Meeting Title: Pro-vs-Consumer-Analysis-Close-Out Date: 2024-06-17 Meeting participants: Jakob Kagel, Uttam Kumaran
WEBVTT
1 00:06:59.630 ⇒ 00:07:00.405 Uttam Kumaran: Yo.
2 00:07:02.220 ⇒ 00:07:04.299 Jakob Kagel: Yo! How are you?
3 00:07:04.830 ⇒ 00:07:05.950 Uttam Kumaran: Update.
4 00:07:06.330 ⇒ 00:07:07.270 Jakob Kagel: How’s it going.
5 00:07:08.470 ⇒ 00:07:09.760 Uttam Kumaran: And.
6 00:07:10.070 ⇒ 00:07:10.595 Jakob Kagel: Are you at.
7 00:07:10.770 ⇒ 00:07:11.500 Uttam Kumaran: The.
8 00:07:11.500 ⇒ 00:07:12.569 Jakob Kagel: You, at.
9 00:07:13.040 ⇒ 00:07:14.480 Uttam Kumaran: Yeah, the usual
10 00:07:14.550 ⇒ 00:07:15.670 Uttam Kumaran: figured.
11 00:07:17.380 ⇒ 00:07:23.850 Uttam Kumaran: yeah, I actually like, yeah, I definitely, I owe this coffee shop like equity in this company dude for
12 00:07:24.090 ⇒ 00:07:27.169 Uttam Kumaran: basically, how many hours I’ve spent here, like.
13 00:07:27.170 ⇒ 00:07:31.929 Jakob Kagel: I don’t know. They should sponsor us. They should give us free coffee. I don’t know.
14 00:07:31.930 ⇒ 00:07:36.019 Uttam Kumaran: I, wanna, yeah, maybe I should. Yeah, or like, yeah, maybe. Honestly, yeah.
15 00:07:36.020 ⇒ 00:07:38.050 Jakob Kagel: We haven’t dealt with any equity.
16 00:07:38.050 ⇒ 00:07:43.586 Uttam Kumaran: I, well, I’m yeah. I’ve actually brought on a lot of business. I told so many people about this place.
17 00:07:43.850 ⇒ 00:07:44.900 Jakob Kagel: Exactly.
18 00:07:45.290 ⇒ 00:07:46.960 Uttam Kumaran: Yeah, how’s stuff with you?
19 00:07:46.960 ⇒ 00:07:56.500 Jakob Kagel: Good good, I mean, had a little bit of a nightmare on Friday like. Then my flight got delayed like 12 h by American to fly.
20 00:07:56.500 ⇒ 00:07:57.610 Uttam Kumaran: No way.
21 00:07:58.420 ⇒ 00:08:10.329 Jakob Kagel: This crazy, and it was not even the flight to Mexico. It was the flight to Dallas like, so I couldn’t make the connecting flight, so I could have driven to Dallas, and they had given me like enough notice. You know.
22 00:08:10.330 ⇒ 00:08:11.120 Uttam Kumaran: Yeah, yeah.
23 00:08:11.120 ⇒ 00:08:19.769 Jakob Kagel: Basically, yeah, like Uber to the airport. Had to like, be in line for 2 and a half hours. Rebooked the flight uber back home and then fly on Saturday. So.
24 00:08:19.770 ⇒ 00:08:27.940 Uttam Kumaran: We had a same like set of nightmare situation like, Yeah, my flight that was delayed from Charlotte. Our flight we’re coming from Boston got delayed.
25 00:08:28.070 ⇒ 00:08:31.479 Uttam Kumaran: and with Charlotte got delayed another 5 h
26 00:08:31.600 ⇒ 00:08:34.370 Uttam Kumaran: instead of getting like a 1 got back at like 8.
27 00:08:34.620 ⇒ 00:08:40.489 Uttam Kumaran: And I’m like, Yeah, I could still right now like, that’s why I had to just get out of the house, because I’m like falling asleep.
28 00:08:40.520 ⇒ 00:08:44.571 Uttam Kumaran: I’m like I just need to be outside drinking my coffee.
29 00:08:44.940 ⇒ 00:08:58.169 Jakob Kagel: Right. I ended up landing like over 24 h like from when I was originally supposed to land. I mean, it’s like, I don’t know I’m trying to get them to give me like a flight credit, or like something like for it. But like.
30 00:08:58.630 ⇒ 00:09:01.689 Uttam Kumaran: Yeah, stay on the phone with them. And like, figure it out. It’s like.
31 00:09:01.690 ⇒ 00:09:11.799 Jakob Kagel: American. They’re so ass, like, they’re like, yeah, they’re like, we can’t do anything on the phone like you have to send it in like Via email like via the website. So like, it basically goes into.
32 00:09:11.800 ⇒ 00:09:12.479 Uttam Kumaran: So they get, you know.
33 00:09:12.480 ⇒ 00:09:14.720 Jakob Kagel: And so like, while you know something right exactly.
34 00:09:14.720 ⇒ 00:09:15.490 Uttam Kumaran: Yeah.
35 00:09:15.580 ⇒ 00:09:17.180 Jakob Kagel: Okay, I said the email, yeah.
36 00:09:17.180 ⇒ 00:09:27.176 Uttam Kumaran: I’m really adamant about like, the problem is like, I have some stuff like that I need to do from like a month ago. But when I get back to it I’ll be like hammering that like, respond.
37 00:09:27.440 ⇒ 00:09:36.255 Jakob Kagel: Acts like, yeah, exactly. I don’t know. I mean, I might give them a little bit of time to respond. But like, otherwise, yeah, I’m just gonna have to keep calling them to us like, Hey.
38 00:09:36.500 ⇒ 00:09:37.110 Uttam Kumaran: Figure it out.
39 00:09:37.110 ⇒ 00:09:38.379 Jakob Kagel: Or this like
40 00:09:38.460 ⇒ 00:09:43.150 Jakob Kagel: at least like, you know, a couple of $100, like, you know. But.
41 00:09:43.150 ⇒ 00:09:43.780 Uttam Kumaran: Yeah.
42 00:09:43.780 ⇒ 00:09:48.694 Jakob Kagel: Also made me have when they rebook my flight, so I don’t wanna derail the meeting. I’ll get off this
43 00:09:49.320 ⇒ 00:09:56.705 Jakob Kagel: flight like they gave me a 6 h layover in Dallas. I’m like, I don’t want to be able to have a layover for 6 h.
44 00:09:56.990 ⇒ 00:09:57.940 Uttam Kumaran: Drove back.
45 00:09:57.940 ⇒ 00:10:14.309 Jakob Kagel: I was basically like, you wanna have a long layover in case there’s more delays. But it was crazy. It was like 10 out of the 13 American flights were delayed in Austin that day like 10 out of 13. I think it’s like.
46 00:10:14.310 ⇒ 00:10:14.780 Uttam Kumaran: Yesterday.
47 00:10:14.780 ⇒ 00:10:15.540 Jakob Kagel: Attendants are.
48 00:10:15.540 ⇒ 00:10:16.640 Uttam Kumaran: This is Friday.
49 00:10:16.820 ⇒ 00:10:17.769 Jakob Kagel: Huh! This is.
50 00:10:18.123 ⇒ 00:10:18.830 Uttam Kumaran: On Friday.
51 00:10:18.830 ⇒ 00:10:19.610 Jakob Kagel: Yeah.
52 00:10:19.790 ⇒ 00:10:26.520 Uttam Kumaran: Yeah. I wonder if it was because, like a Miami like, I think a lot of shit got fucked up from like all the storms there, and like cause they couldn’t.
53 00:10:26.520 ⇒ 00:10:26.850 Jakob Kagel: Sure.
54 00:10:26.850 ⇒ 00:10:28.170 Uttam Kumaran: Planes around.
55 00:10:28.170 ⇒ 00:10:31.525 Jakob Kagel: But like 4 of the flights were to Dallas. I’m like.
56 00:10:31.830 ⇒ 00:10:32.450 Uttam Kumaran: Yeah, yeah, yeah.
57 00:10:32.450 ⇒ 00:10:41.040 Jakob Kagel: I was like a 40 min flight. I’m like, how could the flight be delayed for 12 h like, I don’t know. Anyway, whatever I never flying with them again like ever
58 00:10:41.840 ⇒ 00:10:45.279 Jakob Kagel: I love it so. I love my lesson.
59 00:10:45.300 ⇒ 00:10:49.280 Jakob Kagel: That’s like the worst fall by delay I ever had to deal with. I think it’s like.
60 00:10:49.280 ⇒ 00:10:56.129 Uttam Kumaran: I dealt with some stuff like that that’s like for like from internationally and like, I missed a connection somewhere. And then it’s like cool. Now, I gotta like
61 00:10:56.150 ⇒ 00:10:58.450 Uttam Kumaran: take 3 additional flights.
62 00:10:58.450 ⇒ 00:10:59.020 Jakob Kagel: Right.
63 00:10:59.020 ⇒ 00:11:03.459 Uttam Kumaran: And it’s like dude. You’re in like a hallucination state like walking around like.
64 00:11:04.150 ⇒ 00:11:12.610 Jakob Kagel: Yeah, I mean, it’s good. I’m flying back on Saturday. So it’s like, if I on Saturday it’s like hopefully, I’ll still be able to make it, you know.
65 00:11:12.610 ⇒ 00:11:13.459 Uttam Kumaran: Yeah, yeah, yeah.
66 00:11:13.460 ⇒ 00:11:16.411 Jakob Kagel: Back to Austin without, you know, before Monday.
67 00:11:16.780 ⇒ 00:11:18.560 Uttam Kumaran: Yeah, yeah.
68 00:11:19.864 ⇒ 00:11:21.520 Jakob Kagel: So yeah, we’ll see.
69 00:11:22.392 ⇒ 00:11:26.750 Jakob Kagel: Cool. Yeah. So how did the meeting go? I know you guys had the meeting with them right.
70 00:11:27.200 ⇒ 00:11:40.559 Uttam Kumaran: We? I just basically told, I haven’t. I didn’t have like a to talk too long about it. I basically said that we I wanna come back to them, either, like tomorrow the next day. And like, talk just about this Dan.
71 00:11:41.180 ⇒ 00:11:55.839 Uttam Kumaran: So basically, I don’t know. I thought, like, maybe we try to bring this stuff into real today. And then we just see like, what what else we wanna try and find out before we like, talk with them, basically, or we could basically say, Hey, we have the flag now, like.
72 00:11:56.750 ⇒ 00:11:57.480 Uttam Kumaran: yeah.
73 00:11:58.060 ⇒ 00:12:04.120 Jakob Kagel: Yeah, I mean, definitely, that sounds good. I think. Right? I mean, it’s just like our shopify customers table, right? So we should probably have.
74 00:12:04.120 ⇒ 00:12:04.710 Uttam Kumaran: Yeah.
75 00:12:04.710 ⇒ 00:12:08.473 Jakob Kagel: Your data source in real, anyway, probably for the future.
76 00:12:08.850 ⇒ 00:12:09.860 Uttam Kumaran: Yeah.
77 00:12:10.050 ⇒ 00:12:21.300 Jakob Kagel: But yeah, I think I mean, the things obviously, that we can show is like, yeah, like, aggregate spend. We could show like by product class. Of course, like, I think we can show by region. You know.
78 00:12:21.300 ⇒ 00:12:22.070 Uttam Kumaran: Yeah.
79 00:12:22.070 ⇒ 00:12:24.087 Jakob Kagel: Has more pros
80 00:12:25.080 ⇒ 00:12:39.800 Jakob Kagel: and then I think, like, I don’t know. I mean, I’m kind of think like we maybe come to them just with like, sort of the basic splits first, st and then sort of let them lead like the discussion and and say, like, Okay, this is where we want to go deeper. Maybe.
81 00:12:40.160 ⇒ 00:12:41.930 Uttam Kumaran: Yeah. Yeah. Okay.
82 00:12:44.400 ⇒ 00:12:45.050 Uttam Kumaran: Let me.
83 00:12:45.050 ⇒ 00:12:47.099 Jakob Kagel: Yeah, there’s there’s probably like.
84 00:12:47.620 ⇒ 00:13:04.000 Jakob Kagel: I mean, there’s probably like a marketing angle here, too, where it’s like, I don’t know. I’m just thinking we probably have to join to another table or something, but we could see, like, you know, how many pros have been targeted by each, like how many pros have been targeted, maybe by email marketing.
85 00:13:04.150 ⇒ 00:13:07.900 Jakob Kagel: I don’t know exactly like for each of the marketing tables like
86 00:13:07.950 ⇒ 00:13:14.239 Jakob Kagel: like SMS, or whatever like. How easy that would be to map, cause we maybe don’t have email there.
87 00:13:14.660 ⇒ 00:13:16.650 Jakob Kagel: but I think that’s maybe.
88 00:13:16.650 ⇒ 00:13:20.885 Uttam Kumaran: Yeah, we could probably look at like who’d who we already emailed through Clavio, basically, or.
89 00:13:21.448 ⇒ 00:13:31.610 Jakob Kagel: I think that’s probably a great one that we can knock out, probably pretty easily. Because, yeah, we can just join on email. And then, yeah, it’s like it. It’s boom. It’s like.
90 00:13:31.750 ⇒ 00:13:41.350 Jakob Kagel: here’s sort of like the descriptive like on the pros like, what do they buy? How much they buy where they buy. And then we get, say, Okay, how? Who have we targeted and who have we? Not? I mean, it’s.
91 00:13:41.553 ⇒ 00:13:41.960 Uttam Kumaran: You know.
92 00:13:41.960 ⇒ 00:13:50.890 Jakob Kagel: Caveat it with like this is just the, you know, Clavio Channel, like it’s not counting paid social or attentive, or anything else. But it’s still like
93 00:13:51.370 ⇒ 00:14:05.700 Jakob Kagel: a good breakdown, or whatever to show like this is who we’ve targeted or like. This is the coverage for the the marketing, or whatever cause. That’s their whole thing. They want to market exclusively to the pros or not exclusively, maybe, but majority, you know.
94 00:14:05.700 ⇒ 00:14:06.790 Uttam Kumaran: Yeah, yeah, yeah.
95 00:14:06.790 ⇒ 00:14:09.626 Jakob Kagel: And we can easily answer that question or show like.
96 00:14:10.050 ⇒ 00:14:22.759 Jakob Kagel: how much more that they have to do there. And then it’s like, yeah, you know, basically, we help drive, you know, whatever, because we showed, you know, these are the people that they can target that they haven’t targeted already.
97 00:14:23.250 ⇒ 00:14:26.470 Uttam Kumaran: Maybe today, let’s just let me walk through
98 00:14:26.850 ⇒ 00:14:31.379 Uttam Kumaran: this. Okay, so we have shopify customers already. Here.
99 00:14:31.610 ⇒ 00:14:33.580 Uttam Kumaran: let me create a new model.
100 00:14:37.940 ⇒ 00:14:40.300 Uttam Kumaran: I just create one here, I’ll
101 00:14:41.490 ⇒ 00:14:42.669 Uttam Kumaran: main model
102 00:15:16.070 ⇒ 00:15:18.610 Uttam Kumaran: on customer email.
103 00:15:54.120 ⇒ 00:15:58.565 Uttam Kumaran: Okay, I don’t wanna bring in everything from all order items.
104 00:15:58.970 ⇒ 00:15:59.880 Jakob Kagel: Thing is, I’ve been listening.
105 00:15:59.880 ⇒ 00:16:02.570 Uttam Kumaran: And like, leave it for now let’s decide what we want.
106 00:16:04.020 ⇒ 00:16:07.420 Uttam Kumaran: All order items has like a ton of shit like.
107 00:16:07.420 ⇒ 00:16:15.442 Jakob Kagel: Yeah, I mean, I would just say the things we probably want to bring in from order items is just product, class product, name and skew, maybe
108 00:16:15.740 ⇒ 00:16:18.990 Uttam Kumaran: Okay, yeah, product, class, product, skew, product, name.
109 00:16:18.990 ⇒ 00:16:19.800 Jakob Kagel: Yeah.
110 00:16:20.530 ⇒ 00:16:21.610 Uttam Kumaran: Let’s do that.
111 00:16:23.820 ⇒ 00:16:39.935 Jakob Kagel: And I think we bring in. I mean all the like sales stuff we can. I mean, we can also, I think, bring it in from order items, please. Maybe we have to join on all orders, or we just bring it in from all orders. I mean, when I do it. I I prefer like, if we trust it, just to use total sales from.
112 00:16:40.180 ⇒ 00:16:40.730 Uttam Kumaran: Yeah, but.
113 00:16:40.730 ⇒ 00:16:42.969 Jakob Kagel: Orders like, you know?
114 00:16:52.800 ⇒ 00:16:58.179 Jakob Kagel: I also think like we maybe have to consider like that has refund flag.
115 00:16:58.830 ⇒ 00:16:59.410 Uttam Kumaran: Yeah.
116 00:16:59.410 ⇒ 00:17:20.339 Jakob Kagel: Good to bring into. I mean, I I did consider that like sort of when I was going through the data before. Like for product class on stuff like. When I was looking at the ones that had multiple orders, or I had ordered multiple pumps, I didn’t count it if they had refunded. Because it’s like, you know, they may have just sent one back and then ordered another. So
117 00:17:20.790 ⇒ 00:17:24.639 Jakob Kagel: has refund is like, definitely, I think, a good filter. Yeah.
118 00:17:40.005 ⇒ 00:17:44.450 Uttam Kumaran: I think, on a o dot sales sales
119 00:17:47.490 ⇒ 00:17:50.399 Uttam Kumaran: let’s go from. Let’s just go from here.
120 00:17:59.570 ⇒ 00:18:00.310 Uttam Kumaran: That
121 00:18:01.960 ⇒ 00:18:02.720 Uttam Kumaran: Lisa.
122 00:18:08.930 ⇒ 00:18:11.189 Jakob Kagel: Let’s see a workhouse magic.
123 00:18:24.160 ⇒ 00:18:28.460 Uttam Kumaran: Oh, my, like Vs code may have just died. Okay, one sec
124 00:18:29.040 ⇒ 00:18:30.680 Uttam Kumaran: showcase. I don’t shit
125 00:18:39.600 ⇒ 00:18:40.900 Uttam Kumaran: one sec.
126 00:18:41.820 ⇒ 00:18:42.819 Jakob Kagel: Oh, you’re good.
127 00:19:14.630 ⇒ 00:19:17.729 Jakob Kagel: so I guess this will only have the fields that we brought in. Then.
128 00:19:18.510 ⇒ 00:19:19.360 Uttam Kumaran: Yeah.
129 00:19:19.360 ⇒ 00:19:23.759 Jakob Kagel: Right well do we need to bring in? Is pool pro? Then, like from the other table.
130 00:19:23.760 ⇒ 00:19:25.640 Uttam Kumaran: It should be. It should be here.
131 00:19:25.640 ⇒ 00:19:26.620 Jakob Kagel: Okay, even though.
132 00:19:26.620 ⇒ 00:19:27.890 Uttam Kumaran: Yeah. Brought in everything from.
133 00:19:27.890 ⇒ 00:19:31.900 Jakob Kagel: Select. Oh, you did. Oh, you did select Star from that one. Okay, yeah.
134 00:19:31.900 ⇒ 00:19:34.050 Uttam Kumaran: I think I selected Star. Yeah.
135 00:19:34.500 ⇒ 00:19:36.120 Jakob Kagel: Okay, yeah, I see.
136 00:19:37.520 ⇒ 00:19:38.889 Uttam Kumaran: Let’s see.
137 00:19:41.890 ⇒ 00:19:44.020 Uttam Kumaran: Yeah, his poor price should be there
138 00:20:01.310 ⇒ 00:20:04.000 Uttam Kumaran: probably don’t need to bring in any of this Pii stuff.
139 00:20:06.020 ⇒ 00:20:10.749 Jakob Kagel: Yeah, definitely, I would say, there’s not gonna be anything that there’s gonna be no value out there.
140 00:20:10.880 ⇒ 00:20:15.745 Jakob Kagel: cause it’s all at the individual level. And we need to see everything.
141 00:20:16.120 ⇒ 00:20:18.049 Uttam Kumaran: Once we hit true. Here.
142 00:20:20.380 ⇒ 00:20:21.080 Uttam Kumaran: hey!
143 00:20:27.110 ⇒ 00:20:29.600 Jakob Kagel: So the numbers like 20 K. Though
144 00:20:29.750 ⇒ 00:20:32.040 Jakob Kagel: that’s like higher than what we had.
145 00:20:37.390 ⇒ 00:20:39.049 Uttam Kumaran: This sorted this 20,000.
146 00:20:39.260 ⇒ 00:20:40.100 Jakob Kagel: Right.
147 00:20:41.680 ⇒ 00:20:43.349 Uttam Kumaran: Is higher than what we have. What does that mean?
148 00:20:43.350 ⇒ 00:20:48.150 Jakob Kagel: We have like 16 K, I’m like, is that because, like.
149 00:20:49.220 ⇒ 00:20:55.149 Jakob Kagel: I don’t know if that’s because we joined on email, and we like in some situation. We got multiple rows.
150 00:20:56.400 ⇒ 00:20:58.299 Uttam Kumaran: Like, do now, okay, basically.
151 00:20:59.330 ⇒ 00:21:01.089 Uttam Kumaran: okay, I can take a look.
152 00:21:01.550 ⇒ 00:21:04.721 Jakob Kagel: Yeah, I mean, just because, like
153 00:21:05.750 ⇒ 00:21:08.500 Jakob Kagel: the way that I did it, or whatever is like.
154 00:21:08.590 ⇒ 00:21:12.059 Jakob Kagel: get all the emails like from all orders or whatnot.
155 00:21:12.500 ⇒ 00:21:14.159 Jakob Kagel: and then, like.
156 00:21:14.280 ⇒ 00:21:19.780 Jakob Kagel: join, let me check real quick. Yeah, I just wanna make sure, because this is just like a small sort of
157 00:21:20.280 ⇒ 00:21:21.410 Jakob Kagel: thing. But
158 00:21:21.620 ⇒ 00:21:22.650 Jakob Kagel: we don’t want to double.
159 00:21:22.650 ⇒ 00:21:26.259 Uttam Kumaran: No, let’s check I wanna yeah, I wanna check how you’re doing it. And.
160 00:21:26.260 ⇒ 00:21:26.980 Jakob Kagel: Yeah.
161 00:21:28.390 ⇒ 00:21:33.112 Jakob Kagel: So let me take a look real quick while you’re working on that. See?
162 00:21:34.370 ⇒ 00:21:35.490 Uttam Kumaran: Okay. One. Sec.
163 00:23:01.700 ⇒ 00:23:04.159 Jakob Kagel: I mean, it might just be hold on
164 00:23:04.430 ⇒ 00:23:05.560 Jakob Kagel: second.
165 00:23:37.890 ⇒ 00:23:44.699 Jakob Kagel: Yeah. I mean, if I just do like a very like, let me see, I’ll order items because we did items.
166 00:23:44.700 ⇒ 00:23:48.740 Uttam Kumaran: I mean, I can’t just run in a select and snowflake. Right? I just do select.
167 00:23:48.740 ⇒ 00:23:49.450 Jakob Kagel: Yeah.
168 00:23:49.450 ⇒ 00:23:50.690 Uttam Kumaran: Customer. Id.
169 00:23:51.020 ⇒ 00:23:52.809 Uttam Kumaran: That should be the same thing right?
170 00:23:55.160 ⇒ 00:23:56.390 Jakob Kagel: It should be
171 00:23:56.460 ⇒ 00:23:57.600 Jakob Kagel: this d
172 00:24:14.320 ⇒ 00:24:20.449 Jakob Kagel: cause. If I just join all order items and select distinct email, I’m only getting like, 16 k, yeah.
173 00:24:21.510 ⇒ 00:24:22.930 Uttam Kumaran: Okay. Okay. Let me check.
174 00:24:22.930 ⇒ 00:24:24.550 Jakob Kagel: See? Yeah.
175 00:24:29.800 ⇒ 00:24:31.820 Jakob Kagel: yeah, do we have a chat here? Yeah.
176 00:24:35.590 ⇒ 00:24:37.549 Jakob Kagel: like, if I just do this.
177 00:24:38.350 ⇒ 00:24:42.460 Jakob Kagel: I’m only getting 16 k, so it’s weird that we would get like 20 K.
178 00:24:44.150 ⇒ 00:24:45.370 Uttam Kumaran: Like yours.
179 00:25:08.120 ⇒ 00:25:14.049 Jakob Kagel: It’s like, if I don’t count like, if I don’t make it distinct email, then I’ll get like 37 K.
180 00:25:15.650 ⇒ 00:25:16.420 Uttam Kumaran: Yeah.
181 00:25:16.420 ⇒ 00:25:17.810 Jakob Kagel: So it’s probably not distinct.
182 00:25:17.810 ⇒ 00:25:19.500 Uttam Kumaran: Okay, what’s that? Is.
183 00:25:19.500 ⇒ 00:25:24.000 Jakob Kagel: Probably just like Limited to that time period. There.
184 00:25:59.510 ⇒ 00:26:03.048 Uttam Kumaran: Yeah, let me try to let me change it to distinct.
185 00:26:05.370 ⇒ 00:26:08.130 Jakob Kagel: That should do the trick honestly. But yeah.
186 00:26:46.500 ⇒ 00:26:48.830 Uttam Kumaran: Yeah, I’m seeing the 16.9 K.
187 00:26:49.290 ⇒ 00:26:50.949 Jakob Kagel: Yeah, that should be right. Yeah.
188 00:26:51.460 ⇒ 00:26:53.730 Uttam Kumaran: Okay. Here, let me let me go back to sharing.
189 00:26:58.850 ⇒ 00:26:59.910 Uttam Kumaran: Okay, cool.
190 00:27:00.960 ⇒ 00:27:02.090 Uttam Kumaran: So
191 00:27:02.850 ⇒ 00:27:07.112 Uttam Kumaran: let me try and organize this a little bit. So I’m gonna move
192 00:27:08.220 ⇒ 00:27:09.810 Uttam Kumaran: total sales.
193 00:27:10.870 ⇒ 00:27:11.620 Uttam Kumaran: There’s
194 00:27:12.590 ⇒ 00:27:13.980 Uttam Kumaran: say, it’s on.
195 00:27:18.750 ⇒ 00:27:20.470 Uttam Kumaran: This is.
196 00:27:31.390 ⇒ 00:27:36.779 Jakob Kagel: I mean 16.9 is still higher, though it’s kinda weird that it’s not exact.
197 00:27:37.010 ⇒ 00:27:38.970 Jakob Kagel: But I don’t know.
198 00:27:41.330 ⇒ 00:27:42.430 Jakob Kagel: It’s closed, though.
199 00:27:42.430 ⇒ 00:27:52.820 Uttam Kumaran: Yeah, I’m gonna well, you’re you’re you’re you’re joining us from the other way. I guess I’m I’m we’re joining it from. So there may, there may be customers that don’t have any sales associated with them.
200 00:27:52.820 ⇒ 00:27:57.500 Jakob Kagel: Right? Okay, that’s fair. That’s fair. Yeah, okay, that would explain it.
201 00:27:57.670 ⇒ 00:28:05.001 Uttam Kumaran: Actually, I think that’s what it that’s what it’s that’s what it is, I can confirm. But pretty sure like you can. You can. Even you can. You can. You can get to the point where
202 00:28:06.080 ⇒ 00:28:08.800 Uttam Kumaran: you, you almost check out and basically create an account. And.
203 00:28:08.800 ⇒ 00:28:12.060 Jakob Kagel: Right? Right? Right? Yeah, no. That makes sense. Yeah.
204 00:28:16.190 ⇒ 00:28:18.609 Jakob Kagel: So this is filtered on pros. Now.
205 00:28:20.424 ⇒ 00:28:23.565 Uttam Kumaran: Okay, cool. So just look like helpful right now. So
206 00:28:24.190 ⇒ 00:28:26.240 Uttam Kumaran: yeah, this is filtered to. Pro.
207 00:28:30.930 ⇒ 00:28:32.230 Uttam Kumaran: I guess, like.
208 00:28:33.000 ⇒ 00:28:37.469 Uttam Kumaran: how would would it be helpful to it’d be it’d definitely be helpful to like. Compare both
209 00:28:37.750 ⇒ 00:28:38.890 Uttam Kumaran: like maybe.
210 00:28:38.890 ⇒ 00:28:40.059 Jakob Kagel: I we try to do.
211 00:28:40.060 ⇒ 00:28:40.690 Uttam Kumaran: Yeah.
212 00:28:40.690 ⇒ 00:28:44.699 Jakob Kagel: I think we should have everything as a side by side if we can
213 00:28:45.910 ⇒ 00:28:47.210 Jakob Kagel: like. You know.
214 00:28:47.620 ⇒ 00:28:52.589 Jakob Kagel: we just do one that’s like filtered pro, and one that’s filtered on consumer, and then
215 00:28:52.630 ⇒ 00:28:54.370 Jakob Kagel: have it side by side, and you can.
216 00:28:54.370 ⇒ 00:28:56.970 Uttam Kumaran: No, let’s try this. Let’s see if if this does, it.
217 00:29:00.190 ⇒ 00:29:00.960 Jakob Kagel: Credit.
218 00:29:25.040 ⇒ 00:29:28.410 Uttam Kumaran: I think it’s slow, because joining order items.
219 00:29:29.840 ⇒ 00:29:30.250 Jakob Kagel: Yeah.
220 00:29:30.250 ⇒ 00:29:31.450 Uttam Kumaran: Let’s see so.
221 00:29:32.770 ⇒ 00:29:34.569 Uttam Kumaran: But I can’t have comparing.
222 00:29:34.570 ⇒ 00:29:36.460 Jakob Kagel: Birds, I mean I don’t know.
223 00:29:38.180 ⇒ 00:29:41.558 Uttam Kumaran: I have the comparison here, but I don’t see the other line
224 00:29:41.870 ⇒ 00:29:42.950 Uttam Kumaran: really.
225 00:29:45.665 ⇒ 00:29:46.520 Jakob Kagel: Yeah.
226 00:29:47.026 ⇒ 00:29:48.040 Uttam Kumaran: Hold on!
227 00:29:54.640 ⇒ 00:29:57.843 Uttam Kumaran: Wonder if it’s easiest just to show it here, like if I do.
228 00:29:59.680 ⇒ 00:30:01.060 Uttam Kumaran: it’s cool. Pro.
229 00:30:17.900 ⇒ 00:30:18.600 Uttam Kumaran: just
230 00:30:20.168 ⇒ 00:30:26.300 Uttam Kumaran: so you you were showing that you were showing that the total sale amount.
231 00:30:27.490 ⇒ 00:30:28.979 Uttam Kumaran: Oh, they ordered twice.
232 00:30:28.980 ⇒ 00:30:32.659 Jakob Kagel: The average. Yeah, this is like the average sale amount. Yeah.
233 00:30:33.300 ⇒ 00:30:34.590 Uttam Kumaran: Oh, so average sales.
234 00:30:34.590 ⇒ 00:30:44.469 Jakob Kagel: Yeah. And and this is only when I looked at, I just looked at the last 12 months like, that’s sort of a a typical cut, like, you know, that we look at a lot. And I think basically.
235 00:30:44.470 ⇒ 00:30:47.739 Uttam Kumaran: Well, if you this shows it right. So sub brains show
236 00:30:48.330 ⇒ 00:30:50.790 Uttam Kumaran: full pro true is this much?
237 00:30:51.200 ⇒ 00:30:52.800 Uttam Kumaran: I mean dude. That’s a shitload.
238 00:30:52.800 ⇒ 00:30:53.670 Jakob Kagel: Right.
239 00:30:55.030 ⇒ 00:30:57.840 Uttam Kumaran: So they’re really leading to this much more in the last
240 00:31:01.380 ⇒ 00:31:02.590 Uttam Kumaran: 12 months.
241 00:31:04.660 ⇒ 00:31:10.779 Jakob Kagel: I mean, what’s total sales like? Sorry 9.8 8.3 million.
242 00:31:10.820 ⇒ 00:31:11.950 Jakob Kagel: And then.
243 00:31:12.390 ⇒ 00:31:14.850 Jakob Kagel: yeah, but you have to account for.
244 00:31:15.410 ⇒ 00:31:20.330 Jakob Kagel: Hmm, I don’t know that. Doesn’t they quite tie out with what I had to be honest.
245 00:31:20.620 ⇒ 00:31:21.470 Uttam Kumaran: 5.
246 00:31:21.470 ⇒ 00:31:22.550 Jakob Kagel: Yeah.
247 00:31:22.980 ⇒ 00:31:28.959 Jakob Kagel: I mean the numbers. The overall number seems right, though. But this is the thing is like.
248 00:31:29.010 ⇒ 00:31:35.909 Jakob Kagel: yeah, I was only taking. So I was only taking like pool pros that had made a transaction in the last
249 00:31:36.020 ⇒ 00:31:42.119 Jakob Kagel: 12 months, like when I did that which makes sense, too, because I don’t know. Like.
250 00:31:43.520 ⇒ 00:31:49.239 Jakob Kagel: if you expand it, I guess to the host 16 KI mean, I’m not saying this is wrong either. Like.
251 00:31:49.240 ⇒ 00:31:49.970 Uttam Kumaran: Yeah, yeah.
252 00:31:49.970 ⇒ 00:31:55.179 Jakob Kagel: True, like cause if you expand it and say, like, Okay, is pool pro like
253 00:31:55.650 ⇒ 00:31:57.779 Jakob Kagel: from any point in time.
254 00:31:58.040 ⇒ 00:32:01.749 Jakob Kagel: But then still like doesn’t quite make sense.
255 00:32:01.760 ⇒ 00:32:04.999 Jakob Kagel: Let me see, let me try to query it real quick, and just see
256 00:32:33.850 ⇒ 00:32:37.479 Jakob Kagel: So what’s your time? Period is is last 12 months right?
257 00:32:48.580 ⇒ 00:32:50.699 Uttam Kumaran: It just seems really dramatic.
258 00:32:51.560 ⇒ 00:32:54.100 Jakob Kagel: Yeah, hold on, let me take a look.
259 00:32:59.190 ⇒ 00:33:02.080 Jakob Kagel: So I’m just gonna say, order date greater than
260 00:33:02.190 ⇒ 00:33:05.020 Jakob Kagel: june 16, th 2023,
261 00:33:07.190 ⇒ 00:33:08.550 Jakob Kagel: okay.
262 00:33:34.940 ⇒ 00:33:37.419 Jakob Kagel: yeah, let’s see.
263 00:33:49.810 ⇒ 00:33:57.810 Jakob Kagel: Cause yeah, I was using the average function in sequel. And if you use average function, it’s basically the same. But I’m wondering why that’s not
264 00:33:58.530 ⇒ 00:34:02.120 Jakob Kagel: if I take the sum and look at the count of the emails.
265 00:34:02.750 ⇒ 00:34:08.769 Jakob Kagel: there’s like 12,000 emails. And yes, about 1.1 million, or just over a million
266 00:34:10.880 ⇒ 00:34:13.389 Jakob Kagel: and sales in last 12.
267 00:34:16.550 ⇒ 00:34:18.179 Jakob Kagel: And then what do you have?
268 00:34:18.290 ⇒ 00:34:20.870 Jakob Kagel: Yeah, what do you have as sales for
269 00:34:23.360 ⇒ 00:34:24.090 Jakob Kagel: for?
270 00:34:27.080 ⇒ 00:34:30.279 Jakob Kagel: The consumers? It’s like 900 k. Or something.
271 00:34:33.590 ⇒ 00:34:36.090 Uttam Kumaran: This is just how, what range are you looking in.
272 00:34:36.420 ⇒ 00:34:37.110 Jakob Kagel: Huh?
273 00:34:38.550 ⇒ 00:34:39.439 Jakob Kagel: Are those.
274 00:34:39.449 ⇒ 00:34:40.009 Uttam Kumaran: Deal, yeah, like.
275 00:34:40.010 ⇒ 00:34:40.460 Jakob Kagel: Last 3 months.
276 00:34:40.732 ⇒ 00:34:41.550 Uttam Kumaran: So just all.
277 00:34:41.550 ⇒ 00:34:43.529 Jakob Kagel: Doubling in the last 12 months.
278 00:34:43.920 ⇒ 00:34:44.590 Jakob Kagel: just.
279 00:34:46.380 ⇒ 00:34:48.539 Uttam Kumaran: This is what I have for last 12 months.
280 00:34:50.290 ⇒ 00:34:50.980 Jakob Kagel: Hmm! Let’s see.
281 00:34:50.989 ⇒ 00:34:52.149 Uttam Kumaran: This seems really hot.
282 00:34:52.150 ⇒ 00:35:02.280 Jakob Kagel: High that numbers. Yeah, that number is too high. I mean that that can’t be right, because that doesn’t tie out with our total number, like our total number, like 15 million in the last 12 months. I know that.
283 00:35:02.280 ⇒ 00:35:08.219 Uttam Kumaran: I think there’s probably some, I think what’s happening is the yeah. Actually, I think I know.
284 00:35:09.260 ⇒ 00:35:10.690 Uttam Kumaran: So oh.
285 00:35:20.260 ⇒ 00:35:22.500 Uttam Kumaran: I think instead, we should do this.
286 00:35:28.420 ⇒ 00:35:35.295 Uttam Kumaran: Okay, let me work on finishing this up, I have to jump to something. But basically, I think we’re the way because we’re because we’re joining.
287 00:35:35.950 ⇒ 00:35:37.189 Uttam Kumaran: we’re joining
288 00:35:37.591 ⇒ 00:35:48.790 Uttam Kumaran: all orders to all order items. Instead, I’m gonna take the total sales just from all order items, because the way it works is all order items. It splits out the sale amount across the end.
289 00:35:48.790 ⇒ 00:35:49.270 Jakob Kagel: Right.
290 00:35:49.270 ⇒ 00:35:56.750 Uttam Kumaran: Order and all orders is probably getting duplicated because there’s multiple order Ids for some
291 00:35:57.490 ⇒ 00:35:59.100 Uttam Kumaran: for some orders, because
292 00:35:59.210 ⇒ 00:36:04.749 Uttam Kumaran: if you have 10 order items in one order, and then you get you’re gonna duplicate all the sales for it.
293 00:36:04.780 ⇒ 00:36:06.659 Uttam Kumaran: When you join all orders.
294 00:36:06.660 ⇒ 00:36:07.110 Jakob Kagel: Yeah, it does.
295 00:36:07.110 ⇒ 00:36:10.550 Uttam Kumaran: So I’m I’m just gonna take the sales from all order items.
296 00:36:11.104 ⇒ 00:36:15.219 Uttam Kumaran: And then I’m gonna ship this into real and I’ll send it to you.
297 00:36:15.220 ⇒ 00:36:25.960 Jakob Kagel: Yeah, I mean sounds great. And yeah, I’m happy to help, however, too. But yeah, exactly. I just looked at all order like joining on all orders. And yeah, I mean, it’s like, roughly, twice as much like for sales.
298 00:36:25.960 ⇒ 00:36:27.223 Uttam Kumaran: Yeah, that’s what’s happening.
299 00:36:27.540 ⇒ 00:36:29.029 Jakob Kagel: Like Summit.
300 00:36:29.150 ⇒ 00:36:32.440 Jakob Kagel: It’s still like, it’s twice as much. So yeah.
301 00:36:32.440 ⇒ 00:36:40.490 Uttam Kumaran: So what are some? So what are so? What are some things we want to try and show that we wanted to show? Basically like side by side, as much as many like
302 00:36:41.350 ⇒ 00:36:45.929 Uttam Kumaran: things as we could show, which is just like overall sales. Average order, value.
303 00:36:46.410 ⇒ 00:37:03.650 Jakob Kagel: Right? I think overall sales. Yeah, I think the the sh shopping, like frequency, is not gonna be much there, because we don’t have repeat customers. So it’s not really that much worth showing. But yeah, the product class mix. And then the Geo like region and state and then.
304 00:37:03.650 ⇒ 00:37:06.049 Uttam Kumaran: I also want to try to show everything as like a percent
305 00:37:06.910 ⇒ 00:37:07.750 Uttam Kumaran: like.
306 00:37:07.750 ⇒ 00:37:08.399 Jakob Kagel: That’s fine!
307 00:37:08.400 ⇒ 00:37:13.530 Uttam Kumaran: Because the the totals, I think that’s actually what they’re. I want to be like, okay, we have a baseline understanding of like
308 00:37:13.700 ⇒ 00:37:17.370 Uttam Kumaran: 20% of like 30% of sales is coming from them, or like
309 00:37:17.400 ⇒ 00:37:20.069 Uttam Kumaran: 60% of sales is coming from this one cohort.
310 00:37:21.140 ⇒ 00:37:23.513 Jakob Kagel: That’s fine. Yeah, I mean, I think.
311 00:37:23.810 ⇒ 00:37:26.000 Uttam Kumaran: Once we get the pivots we can do that pretty.
312 00:37:26.000 ⇒ 00:37:26.710 Jakob Kagel: Young
313 00:37:27.364 ⇒ 00:37:32.525 Jakob Kagel: and yeah, just let me know. I know you have to jump to a call, so I don’t want to keep you. But
314 00:37:32.970 ⇒ 00:37:37.600 Jakob Kagel: yeah, I’ll I’m happy to help like with the real stuff, too. Like, however.
315 00:37:37.930 ⇒ 00:37:38.330 Uttam Kumaran: Yeah.
316 00:37:38.930 ⇒ 00:37:45.300 Uttam Kumaran: Okay, cool. Let me let me try to push this, and then we can just hop back on and like, maybe 30, 40 min.
317 00:37:45.500 ⇒ 00:37:48.419 Jakob Kagel: Okay. Yeah, sounds good. Exactly. Alright. Just let me know.
318 00:37:48.420 ⇒ 00:37:50.244 Uttam Kumaran: Okay. Alright! Alright! Thanks.