanted to do that is cause if you put me in front of a screen of something I don’t understand. I’m gonna go. Oh, this is cool like no one gives a shit. Really, no one gives a shit.
573 00:56:16.894 ⇒ 00:56:43.579 Daniel Schonfeld: But when you start standing up and you start taking them through an interactive model, I, my goal was to have them do it and say, Hey, what what are the things that you’d want to know that would affect your inventory. They’re gonna say, well, if the weather sucks, we’re fucked. If the the economy sucks, you’re fucked. And and I was gonna say, well, what if you had your finger on the pulse where you can predict certain things? We can pull in weather patterns. We can pull in economic factors. And you can start to figure out what skews you need more of or less of.
574 00:56:43.680 ⇒ 00:56:51.889 Daniel Schonfeld: prior to, maybe with a little more intelligence than just winging it and saying, Oh, we usually order cover pumps in the fall, and we usually spend a million dollars.
575 00:56:51.900 ⇒ 00:57:03.070 Daniel Schonfeld: What if you had insight into how the rest of the industry was doing, how the cover pumps were selling across the country. If consumers were buying it. Like to me. That’s a powerful thing, and I was going to have them
576 00:57:03.130 ⇒ 00:57:06.130 Daniel Schonfeld: and show them. Say, Hey, you drag in the things that matter to you.
577 00:57:06.530 ⇒ 00:57:11.770 Daniel Schonfeld: but we can still have the same impact if we walk them through that journey in a different way.
578 00:57:12.195 ⇒ 00:57:14.170 Daniel Schonfeld: It just has to be.
579 00:57:14.510 ⇒ 00:57:18.020 Daniel Schonfeld: They have to participate in this process. So think about that, Ben.
580 00:57:18.030 ⇒ 00:57:23.320 Daniel Schonfeld: for when you’re there is, how do we? How do we take them physically through it or get them involved?
581 00:57:23.350 ⇒ 00:57:25.420 Daniel Schonfeld: So they feel it and understand it.
582 00:57:27.680 ⇒ 00:57:34.049 Daniel Schonfeld: And I’ll verify I’ll have the the answers before we take the test of what bothers them from Rob.
583 00:57:34.130 ⇒ 00:57:38.560 Daniel Schonfeld: so I’ll know in advance of what those pain points are, what the emotion is
584 00:57:38.993 ⇒ 00:57:43.839 Daniel Schonfeld: and what their their real struggles are as retailers in as part of that group.
585 00:57:44.390 ⇒ 00:57:46.940 bencohen: Also, we need to know why they’re interested in us.
586 00:57:47.600 ⇒ 00:57:48.320 Uttam Kumaran: Yeah.
587 00:57:48.320 ⇒ 00:57:53.620 bencohen: It’s actually because is is, if it’s, is it just? They like the black and Decker brand, and they want to get closer to it.
588 00:57:53.620 ⇒ 00:57:56.599 Daniel Schonfeld: Actually, they actually don’t even want to use Black and Decker for their members.
589 00:57:56.760 ⇒ 00:58:03.419 bencohen: Interesting. So then, what do what do they like about the whole thing? Because it’s hard if there’s no problem set to solve.
590 00:58:04.179 ⇒ 00:58:18.100 Daniel Schonfeld: Well, we are suffering a major problem. It’s part of a much larger conversation. The who’s really behind this is the private equity firm that just sold heritage to Home Depot for 19 billion. They want to do something big in this industry. Now.
591 00:58:18.410 ⇒ 00:58:25.000 Daniel Schonfeld: we are a big piece of the puzzle because we’re only one of the only viable manufacturers and Omni channel marketers in the industry.
592 00:58:25.320 ⇒ 00:58:31.789 Daniel Schonfeld: So we’ve got a big opportunity here on multiple multiple levels. But they’ve said to me a couple of times, Why do we need the online piece?
593 00:58:31.810 ⇒ 00:58:33.300 Daniel Schonfeld: They clearly don’t get it.
594 00:58:35.020 ⇒ 00:58:37.559 Daniel Schonfeld: So I’ve been teaching them.
595 00:58:37.570 ⇒ 00:58:40.530 Daniel Schonfeld: But I still think they’re like, Okay, it’s kind of cool. But how do we.
596 00:58:40.530 ⇒ 00:58:46.549 Uttam Kumaran: Any online moments of that deal like I just, I don’t. I didn’t read anything when it came out, but.
597 00:58:46.770 ⇒ 00:58:47.690 Daniel Schonfeld: Say it again.
598 00:58:47.690 ⇒ 00:58:53.929 bencohen: There wasn’t much. Truthfully, Tom, there was really the deal happened, and just like there was not much news. It was.
599 00:58:53.930 ⇒ 00:58:57.209 Uttam Kumaran: Yeah. And was there any element to that deal that
600 00:58:57.680 ⇒ 00:59:00.050 Uttam Kumaran: sounds like us? Meaning like.
601 00:59:00.050 ⇒ 00:59:00.439 bencohen: A couple.
602 00:59:00.440 ⇒ 00:59:02.930 Uttam Kumaran: That deal was about that like. That’s the thing.
603 00:59:02.930 ⇒ 00:59:10.510 Daniel Schonfeld: Yes, yes, I’ll tell you how. They bought. They bought Srs, which is a much larger distributor.
604 00:59:10.610 ⇒ 00:59:15.679 Daniel Schonfeld: Heritage is one of only 2 or 3 of the largest distributors of product in the country.
605 00:59:16.010 ⇒ 00:59:17.590 Daniel Schonfeld: It’s probably makes up
606 00:59:18.240 ⇒ 00:59:26.910 Daniel Schonfeld: less than 10% of the overall value of the 19 billion, much less. So it’s kind of an afterthought. But everyone in the industry is very concerned.
607 00:59:27.010 ⇒ 00:59:33.119 Daniel Schonfeld: because now you have a home depot that owns a distributor that also services the the pro.
608 00:59:33.580 ⇒ 00:59:40.260 Daniel Schonfeld: So if they start saying, All right, the pool pro is now going to come to home depot, and they’re going to get pool products there that disrupts
609 00:59:40.360 ⇒ 00:59:42.109 Daniel Schonfeld: everything. The entire industry is.
610 00:59:42.110 ⇒ 00:59:42.600 Uttam Kumaran: Because.
611 00:59:42.600 ⇒ 00:59:43.660 Daniel Schonfeld: Up on distributors.
612 00:59:43.660 ⇒ 00:59:51.249 Uttam Kumaran: You talk about the Home Home Depot online strategy, then, which is like why they’re winning. And they’re also starting an ad network
613 00:59:51.300 ⇒ 00:59:59.309 Uttam Kumaran: in all in their stores. They’re starting a retail out network. They have the pickup in store, like maybe the Home Depot parallel is
614 00:59:59.440 ⇒ 01:00:07.739 Uttam Kumaran: a better of like if we were together. It’s it seems, more like home depot then. Now it’s like separated versus them where they’re fully integrated.
615 01:00:08.160 ⇒ 01:00:18.089 Daniel Schonfeld: Exactly. I’ve I’ve what I’ve pitched to them is I said, guys forget about all the revenue, all the bullshit with the online. Okay, put that aside for a second, I said. We reach pool professionals.
616 01:00:18.280 ⇒ 01:00:29.889 Daniel Schonfeld: Why do you think Home Depot bought Srs. They now have access to pool professionals? They clearly it’s a signal that is the future of pool and service. They bought roofing contractors. All this
617 01:00:30.140 ⇒ 01:00:39.749 Daniel Schonfeld: so knowing that in the future, if Home depot decides to take this whole industry over. Guess what Uag members are going to be going to home depot to buy product in the future?
618 01:00:39.760 ⇒ 01:00:42.780 Daniel Schonfeld: Why do? And they’re gonna be paying a premium for that.
619 01:00:42.940 ⇒ 01:00:45.740 Daniel Schonfeld: And so instead of doing that, we can turn
620 01:00:46.090 ⇒ 01:00:55.970 Daniel Schonfeld: Uag members into their own Mini home depots. And and we are the distributor, and we have the data, we own it. And if you own us and you own this partnership.
621 01:00:56.050 ⇒ 01:01:22.639 Daniel Schonfeld: we don’t have to work with home depot or be beholden to whatever price they want to charge. Because they’ll own the industry. You take control of your own destiny, and we’re already doing it. And that’s why the data you guys just showed me is extremely pertinent. Because we say we’ve already started doing this. It’s not a pipe dream we’re reaching pool professionals. We’re learning how to sell to them. And we’re gathering valuable data to tell us about the future. And now we can direct those people to your stores
622 01:01:22.690 ⇒ 01:01:26.169 Daniel Schonfeld: rather than home depot in the future. And that’s a powerful statement.
623 01:01:26.660 ⇒ 01:01:27.170 bencohen: There’s another.
624 01:01:27.170 ⇒ 01:01:28.010 Daniel Schonfeld: But we need.
625 01:01:28.280 ⇒ 01:01:32.009 bencohen: There’s another another angle here. Why, the Internet
626 01:01:32.290 ⇒ 01:01:36.629 bencohen: operation is so key is, it’s an insurance policy and a hedge. Because
627 01:01:36.700 ⇒ 01:01:43.989 bencohen: if home depot does do that and kicks all of Retail’s ass, which probably will happen because they’ve done it to everyone else.
628 01:01:44.540 ⇒ 01:01:46.070 bencohen: We still sell online.
629 01:01:46.610 ⇒ 01:01:48.799 bencohen: So if their stores have issues.
630 01:01:50.450 ⇒ 01:01:54.480 bencohen: I mean, they have a different problem, but they still have sales from something.
631 01:01:55.650 ⇒ 01:01:57.199 bencohen: It’s a huge hedge.
632 01:01:57.460 ⇒ 01:01:58.540 bencohen: huge.
633 01:01:58.850 ⇒ 01:01:59.490 Uttam Kumaran: Yeah.
634 01:02:03.180 ⇒ 01:02:09.610 bencohen: That’s how private Equity would think. I don’t know if if the buying group would think that way. But private equity would think about this as an insurance.
635 01:02:15.040 ⇒ 01:02:15.470 Daniel Schonfeld: Okay.
636 01:02:17.058 ⇒ 01:02:23.470 Daniel Schonfeld: I do have to run in a few minutes. Do you want to get? Is there more to this? That we want to go through. Okay?
637 01:02:23.800 ⇒ 01:02:31.860 Daniel Schonfeld: awesome. Well, this is obviously extremely valuable information. It’s just again I I was thinking something different when I came on, but it’s it in hindsight, it’s it’s extremely valuable.
638 01:02:31.970 ⇒ 01:02:34.560 Daniel Schonfeld: It’s data I’m going to want to put into a deck.
639 01:02:34.740 ⇒ 01:02:35.230 Uttam Kumaran: Yeah.
640 01:02:35.500 ⇒ 01:02:50.369 Daniel Schonfeld: Start thinking about which pieces I can cut out, and what story we can tell behind it in a very simple way. We, if we say center of gravity ownership, they might get lost. They could. You know what products go where, I don’t know, like simple, dumb it down to the dumbest thing like.
641 01:02:50.884 ⇒ 01:02:52.940 bencohen: Layman, utam just layman.
642 01:02:52.940 ⇒ 01:02:54.340 Uttam Kumaran: Words, you know.
643 01:02:54.340 ⇒ 01:02:55.110 Daniel Schonfeld: And.
644 01:02:55.110 ⇒ 01:02:55.440 Uttam Kumaran: You have a.
645 01:02:55.440 ⇒ 01:02:56.390 Daniel Schonfeld: Is to go.
646 01:02:56.570 ⇒ 01:02:58.920 Daniel Schonfeld: Sorry. Go ahead. You have a demo
647 01:02:59.600 ⇒ 01:03:05.979 Daniel Schonfeld: I have. I hired someone, a professional who’s who’s working through it. She’s just gonna give me the slide. We’ll we’ll dump it in there,
648 01:03:06.520 ⇒ 01:03:11.790 Daniel Schonfeld: and then we’ll need to train Ben up on on what we’re going to talk about. We’re actually doing it early next week.
649 01:03:11.830 ⇒ 01:03:13.440 Daniel Schonfeld: mid to late next week.
650 01:03:13.640 ⇒ 01:03:15.360 Daniel Schonfeld: prior to the meeting.
651 01:03:15.360 ⇒ 01:03:16.320 Uttam Kumaran: Be done.
652 01:03:17.060 ⇒ 01:03:22.940 Daniel Schonfeld: Yeah, we’re gonna do it. We’re gonna do a not a draft a an initial
653 01:03:23.375 ⇒ 01:03:28.999 Daniel Schonfeld: kind of presentation to them to kind of warm them up, and then in person we’ll go into detail
654 01:03:29.180 ⇒ 01:03:30.280 Daniel Schonfeld: of
655 01:03:30.860 ⇒ 01:03:43.619 Daniel Schonfeld: which is how I wanted to do it. I didn’t want to hit them over the head with with all the fluff and stuff before I’m I’m going back to the beginning and telling them the story the history which I already know, but I’m putting it into presentation form, like a true investor deck.
656 01:03:44.032 ⇒ 01:03:50.190 Daniel Schonfeld: And then certain things will be deeper. Dives. This will be one of them. The value of online.
657 01:03:50.600 ⇒ 01:03:53.680 Daniel Schonfeld: not just for us internally, but how it affects their
658 01:03:54.218 ⇒ 01:04:05.631 Daniel Schonfeld: their future and then obviously, other areas on the manufacturing side. But this is a key part point of it, because we’re asking for a for a high valuation.
659 01:04:06.220 ⇒ 01:04:14.160 Daniel Schonfeld: in in this. In this project also, I put you guys on the they have to is important to go forward. Should a transaction happen, you guys are on that. So.
660 01:04:14.160 ⇒ 01:04:14.505 Uttam Kumaran: Right.
661 01:04:14.850 ⇒ 01:04:19.290 Daniel Schonfeld: Solution will will continue in a much bigger way. Should it happen, even if not
662 01:04:19.370 ⇒ 01:04:35.249 Daniel Schonfeld: I, I really don’t give up. I really don’t give a fuck. I want something like this to happen, but I don’t care. We’re still going in the right direction, and probably the values worth a lot more in the future. So it’s just a good exercise to see where we’re at, and it’s good to get noticed by by some, by some big players.
663 01:04:35.930 ⇒ 01:04:44.000 Uttam Kumaran: Can with the deck person, can you? If you’re if she’s sending you drafts back and forth? Can you just CC me on that, and then I’ll just add the slides in.
664 01:04:44.080 ⇒ 01:04:50.520 Uttam Kumaran: and then that way it’s like we don’t send pictures back, and like I’ll just, I’ll just do them, and then you could just tell me edits. That’s fine.
665 01:04:50.520 ⇒ 01:04:56.989 Daniel Schonfeld: Okay, that’s helpful. Yeah. I haven’t seen any yet. I just saw some like color palettes and shit. But she
666 01:04:57.020 ⇒ 01:05:01.049 Daniel Schonfeld: she’s gonna send me stuff this week over the weekend, so you might see some stuff this weekend.
667 01:05:01.290 ⇒ 01:05:04.710 Uttam Kumaran: Yeah, I mean, ideally, my aim was Wednesday. We’re like.
668 01:05:04.870 ⇒ 01:05:10.910 Uttam Kumaran: we’re basically calling it. And then if there’s anything small we can do. But that’s our deadline.
669 01:05:11.460 ⇒ 01:05:17.839 Daniel Schonfeld: Okay, scrap the Ui person I don’t think we have time to do it now. But we we can make this work. We could just create.
670 01:05:17.840 ⇒ 01:05:22.440 Uttam Kumaran: I think there, I think there is a way to make these slides, and the data look
671 01:05:22.870 ⇒ 01:05:29.119 Uttam Kumaran: like pretty good. And like it’s not not being like screenshots. It’s actually like a
672 01:05:29.720 ⇒ 01:05:35.560 Uttam Kumaran: like a nice looking graph. But it’s an it’s like out of figma, basically. So we’ll do something like that. It’ll look good.
673 01:05:35.810 ⇒ 01:05:36.650 Daniel Schonfeld: Awesome.
674 01:05:36.760 ⇒ 01:05:38.120 Daniel Schonfeld: Alright, guys, thank you.
675 01:05:38.360 ⇒ 01:05:39.130 Uttam Kumaran: Thanks.
676 01:05:39.130 ⇒ 01:05:40.080 Daniel Schonfeld: What’s up, Nick?
677 01:05:41.430 ⇒ 01:05:43.859 Nicolas Sucari: Hey, guys, thank you. Thank you for everything. Yeah.
678 01:05:43.920 ⇒ 01:05:59.820 Nicolas Sucari: I was thinking about the prototype of Figma. It’s not that that difficult. But yeah, it will take some time, but we can try to do something with the actual presentation that you have and the information that we have on evidence that we can cut and be pasting there and make something a little bit interactive.
679 01:06:00.000 ⇒ 01:06:01.710 Nicolas Sucari: Yeah, I think it’s doable.
680 01:06:02.310 ⇒ 01:06:03.799 Daniel Schonfeld: Awesome man. Thank you.
681 01:06:03.990 ⇒ 01:06:04.530 Daniel Schonfeld: Get him out.
682 01:06:04.530 ⇒ 01:06:05.329 Nicolas Sucari: Thank you. Guys.
683 01:06:05.620 ⇒ 01:06:06.020 Uttam Kumaran: Okay.
684 01:06:06.020 ⇒ 01:06:08.270 Daniel Schonfeld: Alright, fellas! I appreciate you. Thank you. Bye.
685 01:06:08.270 ⇒ 01:06:08.910 Uttam Kumaran: Thank you.
686 01:06:09.360 ⇒ 01:06:10.659 Daniel Schonfeld: You bet. Bye, guys.