Meeting Title: Uttam <> Dan Date: 2024-03-26 Meeting participants: Daniel Schonfeld, Uttam Kumaran
WEBVTT
1 00:00:29.150 ⇒ 00:00:30.070 Daniel Schonfeld: Hey, Tom!
2 00:00:36.130 ⇒ 00:00:36.910 Uttam Kumaran: Hey!
3 00:00:37.060 ⇒ 00:00:38.050 Daniel Schonfeld: How you doing, Mike?
4 00:00:38.310 ⇒ 00:00:39.509 Uttam Kumaran: Good! How are you?
5 00:00:39.870 ⇒ 00:00:40.630 Daniel Schonfeld: See ya.
6 00:00:41.230 ⇒ 00:00:42.320 Uttam Kumaran: Good to see you, too.
7 00:00:42.410 ⇒ 00:00:44.099 Uttam Kumaran: How’s the week going? How’s the month?
8 00:00:44.410 ⇒ 00:00:45.349 Uttam Kumaran: Well, I am.
9 00:00:45.350 ⇒ 00:00:46.090 Daniel Schonfeld: You all.
10 00:00:46.510 ⇒ 00:00:49.861 Uttam Kumaran: It is very nice here. It’s just getting
11 00:00:50.360 ⇒ 00:00:53.110 Uttam Kumaran: like, just getting like a little bit warmer.
12 00:00:54.080 ⇒ 00:00:55.609 Uttam Kumaran: Weather’s getting nice.
13 00:00:56.170 ⇒ 00:00:58.480 Uttam Kumaran: Yeah, not bad. Finally.
14 00:00:59.140 ⇒ 00:00:59.909 Daniel Schonfeld: Where are you getting.
15 00:00:59.910 ⇒ 00:01:00.710 Uttam Kumaran: Austin.
16 00:01:00.710 ⇒ 00:01:01.860 Daniel Schonfeld: Austin right
17 00:01:03.140 ⇒ 00:01:03.730 Daniel Schonfeld: damn.
18 00:01:03.730 ⇒ 00:01:04.430 Uttam Kumaran: History.
19 00:01:04.430 ⇒ 00:01:05.480 Daniel Schonfeld: I’m jealous.
20 00:01:06.600 ⇒ 00:01:07.060 Daniel Schonfeld: free.
21 00:01:07.060 ⇒ 00:01:11.959 Uttam Kumaran: How’s everything with you? Yeah, I heard. It’s like the weather’s been on and off. My sister’s in Philly.
22 00:01:12.040 ⇒ 00:01:17.189 Uttam Kumaran: And she was like, Yeah, it was like freezing one day, and then it’s back on and.
23 00:01:17.610 ⇒ 00:01:23.626 Daniel Schonfeld: Yeah, we we had the last weekend, I think, got in the sixties. And then today it’s back. I don’t know. Forties
24 00:01:24.160 ⇒ 00:01:25.330 Daniel Schonfeld: sucks.
25 00:01:25.804 ⇒ 00:01:28.160 Daniel Schonfeld: We? We haven’t had a spring in like 5 years here.
26 00:01:29.240 ⇒ 00:01:31.149 Uttam Kumaran: Yeah, I feel like it’s like a week.
27 00:01:31.490 ⇒ 00:01:32.970 Uttam Kumaran: You got a week soon.
28 00:01:33.680 ⇒ 00:01:37.656 Daniel Schonfeld: Yeah, it it will happen. But yeah, busy week, which is trying to get
29 00:01:38.540 ⇒ 00:01:39.940 Daniel Schonfeld: lot of containers.
30 00:01:40.632 ⇒ 00:01:51.820 Daniel Schonfeld: For everybody. Containers have been difficult to get in like we had containers leave from China on the seventh of January, just arriving this week, which typically it’s 30 days.
31 00:01:51.950 ⇒ 00:01:53.020 Uttam Kumaran: Yeah.
32 00:01:53.190 ⇒ 00:01:55.439 Daniel Schonfeld: There were issues at the Suez Canal.
33 00:01:55.790 ⇒ 00:01:58.390 Daniel Schonfeld: There’s obviously the whole container thing going on
34 00:01:58.590 ⇒ 00:02:00.800 Daniel Schonfeld: today in in Maryland. Obviously, there’s.
35 00:02:00.800 ⇒ 00:02:01.530 Uttam Kumaran: Yeah.
36 00:02:01.530 ⇒ 00:02:06.019 Daniel Schonfeld: You know more of a concern for the people there, but it’s just. It’s just a lot of stuff.
37 00:02:06.770 ⇒ 00:02:08.520 Daniel Schonfeld: It’s like a perfect storm this season.
38 00:02:09.060 ⇒ 00:02:19.699 Uttam Kumaran: Yeah, I’ve I I was literally. I’ve been under that bridge like several times I’ve been in that bay, and my friend’s family has a big boat out there.
39 00:02:20.120 ⇒ 00:02:21.890 Uttam Kumaran: It’s insane.
40 00:02:22.150 ⇒ 00:02:26.947 Daniel Schonfeld: Yeah, what’s crazy is this exact time? Last year we were we went over that bridge for my kids
41 00:02:27.540 ⇒ 00:02:31.352 Daniel Schonfeld: a little league tournament we have in in in Maryland. But
42 00:02:31.850 ⇒ 00:02:34.190 Daniel Schonfeld: yeah, very sad story. It’s crazy.
43 00:02:35.310 ⇒ 00:02:38.520 Daniel Schonfeld: I was watching the video up close, and I noticed that the
44 00:02:38.980 ⇒ 00:02:43.273 Daniel Schonfeld: the I don’t know what they’re saying there causes, but I noticed that all the lights went out.
45 00:02:43.500 ⇒ 00:02:46.139 Uttam Kumaran: They’re saying that there was a power daily.
46 00:02:46.140 ⇒ 00:02:46.490 Daniel Schonfeld: Really.
47 00:02:46.490 ⇒ 00:02:48.159 Uttam Kumaran: Sick. Yeah.
48 00:02:48.450 ⇒ 00:02:52.439 Daniel Schonfeld: I just noticed that I just looked briefly at the video. And I saw I was like, Oh, shit! I think that’s a power.
49 00:02:52.440 ⇒ 00:02:58.049 Uttam Kumaran: Because you saw it turned off, and then they kind of cruised and then turned back on. They tried to get it, to turn.
50 00:02:58.620 ⇒ 00:02:58.980 Daniel Schonfeld: Yeah.
51 00:02:58.980 ⇒ 00:03:03.349 Uttam Kumaran: And like, I think they didn’t realize it’s gonna turn off again because it basically be lined
52 00:03:04.210 ⇒ 00:03:05.710 Uttam Kumaran: into the post.
53 00:03:06.210 ⇒ 00:03:09.610 Daniel Schonfeld: It doesn’t, doesn’t look real when you see it. But
54 00:03:09.870 ⇒ 00:03:10.700 Daniel Schonfeld: crazy.
55 00:03:11.990 ⇒ 00:03:12.850 Uttam Kumaran: Yeah.
56 00:03:12.850 ⇒ 00:03:19.215 Daniel Schonfeld: Feel really bad. You could. You could see first responders already on the bridge before that must have been like a traffic stop or ambulance.
57 00:03:19.570 ⇒ 00:03:22.520 Daniel Schonfeld: you know that they didn’t make it. It’s just. It’s very sad.
58 00:03:23.060 ⇒ 00:03:23.880 Uttam Kumaran: Yeah.
59 00:03:24.390 ⇒ 00:03:24.810 Daniel Schonfeld: Last thing.
60 00:03:24.810 ⇒ 00:03:25.769 Uttam Kumaran: And then, yeah.
61 00:03:26.360 ⇒ 00:03:33.680 Uttam Kumaran: I literally just texted a friend this morning. Cause he was also like, Yeah, there’s a ton of shipping the licenses based on that on the east coast.
62 00:03:33.680 ⇒ 00:03:34.720 Daniel Schonfeld: Elliot.
63 00:03:35.000 ⇒ 00:03:37.885 Daniel Schonfeld: That’s a major port for specialized cargo.
64 00:03:38.850 ⇒ 00:03:41.739 Daniel Schonfeld: but there’s a lot of stuff coming in and out of there. It’s all shut down.
65 00:03:41.850 ⇒ 00:03:42.680 Daniel Schonfeld: So
66 00:03:43.130 ⇒ 00:03:44.363 Daniel Schonfeld: anyways. But
67 00:03:45.744 ⇒ 00:03:48.289 Daniel Schonfeld: what we we gonna talk about, we’re gonna talk about.
68 00:03:48.290 ⇒ 00:03:50.569 Uttam Kumaran: We’re gonna talk about scraping stuff.
69 00:03:50.750 ⇒ 00:03:52.860 Daniel Schonfeld: Yeah. So tell me about what you got there.
70 00:03:53.750 ⇒ 00:04:05.789 Uttam Kumaran: Yeah, I mean, since you first email, me about the outscraper stuff, I’ve basically just like one, I think there’s an angle to get that information on direct residences
71 00:04:05.920 ⇒ 00:04:08.489 Uttam Kumaran: based on from the like the county office.
72 00:04:08.530 ⇒ 00:04:11.690 Uttam Kumaran: I think that’s like a pretty easy line to get
73 00:04:11.780 ⇒ 00:04:16.749 Uttam Kumaran: on the consumer side again. I don’t know if you had a chance to ask legal
74 00:04:17.110 ⇒ 00:04:18.689 Uttam Kumaran: like what the
75 00:04:19.140 ⇒ 00:04:24.909 Uttam Kumaran: I don’t know how like. If we were to look at Google Maps and get the pools like we would get the same information.
76 00:04:27.470 ⇒ 00:04:29.039 Uttam Kumaran: so I don’t know.
77 00:04:29.380 ⇒ 00:04:32.059 Uttam Kumaran: I guess we could start there about like the consumer side of things.
78 00:04:32.060 ⇒ 00:04:39.370 Daniel Schonfeld: What were you able to find and then scrape? I saw that there were a list of like I saw island wreck on there. That’s our company for Long Island.
79 00:04:39.770 ⇒ 00:04:52.739 Uttam Kumaran: Yeah. So 2 things. So one I was able to. So the thing I sent like about a few weeks ago was we? I went to the county like, you can go to the county website and basically get all construction permits permits for pools.
80 00:04:52.760 ⇒ 00:05:05.360 Uttam Kumaran: And so those have direct who’s in doing installation as well as the address or the installations happening. The second thing is, being able to get listings for pool service, pool contractors, pool cleaners.
81 00:05:05.400 ⇒ 00:05:11.890 Uttam Kumaran: all those like categories of businesses in any given like jurisdiction
82 00:05:12.100 ⇒ 00:05:18.619 Uttam Kumaran: on Google maps pretty. And that’s I would say, that’s but that’s more of like on the whole sales side, or related to like
83 00:05:19.560 ⇒ 00:05:21.539 Uttam Kumaran: getting the. So the service providers.
84 00:05:21.730 ⇒ 00:05:24.219 Daniel Schonfeld: Do you think we can build something that
85 00:05:25.310 ⇒ 00:05:33.750 Daniel Schonfeld: does all that work for you so like you don’t have to go to every county you think we can build an intelligence that would constantly look for
86 00:05:35.400 ⇒ 00:05:39.229 Daniel Schonfeld: those types of listings in all counties, and keep adding them to a database.
87 00:05:39.540 ⇒ 00:05:42.629 Uttam Kumaran: In terms of the Service Providers.
88 00:05:43.450 ⇒ 00:05:44.670 Daniel Schonfeld: Yeah, or just like.
89 00:05:44.670 ⇒ 00:05:45.240 Uttam Kumaran: Yeah.
90 00:05:45.240 ⇒ 00:05:50.290 Daniel Schonfeld: Start of that, and then maybe we can train it or train an AI to look for
91 00:05:50.770 ⇒ 00:05:54.490 Daniel Schonfeld: pool, permits anything, and keep adding to a database constantly.
92 00:05:54.940 ⇒ 00:06:12.139 Uttam Kumaran: Yeah, basically, the first thing I would suggest is, we populate like, we just pick accounting. We’re like we do 2 things. One is, I get everything that exists today and then have a job that runs and basically is like, add new ones as they get listed or changes. The second thing is, I think, even going one step further
93 00:06:12.250 ⇒ 00:06:21.330 Uttam Kumaran: and figuring out how to automate like, what’s what gets what gets. What happens after like a new listing gets added is that added to like an email list is that.
94 00:06:21.570 ⇒ 00:06:24.799 Uttam Kumaran: are they added directly to postpilot, and then, like a mail is sent
95 00:06:25.248 ⇒ 00:06:30.399 Uttam Kumaran: so I and I on out scraper. We could do that for the listings.
96 00:06:30.430 ⇒ 00:06:31.910 Uttam Kumaran: for like dirt, cheap.
97 00:06:32.070 ⇒ 00:06:32.500 Daniel Schonfeld: 1 s.
98 00:06:34.220 ⇒ 00:06:34.980 Daniel Schonfeld: Hello!
99 00:06:37.650 ⇒ 00:06:38.570 Daniel Schonfeld: Yes.
100 00:06:40.510 ⇒ 00:06:41.610 Daniel Schonfeld: welcome back.
101 00:06:45.300 ⇒ 00:06:53.079 Daniel Schonfeld: He can come whenever it’s not gonna affect me. I just might not be able to. I can’t like sit with him or anything.
102 00:06:54.440 ⇒ 00:06:57.890 Daniel Schonfeld: Okay, no worries. Yeah. Tell me whatever he wants. Okay, bye.
103 00:07:02.540 ⇒ 00:07:05.659 Daniel Schonfeld: too late. They’re gonna need pizza alright. I gotta on a call
104 00:07:06.380 ⇒ 00:07:07.170 Daniel Schonfeld: bye.
105 00:07:07.490 ⇒ 00:07:08.750 Daniel Schonfeld: It’s my wife.
106 00:07:10.700 ⇒ 00:07:18.283 Daniel Schonfeld: She got me home doing all the chores while she’s out with her friends the carpet cleaners coming the the rub. Guys
107 00:07:19.040 ⇒ 00:07:20.860 Daniel Schonfeld: like you. You don’t even work.
108 00:07:22.860 ⇒ 00:07:27.188 Daniel Schonfeld: I’m making my own food making pizzas right now.
109 00:07:28.210 ⇒ 00:07:29.140 Daniel Schonfeld: Looks
110 00:07:30.385 ⇒ 00:07:32.814 Daniel Schonfeld: okay. Sorry. Sorry to interrupt you.
111 00:07:33.220 ⇒ 00:07:36.069 Uttam Kumaran: No, no so basically we can.
112 00:07:36.330 ⇒ 00:07:44.749 Uttam Kumaran: we can get all those listings for new people, basically in a database. And it’s like pretty cheap. It’s like less than a few pennies
113 00:07:44.910 ⇒ 00:07:46.700 Uttam Kumaran: for like
114 00:07:46.960 ⇒ 00:07:48.820 Uttam Kumaran: doing this rate like that. So
115 00:07:49.230 ⇒ 00:07:50.269 Uttam Kumaran: cost is like.
116 00:07:50.270 ⇒ 00:07:50.630 Daniel Schonfeld: Sort of.
117 00:07:50.630 ⇒ 00:07:51.640 Uttam Kumaran: Super negligible.
118 00:07:51.640 ⇒ 00:07:57.007 Daniel Schonfeld: Is it a penny per scrape like like per full data dump? Or is it like for each listing.
119 00:07:59.800 ⇒ 00:08:02.090 Uttam Kumaran: Let me even get you what the.
120 00:08:02.090 ⇒ 00:08:04.969 Daniel Schonfeld: Not that it’s gonna matter for Penny a listing. It’s.
121 00:08:04.970 ⇒ 00:08:10.709 Uttam Kumaran: It was, it was so. They they one they gave me like a bunch of free ones, and
122 00:08:10.830 ⇒ 00:08:15.420 Uttam Kumaran: sure, if I call them, I can get a cheap some cheaper rates, but it was it was so cheap.
123 00:08:18.270 ⇒ 00:08:19.819 Daniel Schonfeld: I don’t need legal
124 00:08:21.000 ⇒ 00:08:23.480 Daniel Schonfeld: to scrape public data.
125 00:08:25.040 ⇒ 00:08:27.889 Daniel Schonfeld: what I do need legal clearance for
126 00:08:27.970 ⇒ 00:08:35.810 Daniel Schonfeld: is sending like, if there’s an email address or a text message. Then I would need to get consent somehow.
127 00:08:36.059 ⇒ 00:08:37.340 Daniel Schonfeld: Okay, so
128 00:08:39.289 ⇒ 00:08:48.579 Daniel Schonfeld: you also can send direct mail. I can send a piece of mail to them. I don’t think I can send email or text because they haven’t opted in for that type of marketing.
129 00:08:48.920 ⇒ 00:08:52.350 Daniel Schonfeld: But if they’re in the public domain, I need to just check.
130 00:08:52.870 ⇒ 00:08:53.490 Daniel Schonfeld: That’s what.
131 00:08:53.733 ⇒ 00:08:57.146 Uttam Kumaran: Let me just like try to get a set together for both of those.
132 00:08:57.390 ⇒ 00:08:59.090 Daniel Schonfeld: Second happen is like this.
133 00:08:59.440 ⇒ 00:08:59.790 Uttam Kumaran: Yeah.
134 00:09:00.700 ⇒ 00:09:01.190 Daniel Schonfeld: I’ll find.
135 00:09:01.190 ⇒ 00:09:09.579 Uttam Kumaran: The second thing is, we had a recent post pilot. I talked to Kim about this yesterday. That’s doing really really well, like the direct mail stuff is kind of crushing.
136 00:09:12.790 ⇒ 00:09:17.199 Uttam Kumaran: So I I think there is like an opportunity there. But maybe we just start with like
137 00:09:17.740 ⇒ 00:09:26.810 Uttam Kumaran: I could put together. Just we can pick a Zip code or again, start in your area and just put together like a basic thing. Look at the data, make sure you have everything. And then
138 00:09:26.850 ⇒ 00:09:30.240 Uttam Kumaran: I can basically say, like, Okay, what’s gonna be the cost to
139 00:09:30.440 ⇒ 00:09:33.239 Uttam Kumaran: keep this up to date with like net new listings.
140 00:09:33.590 ⇒ 00:09:40.940 Uttam Kumaran: But I again, those listings won’t really change. So it may just be like a one time thing. Or if we want to start with a given area and then expand
141 00:09:41.362 ⇒ 00:09:42.929 Uttam Kumaran: cause. I doubt there’s
142 00:09:43.700 ⇒ 00:09:48.990 Uttam Kumaran: like, how many in in that zip code that I sent like how many pool Service providers do you estimate there are.
143 00:09:49.470 ⇒ 00:09:52.608 Uttam Kumaran: This is like, this is like kind of a good like banking question.
144 00:09:53.250 ⇒ 00:09:56.174 Daniel Schonfeld: It is. It’s yeah, I don’t know.
145 00:09:56.540 ⇒ 00:10:00.232 Uttam Kumaran: Okay. So maybe I’ll just run it for that zip and see what the results that is.
146 00:10:00.450 ⇒ 00:10:06.350 Daniel Schonfeld: Yeah, the guy I’m talking to. I we have a consultant. It was like an industry consultant worked at all the big companies.
147 00:10:06.920 ⇒ 00:10:18.806 Daniel Schonfeld: He estimated that. I think it was like 40% of in ground pools, which is 5 million. So stick 2 million. Is that right? 5 million? No.
148 00:10:20.340 ⇒ 00:10:21.350 Uttam Kumaran: Yeah, 2,000,040.
149 00:10:22.189 ⇒ 00:10:24.289 Daniel Schonfeld: Are probably gonna be serviced.
150 00:10:24.750 ⇒ 00:10:33.310 Daniel Schonfeld: But there’s a large there’s I don’t know. I don’t know what the current date is, but in the area that you were looking. It definitely. Skews towards
151 00:10:33.600 ⇒ 00:10:34.680 Daniel Schonfeld: above ground.
152 00:10:35.300 ⇒ 00:10:39.890 Daniel Schonfeld: All above ground’s not really gonna have service people. So I’m guessing it’s a
153 00:10:40.570 ⇒ 00:10:45.610 Daniel Schonfeld: smaller amount, but probably a lot of routes which is good. It’s like dense, probably.
154 00:10:48.320 ⇒ 00:10:51.449 Daniel Schonfeld: But yeah, yeah, I think we just do a small test
155 00:10:51.610 ⇒ 00:10:54.849 Daniel Schonfeld: and see what we can scrape in the specific specific area.
156 00:10:55.811 ⇒ 00:11:04.000 Daniel Schonfeld: We can. Also, the good thing is is that my father in law and all the people work at our stores. Because, did you know that one of the companies you put on there was our store.
157 00:11:05.452 ⇒ 00:11:11.019 Uttam Kumaran: No, I didn’t. I I just I didn’t even like I just like shot, just really quickly ran in trying to get it here.
158 00:11:11.020 ⇒ 00:11:13.360 Daniel Schonfeld: Yeah. Island recreational. I think it was the bottom one on there.
159 00:11:13.360 ⇒ 00:11:16.890 Uttam Kumaran: Oh, okay, I probably I just looked at the top 3. I was like, Okay, cold. He’s like, let’s get.
160 00:11:16.890 ⇒ 00:11:20.980 Daniel Schonfeld: That’s that’s actually good validation. That’s how I know you got the right. But that’s us.
161 00:11:21.340 ⇒ 00:11:26.570 Daniel Schonfeld: So what? Tell me that data set? Where did that one come from that? Had our company on there.
162 00:11:27.307 ⇒ 00:11:40.440 Uttam Kumaran: So that one is from. Oh, yeah, okay, I see it now. Yeah, I have like, I don’t even know I missed it. So that comes from out scraper. Basically, they’re going to Google Maps and and looking up these sorts of categories. So I basically put like pool cleaning
163 00:11:40.450 ⇒ 00:11:43.799 Uttam Kumaran: full repair services, pool supply stores.
164 00:11:43.930 ⇒ 00:11:55.199 Uttam Kumaran: You could put in a different categ couple of different categories, and then they go and find all the listings associated with that and I think I don’t. I think they’re put. They’re basically taking wherever the listing addresses.
165 00:11:55.900 ⇒ 00:11:59.099 Uttam Kumaran: And then, of course, the phone number address
166 00:11:59.190 ⇒ 00:12:00.290 Uttam Kumaran: and
167 00:12:00.520 ⇒ 00:12:04.279 Uttam Kumaran: information related to that. I’m trying to see whether they also have
168 00:12:04.750 ⇒ 00:12:05.820 Uttam Kumaran: the.
169 00:12:05.820 ⇒ 00:12:09.249 Daniel Schonfeld: I thought you got that from. Wait. So there, we’re talking about 2 different things. Then, right? The.
170 00:12:09.250 ⇒ 00:12:13.590 Uttam Kumaran: Talking about 2 different things. There’s one thing from the county office about the residents, residences.
171 00:12:13.590 ⇒ 00:12:14.160 Daniel Schonfeld: Right.
172 00:12:14.160 ⇒ 00:12:21.239 Uttam Kumaran: Second thing is about service providers, the service folks from Google Maps using out scraper.
173 00:12:21.830 ⇒ 00:12:25.109 Uttam Kumaran: So that it’s just coming directly from
174 00:12:25.897 ⇒ 00:12:29.639 Uttam Kumaran: Google Maps, and basically just running a scrape. But then we also have.
175 00:12:29.910 ⇒ 00:12:45.850 Uttam Kumaran: like URL. So for example, if I, if we’re able to get like, Hey, like, let’s say I don’t know if on the island recreational one there is a URL, but I can go to the URL, and then we could try to like scrape that, too. So there’s a lot of like things we could do after we just get the listing.
176 00:12:47.785 ⇒ 00:12:48.210 Daniel Schonfeld: Ship.
177 00:12:49.510 ⇒ 00:12:53.660 Daniel Schonfeld: So when you, how does this scraper work? So like you put in
178 00:12:53.980 ⇒ 00:12:57.409 Daniel Schonfeld: a Zip code and it’ll search Google Maps for.
179 00:12:58.290 ⇒ 00:13:02.270 Uttam Kumaran: Yeah. So let me even let me even show you an example.
180 00:13:02.270 ⇒ 00:13:05.870 Daniel Schonfeld: But it can’t look for physical attributes like a pool in a backyard. Right.
181 00:13:06.420 ⇒ 00:13:12.192 Uttam Kumaran: No, so it’s not look. It’s not doing like a Google Maps image thing. It’s actually just doing
182 00:13:12.800 ⇒ 00:13:13.619 Daniel Schonfeld: The the copy.
183 00:13:13.620 ⇒ 00:13:17.190 Uttam Kumaran: It’s just looking through the listings. So it’s like this is all for, like B to B.
184 00:13:19.940 ⇒ 00:13:22.229 Daniel Schonfeld: Yeah. I wonder if there’s
185 00:13:22.930 ⇒ 00:13:23.890 Daniel Schonfeld: a builder like.
186 00:13:23.890 ⇒ 00:13:29.830 Uttam Kumaran: I I like there is. I did do a bunch of research on. If people could do the image, search.
187 00:13:30.070 ⇒ 00:13:35.299 Uttam Kumaran: The thing is, it’s it’s just like so expensive to do that sort of image recognition.
188 00:13:36.465 ⇒ 00:13:50.620 Uttam Kumaran: We’d have to build like basically, the things I read about building like a pool identification thing is all people that have done it. For like research, basically like training a model to identify pool, the colors, images.
189 00:13:50.850 ⇒ 00:13:56.512 Uttam Kumaran: it’s possible. But again, I that data is the same data that you’d get from the county office.
190 00:13:58.000 ⇒ 00:13:59.360 Uttam Kumaran: But now I will speak.
191 00:13:59.620 ⇒ 00:14:05.010 Daniel Schonfeld: But that’s on new new existing. The county office would only show new, which is a very small percentage like 1%.
192 00:14:05.480 ⇒ 00:14:11.622 Uttam Kumaran: I don’t. I don’t think they would show. I think they show all cause I was seeing records back to like the eighties.
193 00:14:12.335 ⇒ 00:14:12.780 Daniel Schonfeld: Really.
194 00:14:12.950 ⇒ 00:14:15.950 Uttam Kumaran: Yeah, I was seeing like, super super historic records.
195 00:14:16.220 ⇒ 00:14:18.829 Daniel Schonfeld: Alright. Let’s see if we can scrape that data perfect.
196 00:14:19.300 ⇒ 00:14:26.490 Uttam Kumaran: Okay. So let me run. Let’s just I’m gonna run both of these for the one Zip code that we’re focused on.
197 00:14:26.780 ⇒ 00:14:29.309 Uttam Kumaran: And let’s just like that way.
198 00:14:29.480 ⇒ 00:14:35.269 Uttam Kumaran: We at least have our contact before, like scaling it up. And then I’m just gonna try to pull all the
199 00:14:35.960 ⇒ 00:14:41.509 Uttam Kumaran: pool service listings and then also put somewhere all the residential listings.
200 00:14:42.700 ⇒ 00:14:51.559 Daniel Schonfeld: And then my father in law can scrape it against his current database. He’s got 50 60,000 in a database, so he can tell you how what the hit rate is on what you find.
201 00:14:52.070 ⇒ 00:14:52.660 Uttam Kumaran: Don’t!
202 00:14:52.660 ⇒ 00:14:53.200 Daniel Schonfeld: We could see.
203 00:14:53.380 ⇒ 00:15:01.300 Uttam Kumaran: So give you to give you an example. I’ll just show you kind of like how you set up this job in out scraper.
204 00:15:01.610 ⇒ 00:15:03.249 Daniel Schonfeld: You have to write code or.
205 00:15:03.250 ⇒ 00:15:06.139 Uttam Kumaran: No, no, no, this is like it’s like this is like
206 00:15:06.810 ⇒ 00:15:13.489 Uttam Kumaran: super duper, easy. I actually just like came across a tweet of a guy showing how he did this, for, like.
207 00:15:13.850 ⇒ 00:15:18.949 Uttam Kumaran: I think he was looking at like tree cleaning tree cutting services or something. But he’s basically like
208 00:15:19.120 ⇒ 00:15:28.470 Uttam Kumaran: how to do like list. How do? He’s like how to do growth hack some of these B 2 B things. But I was like, Oh, perfect! Let me just go try it. And basically, you put in like these categories. So they have. Like.
209 00:15:28.970 ⇒ 00:15:31.450 Uttam Kumaran: I basically look for like swimming. So they have like
210 00:15:31.510 ⇒ 00:15:36.019 Uttam Kumaran: pool pool supplies for a public swimming pool, swimming, lake, swimming, facility.
211 00:15:36.770 ⇒ 00:15:38.029 Uttam Kumaran: If I search.
212 00:15:38.530 ⇒ 00:15:38.900 Daniel Schonfeld: What
213 00:15:39.775 ⇒ 00:15:40.280 Uttam Kumaran: Or.
214 00:15:40.580 ⇒ 00:15:43.699 Daniel Schonfeld: What? What’s the cost for out scraper? What do they charge.
215 00:15:44.430 ⇒ 00:15:45.285 Uttam Kumaran: It’s
216 00:15:46.140 ⇒ 00:15:46.610 Daniel Schonfeld: Per user.
217 00:15:46.610 ⇒ 00:15:48.910 Uttam Kumaran: Really cheap. So so let’s say, I do
218 00:15:49.380 ⇒ 00:15:51.290 Uttam Kumaran: like 5,000.
219 00:15:51.830 ⇒ 00:15:53.540 Uttam Kumaran: It’s like 13 bucks.
220 00:15:54.150 ⇒ 00:15:55.240 Daniel Schonfeld: 5,000! What?
221 00:15:55.980 ⇒ 00:15:58.399 Uttam Kumaran: 1,000 records.
222 00:15:59.260 ⇒ 00:15:59.720 Daniel Schonfeld: Crazy!
223 00:16:00.690 ⇒ 00:16:03.460 Uttam Kumaran: And then, if I go to like actually there
224 00:16:04.060 ⇒ 00:16:05.560 Uttam Kumaran: pricing here.
225 00:16:06.781 ⇒ 00:16:09.799 Uttam Kumaran: We are looking at the Google Maps
226 00:16:10.750 ⇒ 00:16:12.280 Uttam Kumaran: scraper
227 00:16:15.660 ⇒ 00:16:18.799 Uttam Kumaran: after 500. It’s 3 bucks for a thousand.
228 00:16:19.600 ⇒ 00:16:23.290 Uttam Kumaran: Yeah, which makes sense. So for 10,000, it’s 30 bucks.
229 00:16:23.290 ⇒ 00:16:24.130 Daniel Schonfeld: Is there a Max.
230 00:16:24.444 ⇒ 00:16:24.760 Uttam Kumaran: And
231 00:16:24.990 ⇒ 00:16:31.780 Uttam Kumaran: no, there’s no Max, I mean, I don’t even know whether I I don’t. I want to check like even whether there’s gonna be 1,000 in that zip.
232 00:16:32.550 ⇒ 00:16:35.050 Uttam Kumaran: But again, this is like so cheap.
233 00:16:35.390 ⇒ 00:16:35.780 Daniel Schonfeld: Derek.
234 00:16:35.780 ⇒ 00:16:36.160 Uttam Kumaran: Yeah.
235 00:16:36.160 ⇒ 00:16:37.809 Daniel Schonfeld: Before it’s done.
236 00:16:38.920 ⇒ 00:16:42.660 Uttam Kumaran: Get. I mean, the nice thing is like, if we get a historic set
237 00:16:43.110 ⇒ 00:16:44.250 Uttam Kumaran: like we’re not.
238 00:16:44.360 ⇒ 00:16:46.179 Uttam Kumaran: That’s like a one time thing.
239 00:16:46.400 ⇒ 00:16:52.629 Uttam Kumaran: right? So we’re not like. And then, for example, if we wanted, if we’re like, Oh, we can, we have some understanding like is the data stale. But
240 00:16:53.820 ⇒ 00:16:56.569 Uttam Kumaran: like, I mean, how often are like.
241 00:16:56.810 ⇒ 00:17:01.349 Uttam Kumaran: I think there’s probably something to say for catching new ones and being like
242 00:17:01.440 ⇒ 00:17:03.299 Uttam Kumaran: let’s catch them while they start.
243 00:17:03.300 ⇒ 00:17:03.940 Daniel Schonfeld: Yeah.
244 00:17:03.940 ⇒ 00:17:08.650 Uttam Kumaran: But again, like we can, maybe I what if once we get the data, I can even try to see like, let me even look
245 00:17:08.950 ⇒ 00:17:11.579 Uttam Kumaran: if they have any sort of date on here.
246 00:17:11.589 ⇒ 00:17:12.419 Daniel Schonfeld: Gold.
247 00:17:13.880 ⇒ 00:17:16.549 Uttam Kumaran: Like, if they have a date by which they
248 00:17:16.780 ⇒ 00:17:18.140 Uttam Kumaran: started.
249 00:17:20.109 ⇒ 00:17:21.500 Uttam Kumaran: we can measure like.
250 00:17:21.599 ⇒ 00:17:23.939 Uttam Kumaran: I mean, basically, you can get a sense of like.
251 00:17:24.670 ⇒ 00:17:28.709 Uttam Kumaran: how many like the market of like local service companies.
252 00:17:28.960 ⇒ 00:17:29.889 Daniel Schonfeld: Hold on time.
253 00:17:33.910 ⇒ 00:17:35.510 Daniel Schonfeld: How are you guys? Good. Thank you.
254 00:17:40.650 ⇒ 00:17:42.670 Daniel Schonfeld: In May 16,
255 00:17:44.093 ⇒ 00:17:44.926 Daniel Schonfeld: rusty.
256 00:17:46.936 ⇒ 00:17:49.320 Daniel Schonfeld: are you throwing animals at
257 00:17:50.623 ⇒ 00:17:55.619 Daniel Schonfeld: real a lot of problems with these
258 00:17:59.330 ⇒ 00:18:00.319 Daniel Schonfeld: big license
259 00:18:17.290 ⇒ 00:18:18.070 Daniel Schonfeld: that
260 00:18:56.060 ⇒ 00:18:57.560 Daniel Schonfeld: January. Let’s start
261 00:19:01.300 ⇒ 00:19:01.980 Daniel Schonfeld: okay.
262 00:19:03.840 ⇒ 00:19:04.970 Daniel Schonfeld: Utah.
263 00:19:05.530 ⇒ 00:19:05.870 Uttam Kumaran: Yeah.
264 00:19:05.870 ⇒ 00:19:08.060 Daniel Schonfeld: I’m gonna I’m gonna click! Are you around in 5 min.
265 00:19:08.500 ⇒ 00:19:10.580 Uttam Kumaran: Yeah, yeah, you could just call me on my phone, or whatever.
266 00:19:10.580 ⇒ 00:19:11.330 Daniel Schonfeld: Okay.
267 00:19:12.260 ⇒ 00:19:12.659 Uttam Kumaran: Okay.