Meeting Title: Data-Review-Meeting Date: 2024-06-26 Meeting participants: Nicolas Sucari, Uttam Kumaran, Kim Todaro, Jakob Kagel, Bencohen, Daniel Schonfeld
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1 00:00:55.190 ⇒ 00:00:56.030 Nicolas Sucari: Years. I’m.
2 00:00:56.300 ⇒ 00:00:56.970 Uttam Kumaran: Dev.
3 00:01:01.150 ⇒ 00:01:03.560 Uttam Kumaran: Just finish this up for direct mail.
4 00:01:04.680 ⇒ 00:01:05.300 Daniel Schonfeld: Yeah guys.
5 00:01:05.300 ⇒ 00:01:05.860 Nicolas Sucari: Yeah.
6 00:01:07.230 ⇒ 00:01:08.210 Nicolas Sucari: Hey, Ben.
7 00:01:08.410 ⇒ 00:01:09.070 Uttam Kumaran: In an.
8 00:01:10.190 ⇒ 00:01:11.470 Daniel Schonfeld: Going on. Guys, how are you.
9 00:01:11.660 ⇒ 00:01:13.099 Uttam Kumaran: Hey? Good! How are you?
10 00:01:13.470 ⇒ 00:01:14.610 Daniel Schonfeld: Excellent
11 00:01:14.710 ⇒ 00:01:18.539 Daniel Schonfeld: getting my son ready. He’s going away for 7 weeks, starting tomorrow.
12 00:01:19.590 ⇒ 00:01:20.090 Uttam Kumaran: Wow!
13 00:01:20.090 ⇒ 00:01:21.130 Daniel Schonfeld: Get everything
14 00:01:22.100 ⇒ 00:01:24.320 Uttam Kumaran: 7 weeks is a long time.
15 00:01:24.860 ⇒ 00:01:25.679 Daniel Schonfeld: Oh, yeah.
16 00:01:26.230 ⇒ 00:01:28.020 Daniel Schonfeld: get my summers back now.
17 00:01:28.730 ⇒ 00:01:29.830 Nicolas Sucari: Where is he going?
18 00:01:30.930 ⇒ 00:01:33.729 Daniel Schonfeld: He goes to sleepway campus his second year.
19 00:01:34.330 ⇒ 00:01:37.470 Daniel Schonfeld: And so they he leaves tomorrow. He’s back August
20 00:01:37.970 ⇒ 00:01:39.579 Daniel Schonfeld: 14, th I think.
21 00:01:42.590 ⇒ 00:01:45.399 Daniel Schonfeld: so it’s kind of weird you go from having a kid, and then you’re
22 00:01:45.890 ⇒ 00:01:50.300 Daniel Schonfeld: your life. It revolves around them. And then suddenly they go away to camp, and you’re like. What the hell!
23 00:01:50.920 ⇒ 00:01:52.469 Daniel Schonfeld: You don’t know what to do with yourself.
24 00:01:54.480 ⇒ 00:01:56.549 Nicolas Sucari: That’s when you start playing golf. I think.
25 00:01:57.740 ⇒ 00:01:59.488 Daniel Schonfeld: Exactly what I’m gonna be doing.
26 00:02:02.300 ⇒ 00:02:03.180 Nicolas Sucari: Yeah.
27 00:02:04.110 ⇒ 00:02:05.000 Nicolas Sucari: cool. But.
28 00:02:05.000 ⇒ 00:02:09.750 Uttam Kumaran: So we’ll just jump right into things. Maybe Jacob, you wanna drive.
29 00:02:10.784 ⇒ 00:02:15.280 Jakob Kagel: I was gonna let you do it, but I can pull it up unless you you have it pulled up already.
30 00:02:15.961 ⇒ 00:02:17.768 Uttam Kumaran: Yeah. Maybe you go ahead
31 00:02:18.130 ⇒ 00:02:19.839 Jakob Kagel: Okay, sure. Just give me one.
32 00:02:28.630 ⇒ 00:02:32.420 Nicolas Sucari: In the meantime, Ben, were you able to check the document.
33 00:02:33.450 ⇒ 00:02:37.279 bencohen: Yeah, I was able to open the the notion, but I didn’t have enough time to
34 00:02:37.510 ⇒ 00:02:39.170 bencohen: dive into it a whole lot.
35 00:02:39.770 ⇒ 00:02:40.649 Nicolas Sucari: Okay, to work.
36 00:02:40.650 ⇒ 00:02:44.049 bencohen: I saw how you teared it out with the pros. So that was good.
37 00:02:51.280 ⇒ 00:02:53.140 bencohen: The real dashboard
38 00:02:53.580 ⇒ 00:02:55.050 bencohen: is beautiful.
39 00:02:56.620 ⇒ 00:02:57.350 Uttam Kumaran: Right.
40 00:02:57.490 ⇒ 00:02:59.500 bencohen: I don’t know how else to describe it.
41 00:03:00.040 ⇒ 00:03:01.040 Uttam Kumaran: I agree.
42 00:03:16.470 ⇒ 00:03:18.240 Nicolas Sucari: You’re you’re on mute, Jacob.
43 00:03:18.550 ⇒ 00:03:21.210 Jakob Kagel: Alright, my bad. Sorry. Can you all see my screen?
44 00:03:21.590 ⇒ 00:03:22.280 Jakob Kagel: Yep.
45 00:03:22.530 ⇒ 00:03:25.210 Jakob Kagel: okay, great. Yeah, exactly. So.
46 00:03:25.516 ⇒ 00:03:30.313 Jakob Kagel: I just wanted to say so. We just have the pivot here, but we can just drag in
47 00:03:30.580 ⇒ 00:03:35.030 Uttam Kumaran: Hey, Jacob, if you go to the top right and you go to the bookmarks, there’s a bookmark for.
48 00:03:35.640 ⇒ 00:03:36.910 Jakob Kagel: Oh, they explore!
49 00:03:37.560 ⇒ 00:03:42.519 Uttam Kumaran: If you go to to the top there’s a little bookmark. You could click on the one for this meeting.
50 00:03:45.250 ⇒ 00:03:46.839 Uttam Kumaran: You click on the bookmarks, button.
51 00:03:47.150 ⇒ 00:03:49.239 Jakob Kagel: Is this? Not it? Oh, right here.
52 00:03:51.385 ⇒ 00:03:51.810 Uttam Kumaran: Wouldn’t.
53 00:03:52.320 ⇒ 00:03:55.080 Jakob Kagel: I don’t have it. That’s why I was thinking it would be better if you drove.
54 00:03:55.080 ⇒ 00:03:55.720 Uttam Kumaran: I can share.
55 00:03:55.720 ⇒ 00:03:58.400 Jakob Kagel: Yeah, the one that we built. Yeah.
56 00:03:58.620 ⇒ 00:03:59.270 Uttam Kumaran: Okay.
57 00:04:08.025 ⇒ 00:04:11.109 Uttam Kumaran: do you? Still? Wanna you wanna talk through the different flags? Or I mean, yeah.
58 00:04:11.110 ⇒ 00:04:12.878 Jakob Kagel: For sure, definitely. So
59 00:04:13.610 ⇒ 00:04:20.360 Jakob Kagel: basically like what we did is we broke out into 6 different groups, like on the multiple orders. So
60 00:04:20.450 ⇒ 00:04:39.750 Jakob Kagel: we did one group that has ordered one time, one group that has ordered 2 times, and then one group that has ordered 2 plus times in any 365 day period in the last 2 years. And what we overlap that with here is like the self identified is pool pro question that we have from the checkout
61 00:04:40.097 ⇒ 00:04:55.480 Jakob Kagel: and then we also did the same, but we limited it to order of pumps. So we did the same thing. We did one order, pump order 2 pumps. Order 2 plus pumps in the same in any 365 a day period in the last 2 years.
62 00:04:58.290 ⇒ 00:05:03.059 Jakob Kagel: and yeah, we overlaid the self identified question on top of that.
63 00:05:03.380 ⇒ 00:05:05.749 Jakob Kagel: And what we could see is like
64 00:05:05.980 ⇒ 00:05:19.939 Jakob Kagel: the ratio like of is pool pro true, for the ones that have more orders is higher. So this is like a way that I think that we can sort of get to a more high confidence, like sample of pros.
65 00:05:20.270 ⇒ 00:05:27.019 Jakob Kagel: Versus, like, you know, we had a lot of pro coverage. And that’s something that we talked about in the last meeting was like, Okay.
66 00:05:27.040 ⇒ 00:05:33.020 Jakob Kagel: you know, we’re saying that it’s, you know, 70 plus or some of these sales from pros.
67 00:05:33.370 ⇒ 00:05:45.109 Jakob Kagel: But you are saying that most of the the customer base is like consumers. So the way that you know, we can kind of split on more high confidence, I think, would be to look at like a combination of these order groups
68 00:05:45.120 ⇒ 00:05:50.260 Jakob Kagel: combined with the self identified flag and the emails that we pulled as well.
69 00:05:50.470 ⇒ 00:05:52.360 Jakob Kagel: Did I miss anything there? Utam.
70 00:05:52.640 ⇒ 00:06:00.720 Uttam Kumaran: Yeah. So basically, we looked at we last, how can we have a conversation? We’re talking about the confidence in the flag.
71 00:06:00.850 ⇒ 00:06:08.629 Uttam Kumaran: And we were like, okay, we wanna have some layered approach. We’re talking about emails. We’re talking about the flag. And we’re talking about looking at their order patterns.
72 00:06:08.680 ⇒ 00:06:13.799 Uttam Kumaran: Basically what we found was that the folks that said, Hey, I am a pool pro
73 00:06:13.970 ⇒ 00:06:22.979 Uttam Kumaran: they were more likely to to like have like, for example, if they were to order 2 orders or more.
74 00:06:24.810 ⇒ 00:06:27.590 Uttam Kumaran: they’re more likely to self identify as
75 00:06:27.680 ⇒ 00:06:32.929 Uttam Kumaran: a pool pro meaning we’re trying to both match the behavior with
76 00:06:33.250 ⇒ 00:06:47.290 Uttam Kumaran: the flag as well. Take the more conservative behavior right if people order more than 2 orders in a given year, and that correlates to them. Being a pro given. We have the data from the flag. Then that’s gonna be our criteria.
77 00:06:50.210 ⇒ 00:06:58.440 Uttam Kumaran: that’s basically it. So the options we kind of look through are multiple orders in a 365 day window or multiple pump orders.
78 00:06:58.590 ⇒ 00:07:05.150 Uttam Kumaran: There were only a few people in the last 2 years that ever had more than 2 pump orders
79 00:07:05.400 ⇒ 00:07:07.880 Uttam Kumaran: in a given 3, 65 day period.
80 00:07:08.380 ⇒ 00:07:10.559 Uttam Kumaran: There were several more people that had
81 00:07:10.590 ⇒ 00:07:11.720 Uttam Kumaran: more than
82 00:07:11.900 ⇒ 00:07:13.209 Uttam Kumaran: 2 orders.
83 00:07:13.481 ⇒ 00:07:17.659 Uttam Kumaran: or just to order. So our recommendation is like we just go with anybody that’s had
84 00:07:17.730 ⇒ 00:07:19.110 Uttam Kumaran: 2 orders
85 00:07:19.770 ⇒ 00:07:24.130 Uttam Kumaran: in a 3, 65 day period or more, and consider them
86 00:07:24.730 ⇒ 00:07:26.200 Uttam Kumaran: as professionals.
87 00:07:26.570 ⇒ 00:07:31.279 Uttam Kumaran: In addition to looking at the email domain.
88 00:07:34.530 ⇒ 00:07:35.330 bencohen: Sure.
89 00:07:35.890 ⇒ 00:07:36.750 Daniel Schonfeld: Wasn’t. We learned.
90 00:07:36.750 ⇒ 00:07:41.020 bencohen: That’s fine, but I think we still need to put them through some sort of questionnaire
91 00:07:41.190 ⇒ 00:07:42.470 bencohen: to confirm.
92 00:07:43.800 ⇒ 00:07:45.369 Daniel Schonfeld: Wait. Did this validate
93 00:07:45.460 ⇒ 00:07:51.470 Daniel Schonfeld: the assumptions from last week? Cause I know, Ben, you said, it’s going to be much smaller. Are you saying it’s W. What are we saying from the numbers.
94 00:07:51.470 ⇒ 00:07:52.000 Uttam Kumaran: Smaller.
95 00:07:52.000 ⇒ 00:07:57.870 Jakob Kagel: It would be smaller if we take this higher confidence like, if we take the 2 plus orders or 2 orders
96 00:07:58.280 ⇒ 00:07:59.080 Jakob Kagel: cut.
97 00:08:02.320 ⇒ 00:08:02.840 Jakob Kagel: I mean.
98 00:08:03.241 ⇒ 00:08:07.259 bencohen: I still think we. I still think it goes smaller.
99 00:08:07.750 ⇒ 00:08:08.300 bencohen: A.
100 00:08:08.300 ⇒ 00:08:13.439 Daniel Schonfeld: Can I just wanna Orient ben sorry. Last week we said
101 00:08:14.120 ⇒ 00:08:19.200 Daniel Schonfeld: that, I think was 70 plus were pool pros or something like that. 70% of revenue. I think you said.
102 00:08:19.200 ⇒ 00:08:19.950 Jakob Kagel: Right.
103 00:08:20.060 ⇒ 00:08:20.709 Jakob Kagel: and that was.
104 00:08:21.070 ⇒ 00:08:23.580 Jakob Kagel: It’s on the self identified flag. Right?
105 00:08:23.580 ⇒ 00:08:25.750 Daniel Schonfeld: So that that was on the top left there.
106 00:08:26.420 ⇒ 00:08:29.219 Uttam Kumaran: Yeah. So what you’re seeing here is just the customer counts.
107 00:08:29.220 ⇒ 00:08:29.960 Jakob Kagel: Right, yeah.
108 00:08:30.332 ⇒ 00:08:34.429 Uttam Kumaran: But yeah, it looks like, roughly, yeah, 70% of sales
109 00:08:34.780 ⇒ 00:08:36.070 Uttam Kumaran: in a 2 year period.
110 00:08:36.070 ⇒ 00:08:40.739 Jakob Kagel: Right exactly for. And that’s just is self identified. That’s not with the overlap.
111 00:08:41.400 ⇒ 00:08:43.190 Daniel Schonfeld: Okay. And now you’re saying.
112 00:08:43.260 ⇒ 00:08:46.929 Daniel Schonfeld: once you layered this in, it went from 70%
113 00:08:47.570 ⇒ 00:08:48.540 Daniel Schonfeld: to what.
114 00:08:49.450 ⇒ 00:08:55.190 Uttam Kumaran: To about 20%. So if we were to take, it’ll end up being around 27%.
115 00:08:55.280 ⇒ 00:09:02.650 Uttam Kumaran: If we were to take anyone with 2 or more orders in a 365 day period, and call them professionals.
116 00:09:02.650 ⇒ 00:09:12.490 Jakob Kagel: Right. That also had the self identified. Right? So it’s the combination of the 2 that is basically would give us like higher confidence, like, we could say with more confidence basically than these.
117 00:09:12.490 ⇒ 00:09:15.790 Daniel Schonfeld: Sorry this one, that this, that the fingers over
118 00:09:16.160 ⇒ 00:09:20.520 Daniel Schonfeld: is a combination of self identified, and 2 orders plus.
119 00:09:20.520 ⇒ 00:09:22.240 Uttam Kumaran: Just the 2 orders, plus.
120 00:09:22.240 ⇒ 00:09:23.087 Jakob Kagel: Does the tour.
121 00:09:23.370 ⇒ 00:09:24.400 Daniel Schonfeld: Or 2 things.
122 00:09:24.400 ⇒ 00:09:27.200 Jakob Kagel: The true is the self identified piece. Yeah.
123 00:09:27.200 ⇒ 00:09:27.820 Uttam Kumaran: Yet.
124 00:09:28.790 ⇒ 00:09:36.480 Daniel Schonfeld: Okay. And how do those 2 things correlate? I mean, we were saying that more than like, but there’s a great likelihood that a service Guy came in, or one thing.
125 00:09:36.650 ⇒ 00:09:40.690 Daniel Schonfeld: think back to what I was saying last week was waiting to see what the experience was.
126 00:09:40.940 ⇒ 00:09:46.139 Uttam Kumaran: So here’s here’s an example. Walk through. If I were to say cool, we wanna look at everybody that’s had
127 00:09:46.330 ⇒ 00:09:53.880 Uttam Kumaran: 2 orders in a 3, 65 day period, 80% of those folks identified themselves. Actually, this is
128 00:09:54.180 ⇒ 00:10:02.770 Uttam Kumaran: total sales. If I go to customers, 70% of the people that ordered White in a 3, 5 day period identified themselves as pro
129 00:10:02.840 ⇒ 00:10:06.250 Uttam Kumaran: versus 57 of like the entire
130 00:10:06.870 ⇒ 00:10:07.910 Uttam Kumaran: cohort.
131 00:10:07.910 ⇒ 00:10:08.630 Jakob Kagel: Right.
132 00:10:08.970 ⇒ 00:10:13.320 Uttam Kumaran: If we were to even do more than 2 orders. It’s 74% meaning
133 00:10:13.380 ⇒ 00:10:14.860 Uttam Kumaran: folks that order more
134 00:10:15.350 ⇒ 00:10:17.929 Uttam Kumaran: self identify as pro more
135 00:10:18.110 ⇒ 00:10:26.340 Uttam Kumaran: right. And so that’s kind of a our barometer for figuring out what the behavior that actually indicates the status.
136 00:10:26.380 ⇒ 00:10:29.940 Uttam Kumaran: And then can we can we actually opt for the behavior
137 00:10:30.210 ⇒ 00:10:31.580 Uttam Kumaran: as the flag.
138 00:10:31.740 ⇒ 00:10:34.570 Uttam Kumaran: because I know we’re not as confident in the
139 00:10:35.120 ⇒ 00:10:37.029 Uttam Kumaran: the choosing right? So that
140 00:10:37.100 ⇒ 00:10:38.889 Uttam Kumaran: that was our goal is to
141 00:10:38.900 ⇒ 00:10:41.499 Uttam Kumaran: try to correlate it to the for the right behavior.
142 00:10:41.970 ⇒ 00:10:42.840 Jakob Kagel: Right.
143 00:10:42.840 ⇒ 00:10:50.159 bencohen: I think it’s good. I don’t think you can do with without another step of intervention. I don’t think you can do much better than this.
144 00:10:51.000 ⇒ 00:10:53.990 Uttam Kumaran: Yeah. And I think we’re in the, I think we’re in a better
145 00:10:54.000 ⇒ 00:10:58.199 Uttam Kumaran: like 10 to 20%. I think that’s where it probably ends up
146 00:10:58.430 ⇒ 00:10:59.370 Uttam Kumaran: God.
147 00:10:59.870 ⇒ 00:11:03.289 Uttam Kumaran: and we’re in that range again. The goal is to move
148 00:11:03.300 ⇒ 00:11:07.730 Uttam Kumaran: people into these categories. So this is this is just to get the accurate baseline.
149 00:11:08.360 ⇒ 00:11:09.230 Jakob Kagel: Right.
150 00:11:09.230 ⇒ 00:11:14.300 Uttam Kumaran: You know. And so accuracy is the number. One thing here.
151 00:11:14.440 ⇒ 00:11:15.179 Uttam Kumaran: and it’s.
152 00:11:15.180 ⇒ 00:11:31.890 Jakob Kagel: Pretty clear relationship, too. I mean, it’s like it, cross. If it’s 1. Order 2 orders, 3 orders, or one pump, 2. Pump, 3 pump. It’s the more that you have like the more you self identify. So that’s like really good to see, I think from our end, that it’s exactly across the board that
153 00:11:31.920 ⇒ 00:11:33.989 Jakob Kagel: they’re sort of more likely.
154 00:11:36.180 ⇒ 00:11:38.560 bencohen: Yeah, yeah, it’s migrate.
155 00:11:38.560 ⇒ 00:11:52.969 Jakob Kagel: On the questionnaire. I think it’s great. But that’s also like, now that we have. Like, you know, we can isolate these segments. We can also say, like, look at which ones like the emails like we can reach out to whoever we want to reach out to based on the segments, basically.
156 00:11:52.970 ⇒ 00:11:58.600 bencohen: Yeah, I think I don’t remember. If it was on the last call with all of us, or
157 00:11:58.730 ⇒ 00:12:02.669 bencohen: if I had a follow up with with Tom. I don’t really remember, but I was saying that
158 00:12:02.820 ⇒ 00:12:11.729 bencohen: I was spitballing, but I was like there’s probably 5 buckets that we could fit people into here of service pros. I don’t know who I said this to, but I said it.
159 00:12:11.730 ⇒ 00:12:12.380 Uttam Kumaran: Yeah.
160 00:12:12.770 ⇒ 00:12:14.469 bencohen: There’s this, there’s a
161 00:12:14.960 ⇒ 00:12:27.029 bencohen: someone that thinks that they’re handy, and they do their own thing at home. But they’re not really a professional. There’s a single poll guy that has a a pickup truck, and he he’s he’s he’s got his own route. But he’s alone.
162 00:12:27.310 ⇒ 00:12:29.149 bencohen: and there’s a guy with like
163 00:12:30.110 ⇒ 00:12:37.240 bencohen: 3 people working underneath them, doing a couple of different routes. Then there’s a little company, you know, that has 3 or 4 trucks.
164 00:12:38.010 ⇒ 00:12:40.670 bencohen: Then there’s a much bigger company, and there’s ones that have
165 00:12:40.790 ⇒ 00:12:46.800 bencohen: a bunch of routes and the store. And then there’s like Leslie’s, which we’re not gonna count, really. But
166 00:12:46.900 ⇒ 00:12:56.879 bencohen: what would be good is for us to start to put people into these buckets so like, I don’t actually care. I knew that the 70 was wrong, like wildly wrong. But
167 00:12:57.180 ⇒ 00:12:58.290 bencohen: it’s not.
168 00:12:58.950 ⇒ 00:13:06.450 bencohen: It’s good, because it forces us to figure out where all these people go to, because I think communication to each of these segments
169 00:13:06.930 ⇒ 00:13:11.065 bencohen: deserves to be different because it’s a different. It’s a different community
170 00:13:11.450 ⇒ 00:13:13.040 bencohen: we need to think about.
171 00:13:14.020 ⇒ 00:13:18.369 bencohen: You know how we wanna get these people into the buckets.
172 00:13:18.370 ⇒ 00:13:27.910 Jakob Kagel: I I was suggesting, or like what we discussed, to like as well as like 3 segments of basically like we have high confidence pro, which is like
173 00:13:27.970 ⇒ 00:13:38.580 Jakob Kagel: pros, that self identified and made multiple orders. We have low confidence pro which is, they self identified as pro. But they only have one order, and then we have consumer.
174 00:13:38.969 ⇒ 00:13:57.389 Jakob Kagel: I mean, it’s just an idea, of course, like, you know, but that can kind of give you 2 pro segments where it’s like, okay, these are the people that you know, we more believe maybe are pro. But these are the people that also self identify as pro. But just don’t have the behavior that matches more or less, because they only have one order.
175 00:13:57.930 ⇒ 00:14:08.439 Daniel Schonfeld: Yeah, at the end of the day we care about people ordering multiple things. So if they say that they’re a pool service for on the order multiple product. I could care less if they actually are if they keep ordering product for whatever reason.
176 00:14:08.440 ⇒ 00:14:08.870 Jakob Kagel: Yeah.
177 00:14:08.870 ⇒ 00:14:16.240 Daniel Schonfeld: It means we do, they should fall to the same bucket. It might be the same messaging. It might not matter if we have a bucket of 45 of those people.
178 00:14:16.550 ⇒ 00:14:21.070 Daniel Schonfeld: We’re not going to be able to to do anything with that. We might as well lump them in with the other ones. So
179 00:14:21.150 ⇒ 00:14:26.439 Daniel Schonfeld: I think it’s enough for us to at least start making some marketing and content changes Kim. On your part.
180 00:14:26.650 ⇒ 00:14:28.020 Daniel Schonfeld: We can say, anyone who’s
181 00:14:28.350 ⇒ 00:14:29.879 Daniel Schonfeld: who identifies with this?
182 00:14:29.940 ⇒ 00:14:35.659 Daniel Schonfeld: Well, more, I think about it more importantly, is is a pool pro coming in and buying 2 different pumps.
183 00:14:36.260 ⇒ 00:14:45.829 Daniel Schonfeld: Why would they come back at all. If they’re not getting any kind of wholesale discount, they’re just buying it at the same price as the consumer. There’s no upside for them to buy more.
184 00:14:46.020 ⇒ 00:14:54.650 Daniel Schonfeld: They’re probably just doing it in haste, or there they just needed a quick fix, but there’s no reason for them to come back and keep ordering. If there’s no wholesale program they’re aware of.
185 00:14:54.990 ⇒ 00:15:03.290 Daniel Schonfeld: So I think that’s where our marketing and our content has to step up. And, Kim, we should have a we should all have a meeting about what that pricing is and how they get it.
186 00:15:03.936 ⇒ 00:15:06.200 Daniel Schonfeld: For different product lines.
187 00:15:06.700 ⇒ 00:15:14.910 kim todaro: Is, is there a way we can add, like another question, if they say they are pull pro, can we say, what company do you work for, so we can just like verify that.
188 00:15:15.360 ⇒ 00:15:16.720 kim todaro: I don’t know if that’s like
189 00:15:16.920 ⇒ 00:15:19.830 kim todaro: too cumbersome on the checkout process. But
190 00:15:22.080 ⇒ 00:15:24.280 kim todaro: That would be a little helpful.
191 00:15:25.850 ⇒ 00:15:28.569 Daniel Schonfeld: We could do that, or we could do it. A, you know, follow up email.
192 00:15:28.570 ⇒ 00:15:29.050 Jakob Kagel: Yeah.
193 00:15:29.050 ⇒ 00:15:30.039 Daniel Schonfeld: Sorry. Whoever I just called.
194 00:15:30.040 ⇒ 00:15:31.400 Jakob Kagel: Oh, no, my bad sorry! I didn’t mean to.
195 00:15:31.400 ⇒ 00:15:37.059 Daniel Schonfeld: I was gonna say, we could do a follow up email and say, Hey, great news! You’re a pool service for you qualified for a special discount.
196 00:15:37.330 ⇒ 00:15:48.430 Daniel Schonfeld: We click here to to call whatever to claim it, and then we ask them as 4 or 5 questions. But at least we don’t disrupt the checkout process for everyone.
197 00:15:49.092 ⇒ 00:15:51.420 Daniel Schonfeld: Especially since we know that a good portion of them
198 00:15:51.900 ⇒ 00:15:56.829 Daniel Schonfeld: are potentially not full service. Frozen, saying they are. I just don’t want to get to the point where they’re
199 00:15:56.940 ⇒ 00:16:00.300 Daniel Schonfeld: you know we we all learned this long time ago. Every question you had
200 00:16:00.450 ⇒ 00:16:09.210 Daniel Schonfeld: reduces conversions in one way or another, so I just wonder if there’s might be a simpler way. Post conversion. To ask that question.
201 00:16:09.250 ⇒ 00:16:10.360 Daniel Schonfeld: at least to start.
202 00:16:10.360 ⇒ 00:16:17.209 Uttam Kumaran: That could be in exchange for the like return offer basically, or pricing, or something like that.
203 00:16:18.840 ⇒ 00:16:19.673 kim todaro: Yes, they
204 00:16:20.810 ⇒ 00:16:21.939 kim todaro: sorry. Go ahead.
205 00:16:22.630 ⇒ 00:16:23.120 kim todaro: We talk.
206 00:16:23.120 ⇒ 00:16:34.520 Uttam Kumaran: I see, I said, that, like the exchange of information there would be in like in exchange for the up, like new pricing, or some sort of return offer for a discount, or something like that.
207 00:16:36.620 ⇒ 00:16:37.435 Daniel Schonfeld: Yeah,
208 00:16:38.910 ⇒ 00:16:53.650 Daniel Schonfeld: we could. Yeah, I mean, there’s a lot of ways to skin that cat. I think the the lowest friction is probably just sending up a an auto responder immediately after sign up and just saying, Please tell us who you are, where you are, so we can send you the special discount for future purchases.
209 00:16:53.870 ⇒ 00:16:59.073 Daniel Schonfeld: And even if you want to really incentivize them, we’d say, well, immediately discount this.
210 00:16:59.580 ⇒ 00:17:06.900 Daniel Schonfeld: you know this your purchase for 5% today and give you a discount forward. I think you’ll get a decent amount of people who will
211 00:17:07.380 ⇒ 00:17:08.569 Daniel Schonfeld: respond to that.
212 00:17:12.420 ⇒ 00:17:21.179 Uttam Kumaran: Yeah, the other thing I was. You know, I was talking to Jacob on. We know this is way more occurring on the Saas side. But really, this is just like leaky bucket, which is
213 00:17:21.280 ⇒ 00:17:26.610 Uttam Kumaran: basically the people that just buy once the cack you spent on them is immediately gone
214 00:17:26.730 ⇒ 00:17:39.540 Uttam Kumaran: right. The things about moving people to the recurring category is that they’re gonna continue to buy. And those marketing dollars actually allow them to come back year after year. And so the thing we were thinking about is.
215 00:17:39.620 ⇒ 00:17:43.839 Uttam Kumaran: there’s 2 categories of people we want. We want to entice, keep pros that
216 00:17:43.880 ⇒ 00:17:48.679 Uttam Kumaran: have to make another purchasing decision, and they want to go with us. They’ve already bought from us.
217 00:17:48.760 ⇒ 00:17:54.479 Uttam Kumaran: and the folks that are net new that don’t know about us, or maybe know about us haven’t made an initial purchase.
218 00:17:54.510 ⇒ 00:17:56.859 Uttam Kumaran: Those are going to be way. More
219 00:17:57.140 ⇒ 00:18:02.119 Uttam Kumaran: efficient uses of the same marketing dollar than the folks that are one time in and out.
220 00:18:02.782 ⇒ 00:18:05.269 Uttam Kumaran: And those are the kind of the 2 categories.
221 00:18:05.290 ⇒ 00:18:07.029 Uttam Kumaran: Him that we wanted to
222 00:18:07.120 ⇒ 00:18:10.679 Uttam Kumaran: work with you about was one reengaging the pro so like.
223 00:18:10.940 ⇒ 00:18:15.319 Uttam Kumaran: whatever the criteria we end up with, we can just pass you that email list.
224 00:18:15.420 ⇒ 00:18:19.409 Uttam Kumaran: And that could go right into klaviyo. The second thing is
225 00:18:19.490 ⇒ 00:18:25.570 Uttam Kumaran: we talked previously about kind of scraping and beginning to target these net new service professionals.
226 00:18:25.650 ⇒ 00:18:27.750 Uttam Kumaran: I think we can think through a strategy of
227 00:18:27.770 ⇒ 00:18:31.090 Uttam Kumaran: either, for, like a certain Geo
228 00:18:31.220 ⇒ 00:18:34.609 Uttam Kumaran: or something, where we can start with an initial list.
229 00:18:34.810 ⇒ 00:18:37.379 Uttam Kumaran: and then again load those in
230 00:18:37.520 ⇒ 00:18:42.019 Uttam Kumaran: as bringing net new folks. And the nice thing is, we now have a baseline.
231 00:18:42.200 ⇒ 00:18:46.040 Uttam Kumaran: So the folks that we invite they’re automatically tagged
232 00:18:46.220 ⇒ 00:18:47.000 Uttam Kumaran: as
233 00:18:47.130 ⇒ 00:18:48.400 Uttam Kumaran: professionals.
234 00:18:48.550 ⇒ 00:18:59.879 Uttam Kumaran: and then we can immediately see if the sales and the custom, the distinct customer amount for this segment rise over time. Right? So that’s the benefit of setting this baseline right now.
235 00:19:00.670 ⇒ 00:19:01.250 Daniel Schonfeld: Yeah.
236 00:19:01.520 ⇒ 00:19:02.320 Daniel Schonfeld: that’s right.
237 00:19:02.320 ⇒ 00:19:02.960 kim todaro: Yep.
238 00:19:03.440 ⇒ 00:19:04.950 Uttam Kumaran: And the Aovs are
239 00:19:05.120 ⇒ 00:19:06.845 Uttam Kumaran: like, way better.
240 00:19:08.087 ⇒ 00:19:11.639 Uttam Kumaran: Yeah. And incredibly like, way way, better.
241 00:19:12.450 ⇒ 00:19:13.190 Uttam Kumaran: So.
242 00:19:15.060 ⇒ 00:19:19.900 Daniel Schonfeld: Yup, and it’s a th. These people keep their routes. So
243 00:19:20.350 ⇒ 00:19:26.961 Daniel Schonfeld: it doesn’t typically change. So you’re talking about, you know, lifetime value of many, many, many years.
244 00:19:29.640 ⇒ 00:19:33.839 Daniel Schonfeld: you know, across the route some of these service guys have 50 60 routes
245 00:19:34.170 ⇒ 00:19:35.030 Daniel Schonfeld: per
246 00:19:36.464 ⇒ 00:19:41.479 Daniel Schonfeld: so as we reach more and more, you know that it’s like a a compounds
247 00:19:42.120 ⇒ 00:19:47.750 Daniel Schonfeld: that aob will compound the the lifetime value will will compound so.
248 00:19:48.290 ⇒ 00:19:50.620 Daniel Schonfeld: and especially as we add more products.
249 00:19:52.260 ⇒ 00:19:55.790 Daniel Schonfeld: it’s a ex. The point is extremely valuable. Customer.
250 00:19:56.970 ⇒ 00:19:57.690 Daniel Schonfeld: Yeah.
251 00:20:00.230 ⇒ 00:20:03.236 Daniel Schonfeld: so we could even do something really special for them. Kim.
252 00:20:03.680 ⇒ 00:20:07.409 Daniel Schonfeld: you know, we. Then I know we talked about sending like a welcome package and
253 00:20:08.340 ⇒ 00:20:14.459 Daniel Schonfeld: a maintenance kit all that it might in time be worth sending to that high value group immediately
254 00:20:14.510 ⇒ 00:20:17.060 Daniel Schonfeld: sending them a welcome package, with like a box with.
255 00:20:17.060 ⇒ 00:20:17.540 bencohen: Yeah.
256 00:20:17.540 ⇒ 00:20:23.934 Daniel Schonfeld: Stuff in it and their own special code. We had talked about a card that they get with their own special discount code on it.
257 00:20:24.290 ⇒ 00:20:27.510 Daniel Schonfeld: We’ll have to have another meeting and figure out how we actually execute that. But
258 00:20:27.930 ⇒ 00:20:30.220 Daniel Schonfeld: we want to treat those people like gold, because.
259 00:20:30.510 ⇒ 00:20:35.790 Daniel Schonfeld: as we know, a consumer will buy a pump once every 7 years. These guys can buy them once every 7 days. So.
260 00:20:35.790 ⇒ 00:20:36.130 bencohen: Yeah.
261 00:20:36.130 ⇒ 00:20:37.310 Daniel Schonfeld: Treat them really well.
262 00:20:37.740 ⇒ 00:20:40.896 bencohen: And the other thing we can try to figure out is is
263 00:20:41.310 ⇒ 00:20:46.389 bencohen: how to identify someone that looks like they might become a customer like that, you know, like
264 00:20:46.550 ⇒ 00:20:49.889 bencohen: they’re showing promise they’re showing potential. And
265 00:20:50.180 ⇒ 00:20:53.100 bencohen: maybe that’s when we they get, you know.
266 00:20:53.100 ⇒ 00:20:53.990 Uttam Kumaran: Exactly.
267 00:20:53.990 ⇒ 00:20:58.039 bencohen: A package. And you know, a phone call or whatever we need cause we need to get.
268 00:20:58.700 ⇒ 00:21:01.710 bencohen: We need to validate who can get there
269 00:21:02.230 ⇒ 00:21:06.180 bencohen: and keep them there and we gotta do whatever we can to do it. So
270 00:21:06.270 ⇒ 00:21:07.679 bencohen: yeah, this is great.
271 00:21:08.020 ⇒ 00:21:09.480 Uttam Kumaran: That I think that’ll be our
272 00:21:09.530 ⇒ 00:21:10.679 Uttam Kumaran: sorry. Go ahead, Dan.
273 00:21:11.010 ⇒ 00:21:24.499 Daniel Schonfeld: I was. Gonna say, I think we can pick an area like, let’s say, the next guy who identifies as a service guy that comes in these regions at least, like Northern Florida, Southern Florida, where we know they’re going to be buying and servicing pools year round.
274 00:21:25.220 ⇒ 00:21:35.149 Daniel Schonfeld: Maybe we start there and send them welcome packages and see what that experience was and the ordering process, and we interview them. But then you gotta start with those highest value
275 00:21:35.170 ⇒ 00:21:38.029 Daniel Schonfeld: the assumption, the assumed highest value regions.
276 00:21:38.230 ⇒ 00:21:38.790 Daniel Schonfeld: Yeah,
277 00:21:39.450 ⇒ 00:21:43.860 Daniel Schonfeld: you know, you can target that way. Sorry Tom didn’t mean to interrupt it.
278 00:21:43.860 ⇒ 00:21:48.670 Uttam Kumaran: No, I think that’s exactly it. Like we basically wanna disqualify the folks that
279 00:21:48.720 ⇒ 00:21:59.489 Uttam Kumaran: screen that their consumer. There is some folks in the middle that it’s like, how do we get them to these like star customers? And the star customers is keeping
280 00:22:00.850 ⇒ 00:22:05.279 Uttam Kumaran: that middle segment is gonna be the next thing for us to kind of isolate.
281 00:22:05.590 ⇒ 00:22:20.280 Uttam Kumaran: But I think, even just again having like reactivating the folks that we know are for sure or like the thing I told Jacob, is the folks that are professionals they are ordering from somewhere else. If if they’re not ordering from us right.
282 00:22:20.280 ⇒ 00:22:21.060 Jakob Kagel: Definitely.
283 00:22:21.320 ⇒ 00:22:26.100 Uttam Kumaran: So that’s the thing is like that. There’s their spend is already happening somewhere.
284 00:22:26.120 ⇒ 00:22:28.429 Uttam Kumaran: So it’s just not happening with us.
285 00:22:28.750 ⇒ 00:22:31.780 Uttam Kumaran: probably for the most part. So the thing is, how do we move that
286 00:22:32.350 ⇒ 00:22:37.709 Uttam Kumaran: here for the folks that already exist in our database? And then it’s everyone that’s net new.
287 00:22:37.970 ⇒ 00:22:39.669 Uttam Kumaran: I think that’s the low hanging.
288 00:22:40.480 ⇒ 00:22:55.720 Jakob Kagel: Right. And once we have these flags, I mean, we can really easily just isolate, like the customer emails and probably cross, check and see? Like which ones have we targeted? How often have we targeted them like which ones have we not, you know, reached out to at all? And yeah.
289 00:22:59.950 ⇒ 00:23:01.639 Daniel Schonfeld: What do we do from here, Tim?
290 00:23:01.640 ⇒ 00:23:03.340 Jakob Kagel: Yeah, I think, sorry. Go ahead.
291 00:23:03.340 ⇒ 00:23:04.532 Daniel Schonfeld: But go ahead, Jacob.
292 00:23:04.830 ⇒ 00:23:25.639 Jakob Kagel: Oh, I was just gonna say, I mean, I think sort of the thing like the goal that we wanna have out of this meeting, too, is like to maybe just sort of like. Finally, like align on what like the 2 or 3 buckets like should be like what the logic should be because we like, I said. We do have like, you know, we have one order, 2 orders, and then like 2 plus orders, and then we have
293 00:23:25.930 ⇒ 00:23:31.440 Jakob Kagel: one pump, 2 pump and 2 plus pumps. Right? So
294 00:23:31.690 ⇒ 00:23:42.070 Jakob Kagel: I think I mean, you can correct me if if if if you disagree here, but I think sort of like what we thought was like. Probably best was like 2 or more orders.
295 00:23:42.547 ⇒ 00:23:48.760 Jakob Kagel: in combination with the self identified in the emails. But we just did want to like sort of
296 00:23:49.210 ⇒ 00:23:51.599 Jakob Kagel: open that up and and and sort of
297 00:23:51.710 ⇒ 00:23:53.949 Jakob Kagel: see if we can get alignment there. I think.
298 00:23:54.130 ⇒ 00:23:55.489 bencohen: I think that’s good.
299 00:23:55.490 ⇒ 00:23:55.900 Uttam Kumaran: Okay.
300 00:23:56.265 ⇒ 00:24:00.649 bencohen: I think we can go with that. I do want more info.
301 00:24:00.790 ⇒ 00:24:02.960 bencohen: So I will email these people
302 00:24:03.254 ⇒ 00:24:08.740 bencohen: but for the sake of your question. I think that would be the correct list to take from here.
303 00:24:09.130 ⇒ 00:24:33.159 Jakob Kagel: Okay? And then, yeah, would we want to do like what? I said, basically, which is like, Yeah, we’ll take the 2 2 or more orders and say, that’s like high confidence, and I’m just saying high confidence. But we could also just call it something else. We could call it, you know, pool pro plus 2 or whatever. But high confidence pros, and then we have the people that also self identify as pro, but only ordered once as a separate segment, and then we have consumer as the 3rd segment.
304 00:24:33.570 ⇒ 00:24:50.100 Jakob Kagel: I think it makes sense. As these people did self identify as pro. I mean, you know, for whatever that’s worth. I mean, we know that’s, you know, the coverage is kind of high for self identified. But I think it’s maybe still worth having it as like a 3rd segment.
305 00:24:51.460 ⇒ 00:24:54.370 Uttam Kumaran: Let’s do that. And that’s gonna be the one we continue to debate. Basically.
306 00:24:54.370 ⇒ 00:24:56.730 Jakob Kagel: Right? They can. Okay.
307 00:24:57.470 ⇒ 00:24:58.517 Uttam Kumaran: So let’s do that.
308 00:25:00.250 ⇒ 00:25:01.319 Jakob Kagel: That’s great. Yeah.
309 00:25:05.170 ⇒ 00:25:19.110 Jakob Kagel: I think it’s fine, of course, like, for most of our analysis will probably just focus on high confidence, at least in the short term, but I think it’s great, you know, if we have all 3 kind of split out in the same flag where we can slice them easily.
310 00:25:19.760 ⇒ 00:25:23.690 Uttam Kumaran: And then, Kim, maybe we can just talk on slack in here.
311 00:25:24.240 ⇒ 00:25:29.179 Uttam Kumaran: We can just bring in their email. And then you can export. Basically, we can load all this in
312 00:25:29.614 ⇒ 00:25:30.990 Uttam Kumaran: we will. We’re gonna
313 00:25:31.290 ⇒ 00:25:42.148 Uttam Kumaran: clean this up. This is like way too many flags to have just consolidated to the 3 segments we talked about. Well, like we can turn this back on and come back and do this sort of analysis.
314 00:25:43.430 ⇒ 00:25:50.750 Uttam Kumaran: but I’m glad we reached that kind of those 3. So we’ll so I’ll share you with you, Kim, where to get these emails. And then
315 00:25:50.960 ⇒ 00:25:55.999 Uttam Kumaran: I also think maybe we can agree on, like to do north Florida, or like, start with
316 00:25:56.160 ⇒ 00:26:02.080 Uttam Kumaran: an area where we want to target. Geo’s that way, I can try to run a scrape basically
317 00:26:02.515 ⇒ 00:26:06.689 Uttam Kumaran: to get us a list that we, I think, basically, we want to talk about a plan
318 00:26:06.850 ⇒ 00:26:09.280 Uttam Kumaran: for the re-engagement and a plan for
319 00:26:09.710 ⇒ 00:26:10.909 Uttam Kumaran: new folks.
320 00:26:11.530 ⇒ 00:26:12.550 Uttam Kumaran: Yeah. But.
321 00:26:12.550 ⇒ 00:26:16.070 kim todaro: That makes sense to me. When you say scrap, you mean new folks right.
322 00:26:16.420 ⇒ 00:26:17.233 Uttam Kumaran: Exactly. Yeah.
323 00:26:18.150 ⇒ 00:26:23.180 kim todaro: For sure. I mean, legally. And maybe we could just get around this up to Dan and Ben.
324 00:26:23.390 ⇒ 00:26:25.810 kim todaro: We can’t email them if they haven’t subscribed.
325 00:26:26.580 ⇒ 00:26:28.339 kim todaro: We can send them direct mail
326 00:26:29.528 ⇒ 00:26:30.640 kim todaro: if we have their.
327 00:26:30.640 ⇒ 00:26:32.030 Daniel Schonfeld: Yeah, or email, do we.
328 00:26:33.310 ⇒ 00:26:35.119 kim todaro: No, I’m saying, for the the new people
329 00:26:35.420 ⇒ 00:26:37.829 kim todaro: where we would script their information and
330 00:26:38.120 ⇒ 00:26:39.600 kim todaro: get lists of
331 00:26:39.750 ⇒ 00:26:42.659 kim todaro: pull pros in like North Florida. Right? You tell them.
332 00:26:42.950 ⇒ 00:26:46.620 Uttam Kumaran: Yeah, that’s exactly what we would do. Basically, look at the Google Maps listings.
333 00:26:48.250 ⇒ 00:26:49.040 kim todaro: Yeah.
334 00:26:49.040 ⇒ 00:26:52.530 Uttam Kumaran: If we can, we can run it. We I mean, we can run that through direct mail first.st
335 00:26:52.830 ⇒ 00:26:56.599 Uttam Kumaran: The other thing that I’ll show at the end of the meeting we have all the direct mail stuff hooked up.
336 00:26:58.290 ⇒ 00:26:59.250 Uttam Kumaran: though.
337 00:26:59.250 ⇒ 00:26:59.900 kim todaro: Yeah.
338 00:27:00.590 ⇒ 00:27:05.199 kim todaro: And we can probably offer them a discount, assuming that they would buy more than you know. Just one product.
339 00:27:05.654 ⇒ 00:27:08.570 kim todaro: I think if Ben and Dan are like that.
340 00:27:08.570 ⇒ 00:27:19.860 Daniel Schonfeld: The goal would be to get them into the funnel. So get their email. So let’s discount for email somehow. You know, all you need is an email to sign up. We’ll come up with what that marketing is. But that direct mail piece should be all about getting their email.
341 00:27:20.130 ⇒ 00:27:20.840 Jakob Kagel: Right.
342 00:27:20.990 ⇒ 00:27:25.469 Daniel Schonfeld: Not purchasing cause. We just want to get as many people into the funnel as possible. We’ll eventually get them.
343 00:27:26.530 ⇒ 00:27:27.230 kim todaro: Okay.
344 00:27:34.170 ⇒ 00:27:35.632 Uttam Kumaran: Okay, so let’s
345 00:27:38.070 ⇒ 00:27:40.879 Uttam Kumaran: Let’s act on that. And then maybe we come back next week
346 00:27:42.910 ⇒ 00:27:44.109 Uttam Kumaran: works for me.
347 00:27:46.370 ⇒ 00:27:48.210 Uttam Kumaran: Kim, if you have
348 00:27:48.820 ⇒ 00:27:52.405 Uttam Kumaran: few more minutes. I’ll just walk you through how to get the direct mail stuff, but.
349 00:27:52.630 ⇒ 00:27:55.610 kim todaro: Yeah, I do. I just signed up for real, too.
350 00:27:55.610 ⇒ 00:27:56.290 Uttam Kumaran: Cool
351 00:27:57.670 ⇒ 00:28:01.789 Uttam Kumaran: and then feel free to stay on. I’ll I’m just gonna walk through how to get get this
352 00:28:04.140 ⇒ 00:28:09.949 Uttam Kumaran: So we have 2 models. We have one for direct mail, where you can see all the orders associated.
353 00:28:10.627 ⇒ 00:28:12.570 Uttam Kumaran: So, for example.
354 00:28:12.800 ⇒ 00:28:14.680 Uttam Kumaran: I’m sorry I’m just gonna make this a little bit
355 00:28:14.790 ⇒ 00:28:23.430 Uttam Kumaran: smaller. You can see like these are the campaigns that we ran. This column. Here is this which is total revenue.
356 00:28:23.630 ⇒ 00:28:24.999 kim todaro: Wait! Are you sharing your screen.
357 00:28:26.660 ⇒ 00:28:28.330 Jakob Kagel: I can see it. I don’t know.
358 00:28:28.330 ⇒ 00:28:29.120 kim todaro: Oh, weird!
359 00:28:29.120 ⇒ 00:28:29.830 Nicolas Sucari: Yeah.
360 00:28:30.676 ⇒ 00:28:31.419 kim todaro: I won’t see it.
361 00:28:32.120 ⇒ 00:28:33.520 kim todaro: I see, too.
362 00:28:33.930 ⇒ 00:28:36.330 Uttam Kumaran: Yeah, I’ll stop and reshare maybe glitch.
363 00:28:36.330 ⇒ 00:28:37.220 kim todaro: Hmm.
364 00:28:37.220 ⇒ 00:28:40.098 bencohen: Now I see us all much bigger, which I don’t want.
365 00:28:40.360 ⇒ 00:28:41.170 kim todaro: The end.
366 00:28:41.170 ⇒ 00:28:42.020 Uttam Kumaran: Okay, great.
367 00:28:42.757 ⇒ 00:28:46.580 Uttam Kumaran: So these are the the campaign. Just take like
368 00:28:47.390 ⇒ 00:28:59.440 Uttam Kumaran: this campaign, for example, you can see all of the orders associated with this campaign when those were made so we basically joined the direct mail.
369 00:28:59.600 ⇒ 00:29:02.620 Uttam Kumaran: like the conversions that postpile gave us
370 00:29:02.670 ⇒ 00:29:15.059 Uttam Kumaran: directly to the order. Of course, like order is at can have multiple items in them. So this is just going to show the individual orders associated. We also have another one that shows, like the items.
371 00:29:15.060 ⇒ 00:29:15.509 kim todaro: Oh, cool!
372 00:29:16.210 ⇒ 00:29:21.659 Uttam Kumaran: so you can see the click of the campaign. And then also see basically what products were bought. So.
373 00:29:21.660 ⇒ 00:29:22.370 kim todaro: Yeah. Aye.
374 00:29:22.370 ⇒ 00:29:24.040 Uttam Kumaran: An example that shows
375 00:29:24.610 ⇒ 00:29:29.039 Uttam Kumaran: campaign. There was 320 orders from this ab checkout campaign.
376 00:29:29.120 ⇒ 00:29:31.439 Uttam Kumaran: 100 and 15 of them were heat pumps
377 00:29:32.080 ⇒ 00:29:36.260 Uttam Kumaran: like you. Can you really easily just say, like, I want to look at this the last 4 weeks?
378 00:29:37.670 ⇒ 00:29:40.899 Uttam Kumaran: for the AV checkout campaign. What ran so.
379 00:29:41.240 ⇒ 00:29:50.190 kim todaro: Can you look at the there’s 1 for black and decker filters. I just wanna see if they actually bought filters cause that did have a good, a decent return on, add, spend.
380 00:29:50.740 ⇒ 00:29:51.470 Uttam Kumaran: October.
381 00:29:51.470 ⇒ 00:29:53.779 kim todaro: Looked at was just abandoned cart, so.
382 00:29:53.780 ⇒ 00:29:58.219 Uttam Kumaran: Do you know what? Oh, do you do you know what the campaign name is, or just any for any filters.
383 00:29:58.486 ⇒ 00:30:04.879 kim todaro: No, it was a campaign name, and it was black and Decker filters, but for some reason I’m not seeing it on that list.
384 00:30:05.380 ⇒ 00:30:06.347 Uttam Kumaran: Yeah, I see.
385 00:30:06.670 ⇒ 00:30:10.109 Jakob Kagel: Top 5 right now. I think you can. Probably, if you just add the filter.
386 00:30:10.110 ⇒ 00:30:10.780 kim todaro: Well, that makes sense.
387 00:30:10.780 ⇒ 00:30:11.510 Jakob Kagel: Signed it.
388 00:30:12.810 ⇒ 00:30:15.739 Uttam Kumaran: I think this is all we have for the last 12 months.
389 00:30:16.220 ⇒ 00:30:17.590 kim todaro: Hmm, okay.
390 00:30:18.000 ⇒ 00:30:20.391 Uttam Kumaran: But let send me the send me the
391 00:30:20.820 ⇒ 00:30:22.190 Uttam Kumaran: campaign name
392 00:30:23.980 ⇒ 00:30:25.540 Uttam Kumaran: and we can look. I mean.
393 00:30:25.540 ⇒ 00:30:27.700 Jakob Kagel: Definitely validate that and make sure that
394 00:30:28.440 ⇒ 00:30:29.589 Jakob Kagel: we have that.
395 00:30:32.710 ⇒ 00:30:34.539 Uttam Kumaran: I could look to see if any
396 00:30:35.150 ⇒ 00:30:37.239 Uttam Kumaran: the filter products were there.
397 00:30:41.480 ⇒ 00:30:46.640 Uttam Kumaran: Yeah, if you send me the campaign name, or I can look to see actually where it is, cause I don’t see.
398 00:30:46.960 ⇒ 00:30:47.850 kim todaro: Yeah, I, just.
399 00:30:47.850 ⇒ 00:30:50.219 Uttam Kumaran: There being conversions. So either we
400 00:30:51.040 ⇒ 00:30:54.850 Uttam Kumaran: we don’t have the conversions from them. Or
401 00:30:55.250 ⇒ 00:30:56.140 Uttam Kumaran: yeah.
402 00:30:56.140 ⇒ 00:30:57.359 Daniel Schonfeld: Whom is data from.
403 00:30:58.442 ⇒ 00:31:01.680 Uttam Kumaran: All of this data is coming from
404 00:31:02.896 ⇒ 00:31:06.710 Uttam Kumaran: postpilot post pilot is our direct mail vendor.
405 00:31:07.680 ⇒ 00:31:10.539 Daniel Schonfeld: Yeah, are you? Is it an Api? Or they did it like a data dump.
406 00:31:10.770 ⇒ 00:31:14.319 Uttam Kumaran: It. Is a data dump that we’re processing.
407 00:31:15.350 ⇒ 00:31:15.880 Daniel Schonfeld: Okay.
408 00:31:15.880 ⇒ 00:31:21.910 Uttam Kumaran: Basically like they’re, they just have a Ui, and then we’re we’re working directly with their team. They don’t have an Api.
409 00:31:21.970 ⇒ 00:31:23.719 Uttam Kumaran: so we’re getting
410 00:31:23.860 ⇒ 00:31:25.579 Uttam Kumaran: daily updates of
411 00:31:26.240 ⇒ 00:31:27.930 Uttam Kumaran: the campaigns and conversions.
412 00:31:28.060 ⇒ 00:31:28.810 Uttam Kumaran: I guess, became like.
413 00:31:28.810 ⇒ 00:31:29.739 Daniel Schonfeld: And repeat it.
414 00:31:30.807 ⇒ 00:31:32.660 Uttam Kumaran: We’re putting it.
415 00:31:33.041 ⇒ 00:31:37.060 Uttam Kumaran: No, no, we’re getting it as an email. And then we’re dumping it into the system.
416 00:31:37.745 ⇒ 00:31:38.370 Uttam Kumaran: Yeah.
417 00:31:39.520 ⇒ 00:31:42.059 Daniel Schonfeld: What is real? Quick. What? What is this system.
418 00:31:42.770 ⇒ 00:31:44.539 Uttam Kumaran: This is real.
419 00:31:44.810 ⇒ 00:31:46.740 Uttam Kumaran: Rhyl is
420 00:31:47.530 ⇒ 00:31:51.209 Uttam Kumaran: real is a software that, like I kind of learned about in the last few months. And I’ve been
421 00:31:51.270 ⇒ 00:31:53.159 Uttam Kumaran: playing around with our data. And it’s
422 00:31:53.530 ⇒ 00:31:55.730 Uttam Kumaran: it’s like, Been way better than my dash.
423 00:31:56.253 ⇒ 00:32:01.329 Uttam Kumaran: Basically been using it for all the exploration related questions we had
424 00:32:01.620 ⇒ 00:32:07.790 Uttam Kumaran: to give you like an example. This is like, this is our table. For, like all orders.
425 00:32:09.580 ⇒ 00:32:12.540 Uttam Kumaran: and basically, it’s really easy for us to see
426 00:32:12.830 ⇒ 00:32:17.929 Uttam Kumaran: like sales on a given day, orders on a given day where things are coming from.
427 00:32:19.930 ⇒ 00:32:23.269 Uttam Kumaran: and it’s really for like operational use cases. It’s really nice.
428 00:32:23.360 ⇒ 00:32:29.219 Uttam Kumaran: The visualizations are a bit limited in that. This is mostly it can also do pivot tables.
429 00:32:29.430 ⇒ 00:32:33.790 Uttam Kumaran: But it’s really quick, and you can easily do things like percent of totals
430 00:32:34.230 ⇒ 00:32:36.460 Uttam Kumaran: really quick to filter on things.
431 00:32:37.050 ⇒ 00:32:37.320 Uttam Kumaran: Yeah.
432 00:32:37.320 ⇒ 00:32:38.529 Daniel Schonfeld: Much are we paying for it?
433 00:32:38.860 ⇒ 00:32:42.339 Uttam Kumaran: We are actually not paying for this right now. But
434 00:32:42.530 ⇒ 00:32:45.890 Uttam Kumaran: I’ve just been testing it. And I I know the
435 00:32:45.900 ⇒ 00:32:49.959 Uttam Kumaran: I know the the guys at the company. So I was basically like, we’re gonna test it for a bit.
436 00:32:50.517 ⇒ 00:32:54.670 Uttam Kumaran: We currently pay 600 a month for for light dash.
437 00:32:54.760 ⇒ 00:32:59.280 Uttam Kumaran: I think this would come in. This would come in, and probably like 200 or 300 a month.
438 00:32:59.860 ⇒ 00:33:00.380 Uttam Kumaran: That’s like.
439 00:33:00.380 ⇒ 00:33:03.320 Daniel Schonfeld: And we. We would use this in conjunction with light dash.
440 00:33:04.090 ⇒ 00:33:09.278 Uttam Kumaran: My pitch for would be to replace light dash with this.
441 00:33:09.750 ⇒ 00:33:11.420 Jakob Kagel: It’s, in my opinion. Yeah.
442 00:33:11.420 ⇒ 00:33:20.630 Uttam Kumaran: To give you a reasoning on on the the development time it takes is like way faster. All of this is actually living as code.
443 00:33:21.136 ⇒ 00:33:32.930 Uttam Kumaran: So for us to develop these dashboards, it’s a way quicker, light dash. Of course, we dealt with a lot of drag and drop. The problem with this is that the visualizations options are limited.
444 00:33:33.120 ⇒ 00:33:37.469 Uttam Kumaran: in that I don’t think this is great to like. Run to like
445 00:33:37.900 ⇒ 00:33:52.409 Uttam Kumaran: have like a full story about something. But if you’re like, for example, if you’re if you’re logging in, we’re like cool. I want to look at the last 4 weeks on shopify, and I want to compare it by time to
446 00:33:53.106 ⇒ 00:33:54.480 Uttam Kumaran: last year.
447 00:33:55.071 ⇒ 00:33:57.060 Uttam Kumaran: You could do that that quick.
448 00:33:57.790 ⇒ 00:33:58.390 Daniel Schonfeld: Oh, yeah.
449 00:33:59.220 ⇒ 00:34:03.320 Uttam Kumaran: So like the gray line is last year. Same time. This is right. Now.
450 00:34:03.330 ⇒ 00:34:05.490 Uttam Kumaran: Everything gets filtered by that.
451 00:34:05.600 ⇒ 00:34:09.219 Uttam Kumaran: And you could basically look at changes for every single dimension
452 00:34:09.620 ⇒ 00:34:16.510 Uttam Kumaran: that that what I did just right there would have taken me an hour to have done elsewhere.
453 00:34:17.199 ⇒ 00:34:21.479 Uttam Kumaran: And it’s built on this new technology that came out in the past
454 00:34:21.659 ⇒ 00:34:22.850 Uttam Kumaran: year and a half
455 00:34:23.290 ⇒ 00:34:28.149 Uttam Kumaran: kind of like a in browser database technology that a lot of new data companies are building on
456 00:34:28.219 ⇒ 00:34:32.679 Uttam Kumaran: where you can do these advanced like how fast it was and how much you computed.
457 00:34:32.850 ⇒ 00:34:42.019 Uttam Kumaran: It’s based on this new technology. That’s like allowing for that. So there’s a couple of new startups that basically took that software and are building on top of it. So.
458 00:34:42.520 ⇒ 00:34:46.459 Daniel Schonfeld: Okay, as long as we can extrapolate the same data or extract the same data.
459 00:34:47.909 ⇒ 00:34:51.130 Daniel Schonfeld: Then we’re we’re fine, especially with all the shipping stuff you guys worked on.
460 00:34:51.130 ⇒ 00:34:52.440 Jakob Kagel: It’s way. Better for sure.
461 00:34:52.440 ⇒ 00:34:56.980 Uttam Kumaran: Yeah. So for the on the shipment side, this is like what we’ve been what I’m gonna use
462 00:34:57.580 ⇒ 00:34:59.219 Uttam Kumaran: for the conversation with
463 00:34:59.660 ⇒ 00:35:01.120 Uttam Kumaran: with Michael on
464 00:35:01.290 ⇒ 00:35:04.609 Uttam Kumaran: Friday. Then, basically, I’m just gonna pull this up and
465 00:35:04.870 ⇒ 00:35:10.150 Uttam Kumaran: like what Jacob’s also working on the like, where do we ship from analysis? And we’re basically gonna.
466 00:35:10.150 ⇒ 00:35:13.010 bencohen: Yeah. Texas? Yeah, yeah, we’re in Texas is the spot.
467 00:35:13.010 ⇒ 00:35:13.630 Uttam Kumaran: Dude
468 00:35:14.060 ⇒ 00:35:18.540 Uttam Kumaran: to basically show like, Hey, here’s what we’re working with. Here’s what our average prices are.
469 00:35:19.590 ⇒ 00:35:20.510 Uttam Kumaran: so yeah.
470 00:35:22.470 ⇒ 00:35:26.539 Daniel Schonfeld: What are the numbers on the right like? It’s a Japank, 9.5 K. What does that represent?
471 00:35:27.060 ⇒ 00:35:28.369 Uttam Kumaran: That. So.
472 00:35:28.370 ⇒ 00:35:29.269 Daniel Schonfeld: Bottom, left.
473 00:35:29.770 ⇒ 00:35:36.010 Uttam Kumaran: Yeah. So this is just for the last 3 months. These are the number of order, the number of shipments from y’all bank.
474 00:35:39.000 ⇒ 00:35:42.009 Daniel Schonfeld: Okay? And does. Do you do? We have cost in there.
475 00:35:44.420 ⇒ 00:35:48.352 Uttam Kumaran: we do have costs in there. Yeah. So if I do like
476 00:35:49.240 ⇒ 00:35:50.670 Uttam Kumaran: total shipping amount.
477 00:35:50.730 ⇒ 00:35:51.930 Uttam Kumaran: you could see
478 00:35:52.880 ⇒ 00:35:54.460 Uttam Kumaran: the costs associated
479 00:35:55.325 ⇒ 00:35:57.290 Uttam Kumaran: so I could just filter to these 2.
480 00:35:59.200 ⇒ 00:36:00.610 Daniel Schonfeld: Okay, so this is pretty good.
481 00:36:01.070 ⇒ 00:36:01.950 bencohen: Yeah.
482 00:36:04.070 ⇒ 00:36:06.739 Daniel Schonfeld: Alright! Alright! We’ll say that for another another meeting. But.
483 00:36:06.740 ⇒ 00:36:22.019 bencohen: Yeah, I didn’t want to bog you down with it. We, Tom and I looked at it for the 1st time together, like 3 months ago, and I was like, this is incredible. But it was all uncertain, because it was such a new company. So we just said, We’ll explore it in parallel. But we’re not gonna
484 00:36:22.520 ⇒ 00:36:24.310 bencohen: make a lot of noise about this.
485 00:36:24.630 ⇒ 00:36:29.950 Daniel Schonfeld: Okay, yeah. Keep keep exploring, going back and forth, making sure data matches, too.
486 00:36:30.110 ⇒ 00:36:32.980 bencohen: Yeah, no, for me it’s much. It’s more, it’s more
487 00:36:33.370 ⇒ 00:36:35.470 bencohen: great. It seems more like.
488 00:36:36.550 ⇒ 00:36:38.219 bencohen: what was that class I took?
489 00:36:38.820 ⇒ 00:36:45.540 bencohen: Indiana is like K. 2 0. 1. It was like business and computers, and it was all about Microsoft access. I hated it, but
490 00:36:45.780 ⇒ 00:36:48.339 bencohen: it was the 1st deja vu I got with this just.
491 00:36:48.340 ⇒ 00:36:52.060 Uttam Kumaran: It just seems like you if you’re in like some sort of like control room.
492 00:36:52.210 ⇒ 00:36:54.120 Uttam Kumaran: And you’re like seeing everything.
493 00:36:54.140 ⇒ 00:36:56.790 Uttam Kumaran: And frankly, this is like how I think
494 00:36:56.850 ⇒ 00:36:58.400 Uttam Kumaran: I see
495 00:36:58.470 ⇒ 00:37:01.970 Uttam Kumaran: all this data. And I’m like, Okay, I want, I have like 5 questions.
496 00:37:01.970 ⇒ 00:37:02.900 Jakob Kagel: Right.
497 00:37:02.900 ⇒ 00:37:09.080 Uttam Kumaran: The way that I’ve actually been doing this is like anytime. There’s a data question I’ve been using this
498 00:37:09.320 ⇒ 00:37:11.709 Uttam Kumaran: to answer. And that’s how I basically
499 00:37:12.050 ⇒ 00:37:15.160 Uttam Kumaran: dust it out there. Actually, until this product there
500 00:37:15.280 ⇒ 00:37:19.219 Uttam Kumaran: light dash and like that class of bi tools is what I would recommend
501 00:37:19.650 ⇒ 00:37:23.619 Uttam Kumaran: it. I don’t know, I just feel like it’s like it’s like way better. And I’m kind of like.
502 00:37:24.120 ⇒ 00:37:25.850 Uttam Kumaran: I think this is gonna.
503 00:37:26.140 ⇒ 00:37:30.179 Jakob Kagel: It’s definitely great. For, like the data exploration piece, like, sure.
504 00:37:30.180 ⇒ 00:37:38.840 bencohen: Yeah, it’s different. I mean, you. Don’t you know what you don’t have here is like, how much money have we made today? How much profit marketing stuff, certainly.
505 00:37:38.840 ⇒ 00:37:40.180 Uttam Kumaran: And nice thing. Yeah.
506 00:37:40.180 ⇒ 00:37:43.850 bencohen: But when we’re problem solving and optimizing the business.
507 00:37:44.880 ⇒ 00:37:47.159 bencohen: this is usually what you’re trying to
508 00:37:47.460 ⇒ 00:37:48.710 bencohen: triangulate
509 00:37:49.160 ⇒ 00:37:51.299 bencohen: different different different stuff.
510 00:37:51.480 ⇒ 00:38:07.449 Jakob Kagel: Yeah, exactly. Yeah. I think the exploration piece is good. But like some of the visualization. And like, just, you know, I think, sort of the communication piece. Sometimes it can be a lot to look at, so I don’t know. We’ll we’ll we’ll figure out sort of the best path on that.
511 00:38:08.910 ⇒ 00:38:13.130 Uttam Kumaran: Yeah. And Ben, this is actually what we’re gonna look at that goes down is this average shipping zone
512 00:38:13.530 ⇒ 00:38:19.469 Uttam Kumaran: right now, we’re at like 5, 3. So this is what we’re going to be like. Can we get to like less than 3.
513 00:38:19.470 ⇒ 00:38:20.930 bencohen: Yup, yeah.
514 00:38:20.940 ⇒ 00:38:27.430 bencohen: yeah, from Texas. Be great. And I think we have to figure out is like, if you get, you know, like, we got a heat pump order in Michigan this morning.
515 00:38:27.630 ⇒ 00:38:33.229 bencohen: and instead of celebrating that we had a $4,000 transaction, my thought was like.
516 00:38:33.720 ⇒ 00:38:34.410 bencohen: Hmm.
517 00:38:34.850 ⇒ 00:38:36.870 bencohen: From Texas. Does this work.
518 00:38:37.640 ⇒ 00:38:39.810 bencohen: or do we go from yapping?
519 00:38:40.570 ⇒ 00:38:41.260 bencohen: It was, Yeah.
520 00:38:41.260 ⇒ 00:38:51.139 Uttam Kumaran: Once we get the locations there, then it’s like we should last thing. If there’s any juice is figuring out. If we have the right inventory in the right place.
521 00:38:51.410 ⇒ 00:38:52.130 bencohen: Right.
522 00:38:53.390 ⇒ 00:38:54.290 bencohen: Yep.
523 00:38:54.490 ⇒ 00:39:02.930 Uttam Kumaran: But the Texas one will help. Okay. So okay, I think we’re good on that. The other thing is the worst. We keep adding stuff for the anomaly detection. So
524 00:39:03.000 ⇒ 00:39:08.899 Uttam Kumaran: we basically did the things that Ben you mentioned. We have like rolling 14 day and 30 day
525 00:39:09.370 ⇒ 00:39:10.920 Uttam Kumaran: detection on stuff
526 00:39:12.350 ⇒ 00:39:17.190 Uttam Kumaran: that one is still really noisy. So I’m a bit like nervous to hook up to anything
527 00:39:17.630 ⇒ 00:39:21.130 Uttam Kumaran: until we’re like dialed in on identifying.
528 00:39:21.130 ⇒ 00:39:23.149 bencohen: Just just message me if you wanna
529 00:39:23.360 ⇒ 00:39:25.400 bencohen: go into it and talk about it.
530 00:39:27.710 ⇒ 00:39:33.680 Uttam Kumaran: Okay, great next steps on this, Kim. We’ll just talk in slack. And then I think we’ll try to send an update on this.
531 00:39:33.830 ⇒ 00:39:35.600 Uttam Kumaran: probably hopefully in midweek next week.
532 00:39:36.040 ⇒ 00:39:36.970 kim todaro: Sounds good.
533 00:39:37.260 ⇒ 00:39:37.980 kim todaro: Okay.
534 00:39:39.170 ⇒ 00:39:41.235 bencohen: Alright guys have a great evening.
535 00:39:41.900 ⇒ 00:39:42.450 Jakob Kagel: Thanks, Edward.
536 00:39:42.450 ⇒ 00:39:43.290 Nicolas Sucari: You guys.
537 00:39:43.550 ⇒ 00:39:44.590 Daniel Schonfeld: Thanks. Everyone.
538 00:39:45.110 ⇒ 00:39:45.830 Nicolas Sucari: And I.