Meeting Title: Data-Review-1-5-23 - Uttam <> Ben-Dan Date: 2024-01-05 Meeting participants: Uttam Kumaran, Bencohen, Daniel Schonfeld
WEBVTT
1 00:00:18.690 ⇒ 00:00:23.730 Daniel Schonfeld: Hello! Hey, Dan! Hey, Buddy, how are you? How are you.
2 00:00:24.550 ⇒ 00:00:30.050 Daniel Schonfeld: hey? Quick! Question! II see. I always see that says it uses AI. Companion.
3 00:00:30.520 ⇒ 00:00:33.919 Uttam Kumaran: It never sends me anything after, though
4 00:00:34.030 ⇒ 00:00:47.909 Uttam Kumaran: I’m happy to send it to you. So I have been recording conversations just because it helps me not miss any details, and then I actually leverage it for to do lists I’m happy to forward you.
5 00:00:48.020 ⇒ 00:00:53.860 Uttam Kumaran: I think you’ll actually be. I can even show you the one from last week. It’s it’s really quite nice.
6 00:00:54.230 ⇒ 00:01:16.179 Uttam Kumaran: What does it do to just send you a video? But it’ll give you notes or something like companion. Yeah. So, for example, like our last conversation, we talked about so many things. And so I took notes. And I, you know, I leverages kind of like a backup just in case I missed something, and I’ll show you on my end. They send you both a summary they send you a summary, they send you
7 00:01:16.260 ⇒ 00:01:27.379 Uttam Kumaran: a like a and a to do list, and they also transcribe the whole thing. Yeah. So let me just I’ll send you kind of what it looks like.
8 00:01:27.560 ⇒ 00:01:35.320 Daniel Schonfeld: I’ve turned it on on different meetings. I just never got anything. Maybe I just didn’t notice it. Yeah, it goes.
9 00:01:35.370 ⇒ 00:01:39.780 Uttam Kumaran: it usually just goes straight to to zoom. But
10 00:01:40.020 ⇒ 00:02:06.340 Uttam Kumaran: What do you mean straight to zoom like, can you? Is there a database for it in there for previous ones. Yeah. So I’ll I’m just gonna pull up exactly what it looks like in the Zoom Platform. I think it’s like amazing, and it’s free. If you pay for zoom which is great, cause a lot of other people use gong and couple of other tools that are that are paid for here is
11 00:02:08.350 ⇒ 00:02:18.800 Uttam Kumaran: like. Here’s the page for our last meeting. So if you go in here you’ll see like highlights.
12 00:02:19.080 ⇒ 00:02:20.740 Uttam Kumaran: Let’s see.
13 00:02:22.430 ⇒ 00:02:28.089 Uttam Kumaran: so it’ll go through and give like the entire audio. Transcript.
14 00:02:28.170 ⇒ 00:02:39.770 Uttam Kumaran: Give like next steps. So this is like kind of like, mainly what I’m looking to do. And then, for example, we discussed product class. And I remembered, like, probably 3 or 4 of them. And I’m like, Okay, cool. Let me go
15 00:02:39.810 ⇒ 00:02:50.349 Uttam Kumaran: look at what Ben said exactly, and then try to match that, and then it does some sort of segmentation on the meeting. and then this is like.
16 00:02:50.520 ⇒ 00:03:15.589 Uttam Kumaran: did you create the next steps? Or they know he automatically created it. Again, tough part is like, if we have, if we have like small talk, or if we talk about something else, it may not like exclude that. But I found it to be really helpful, because I you know, I usually take notes, and I have a decent memory. But we usually, I’m like, Okay, I want to talk about as much as we can. So it’s really helpful to transcribe.
17 00:03:15.850 ⇒ 00:03:17.110 Uttam Kumaran: It’s really nice.
18 00:03:17.130 ⇒ 00:03:18.880 Daniel Schonfeld: What’s my meeting coach?
19 00:03:19.020 ⇒ 00:03:39.910 Uttam Kumaran: Oh, it tells you it tells you I’ve been looking. It tells you. If you’re I guess. Good at doing meetings. But you see that you’re like I was like, Wow! That I really talk for 2 min straight. Spiel, is that Yiddish bet.
20 00:03:40.270 ⇒ 00:03:41.990 Daniel Schonfeld: Ben, you’re on mute.
21 00:03:43.770 ⇒ 00:03:44.820 bencohen: It is.
22 00:03:45.100 ⇒ 00:03:48.339 Daniel Schonfeld: That’s amazing that it uses that
23 00:03:48.400 ⇒ 00:04:05.849 Uttam Kumaran: This is great. I I’ve been turning it on. I just. I’m just like I turn it on. I don’t know where the hell it goes, but this is great. I’ll share the one from today. And this one, I’ll just email to you guys. And then you could take a look and then happy to share any of the other ones. Yeah, mainly just record for for notes.
24 00:04:06.110 ⇒ 00:04:12.410 Daniel Schonfeld: So it asked me, it says, Ask host to start. There’s a little AI companion button. If I say it’s a send request on mine.
25 00:04:12.610 ⇒ 00:04:22.810 Daniel Schonfeld: If I send that will just send me a carbon copy. It just says, using, I command to ask questions. Let me try it out. I just sent the request. Okay.
26 00:04:23.050 ⇒ 00:04:34.009 Uttam Kumaran: so I think there’s like a couple of features. There’s like the recording. There’s the transcription, the summary. And then there’s something that happens in meeting. So I guess like. Let me let me try to hit. Catch me up.
27 00:04:34.120 ⇒ 00:04:39.050 Uttam Kumaran: What does it say? I don’t know if you I guess you can see that
28 00:04:39.190 ⇒ 00:04:47.839 Uttam Kumaran: It’s like if you click on AI companion, and then you should see like a little chat come up. Oh, yeah, catch me up.
29 00:04:48.220 ⇒ 00:05:03.040 Uttam Kumaran: I think this. I think it’s just one on one actually, so far, has focused on exploring different features of the online meeting software, Daniel. And it’s on discuss features such as recording trends. This is amazing. He also mentioned trying out AI commanding to ask questions wherever no specific details or outcomes were mentioned.
30 00:05:03.350 ⇒ 00:05:04.310 Daniel Schonfeld: That’s correct.
31 00:05:04.420 ⇒ 00:05:06.579 Daniel Schonfeld: Okay, that’s really clever.
32 00:05:07.840 ⇒ 00:05:17.309 Uttam Kumaran: Yeah, I’m finding it helpful, because it’s just good to retain some of this. No, this is very good, especially when I do. I’m gonna request all of my father-in-law because he forgets everything.
33 00:05:17.340 ⇒ 00:05:26.519 Daniel Schonfeld: I this is amazing cause I’m just gonna go back to Nope. Yeah, Ben, he tells me that you told me this. He never remembers telling me anything, and I forget to.
34 00:05:27.180 ⇒ 00:05:52.580 Uttam Kumaran: yeah, it’s really nice, because we may have a specific conversation like even I look back at some of our older conversations as well. Because I’m getting to some of those projects, or I’m like thinking about something we talked about, and I don’t have to. Instead of asking again or bringing it up again. II can just go back at the Transcript and look. And then, you know, that’s been amazing. Cause. Yeah, this is great. II gotta remember, I have like 10 different zoom accounts. I gotta remember to use the right one.
35 00:05:52.580 ⇒ 00:06:04.809 Uttam Kumaran: Yeah, it’s worth it. Yeah, definitely, yeah, a couple of things to go through today. So I have one. I have one thing.
36 00:06:05.000 ⇒ 00:06:13.839 bencohen: the ad spend one sec. Hold on. I lost my tab hold on! Hold on a lot too many tabs. I wanna I wanna nip this one.
37 00:06:15.220 ⇒ 00:06:16.240 bencohen: Okay.
38 00:06:17.390 ⇒ 00:06:18.510 bencohen: the
39 00:06:18.790 ⇒ 00:06:22.249 bencohen: it’s unlikely, but I guess possible. But
40 00:06:22.810 ⇒ 00:06:28.750 bencohen: it says we lost almost 9 grand yesterday. I just want to dig into that.
41 00:06:28.770 ⇒ 00:06:34.240 bencohen: II think I know why this is happening. But I just wanna make sure that these numbers are accurate, because
42 00:06:35.270 ⇒ 00:06:46.820 bencohen: in a normal thing, if if if there’s not significant returns like if we didn’t process a lot of returns or something on one day. This this would not be the case. 14,000, I think, is
43 00:06:47.230 ⇒ 00:06:53.010 bencohen: just about like on the nose. I looked shopify was about 8. Amazon was about
44 00:06:53.420 ⇒ 00:06:56.760 Uttam Kumaran: little under 6 Walmart
45 00:06:57.200 ⇒ 00:07:00.260 bencohen: couple of shekels, I guess, but
46 00:07:01.350 ⇒ 00:07:03.340 bencohen: The 8.6 is too high
47 00:07:04.170 ⇒ 00:07:11.290 Daniel Schonfeld: on what? On the marketing negative profit yesterday. Well, I did see that there were 2 he pumps returned.
48 00:07:11.500 ⇒ 00:07:17.680 Daniel Schonfeld: If you go to the gross sales under shopify, there was 1,000, whatever that discount was.
49 00:07:18.070 ⇒ 00:07:19.190 Daniel Schonfeld: So those are
50 00:07:19.700 ⇒ 00:07:25.650 Daniel Schonfeld: yeah, you gotta go to the raw numbers I tried to pull. That’s what I was trying to do today. That’s what I’m trying to do
51 00:07:26.110 ⇒ 00:07:36.870 Daniel Schonfeld: for the next couple of weeks is every single day I’m running up my own personal Pnl. just using raw data from the source itself and then comparing it to like dash and to
52 00:07:37.270 ⇒ 00:07:42.720 Daniel Schonfeld: Yeah, I’m just comparing it. I’m just not getting
53 00:07:43.610 ⇒ 00:07:45.220 Daniel Schonfeld: the same numbers yet.
54 00:07:45.390 ⇒ 00:07:50.689 Daniel Schonfeld: We’re getting close on some, but I also think it has to do with the time things are processed.
55 00:07:50.750 ⇒ 00:08:02.110 Daniel Schonfeld: the cut off, the cut off dates, all that kind of stuff. So just I just want to get it to a point where I can stop. I can rely on it, at least, for I know there’s so much cool data in here, but
56 00:08:02.170 ⇒ 00:08:11.639 Daniel Schonfeld: I still can’t get past the first boxes on the home screen. Those work where it says daily sales like, I show if you go to the go to the main dashboard.
57 00:08:16.590 ⇒ 00:08:24.050 Daniel Schonfeld: Yeah. So 1411 is that, can you just tell me? 1411, what is that snapshot from? Is that just
58 00:08:24.200 ⇒ 00:08:35.650 Uttam Kumaran: yeah. What? What exact timeframe is that from this is from completed day yesterday, and the sources are Amazon shopify. And then, if there’s anything from Walmart.
59 00:08:35.710 ⇒ 00:08:38.949 Daniel Schonfeld: okay? And do what’s the exact time cut off for that?
60 00:08:39.039 ⇒ 00:08:45.999 Daniel Schonfeld: Yeah, obviously, Amazon is different than shopify. Is it set for like, just take it from 12 Pm. Eastern.
61 00:08:46.370 ⇒ 00:08:48.340 Daniel Schonfeld: I mean 12 am. To 12.
62 00:08:48.490 ⇒ 00:09:00.199 Uttam Kumaran: Exactly. So everything I’m converting. So we get everything in like Utc time, and like everything converted to East Coast, and then is East Coast 12 to 12.
63 00:09:00.770 ⇒ 00:09:04.060 Daniel Schonfeld: Everything is converted to East coast. So
64 00:09:05.310 ⇒ 00:09:13.509 Daniel Schonfeld: so really, Amazon, we’re not getting the picture because there would be 9 8. II think they’re on California.
65 00:09:13.550 ⇒ 00:09:22.530 bencohen: Amazon. 30’clock, our time to call a day. Yeah, I’m just wondering if maybe you wait till 3 Am. To pull it.
66 00:09:22.590 ⇒ 00:09:30.070 Daniel Schonfeld: and Jessica goes back and pulls all shop. I have no idea what Walmart schedule is, but imagine by 3 Am.
67 00:09:30.400 ⇒ 00:09:35.320 Daniel Schonfeld: We’ll have Amazon’s full picture, and then it could just go back and pull all the numbers one time.
68 00:09:35.860 ⇒ 00:09:46.299 Daniel Schonfeld: You know, for for the previous day, and then we’ll at least have when I go apples to apples like today. When I look at Amazon’s numbers yesterday, it’s really pulling from
69 00:09:46.750 ⇒ 00:09:55.560 Daniel Schonfeld: till 3 am. And again, I don’t know if that’s right? Because losing 3 h like if a big sale came in between 12 and 3,
70 00:09:56.670 ⇒ 00:09:59.949 Daniel Schonfeld: is that also being considered for the for the lag.
71 00:10:00.720 ⇒ 00:10:23.770 Uttam Kumaran: Yeah, no, you’re right. So there’s 2 things. One is when the day closes, there’s typically a lag where they reconcile and they ship the data. So usually I’m trying to align all of the stuff to run and get completed. So both the data gets pulled and the models run before 6 Am. Eastern. So that means I usually have stuff triggered around 4
72 00:10:23.950 ⇒ 00:10:38.170 Uttam Kumaran: But now that we’ve added a couple of more sources, I need to go back and kind of do that audit, and just kind of do a visual that everything triggers around 4, and then the models kind of kick off immediately after that. And so that’s that’s like pretty common
73 00:10:38.260 ⇒ 00:10:43.299 Uttam Kumaran: the way I’ve handled. You know those types of timestamps. But you’re right. If something does come in
74 00:10:43.370 ⇒ 00:10:49.999 Uttam Kumaran: later in the day Pacific. Then it depends on what we want to consider.
75 00:10:50.060 ⇒ 00:10:53.790 Daniel Schonfeld: I would just make it true.
76 00:10:53.870 ⇒ 00:10:55.799 Daniel Schonfeld: I would just imagine me.
77 00:10:56.050 ⇒ 00:11:02.660 Daniel Schonfeld: and then Ben and I sitting here at I’m gonna come in. I’m gonna do this now every day, religiously.
78 00:11:03.120 ⇒ 00:11:12.020 Daniel Schonfeld: I’ve I’ve resigned. I have it in my schedule to do it every day at 9 Am. Is all I do is I go back and run and try, go to shopify around the previous day.
79 00:11:12.170 ⇒ 00:11:18.899 Daniel Schonfeld: exports all those numbers from the very gross. because it yeah, just like that.
80 00:11:19.260 ⇒ 00:11:29.670 Daniel Schonfeld: And then I go to Amazon, which is not so simple, which is really where most of my problems come from. They basically have to just take the the top line rock. I don’t think I get, or maybe I just don’t know how to look for it. But
81 00:11:29.910 ⇒ 00:11:33.460 Daniel Schonfeld: if you run yesterday’s yeah. I tried to run.
82 00:11:34.010 ⇒ 00:11:54.619 Uttam Kumaran: Yeah, I mean, I tried to run yesterday’s, but it even said, data isn’t fully available yet. So I looked at this like home screen. But I’m gonna try to. I looked at like the sales dashboard and then filter this. But I’m gonna try to find where we can get that the thing with Amazon is
83 00:11:54.990 ⇒ 00:12:02.240 bencohen: so what they will do sometimes is like you’ll sell something, and of course it captures the sale. but, like
84 00:12:03.030 ⇒ 00:12:07.779 bencohen: like the payment process, it could take like 3 days for that specific order.
85 00:12:07.890 ⇒ 00:12:09.699 bencohen: and when that happens
86 00:12:10.030 ⇒ 00:12:17.990 bencohen: they count it in the day which doesn’t in my mind. It doesn’t make sense, and it’s unlike how everybody else operates, but
87 00:12:18.880 ⇒ 00:12:26.770 bencohen: nothing nothing. That’s why you have that. It’s not all in yet, cause they’re like a reconciliation period.
88 00:12:27.280 ⇒ 00:12:32.009 bencohen: Listen, we. I don’t think we’re gonna be able to get 100 100 100%. But.
89 00:12:32.120 ⇒ 00:12:42.280 Uttam Kumaran: by the way, it’s a lot worse now, when we’re not in season, because the numbers are way smaller. So it’s like
90 00:12:42.980 ⇒ 00:12:44.540 bencohen: one sail missed
91 00:12:44.550 ⇒ 00:12:53.420 bencohen: can create like a like a 9% delta that all of a sudden you’re saying this isn’t good. If we’re selling $60,000 of stuff a day.
92 00:12:54.500 ⇒ 00:13:01.800 bencohen: 5 sales missed, you know. It’s it’s annoying, but it’s doesn’t feels as damaging.
93 00:13:02.220 ⇒ 00:13:03.940 Uttam Kumaran: Yeah, understand?
94 00:13:04.330 ⇒ 00:13:24.580 Uttam Kumaran: So there’s so the one thing on going back to Ben, on your point. There you’re right. There is the 14,000 in sales. The one thing I’m noticing is the report in which we calculate total profit, and I’ll just show you at the bottom. Here is actually where we have, like all the components of
95 00:13:24.650 ⇒ 00:13:40.940 Uttam Kumaran: that calculation. It’s coming out to just eightk on total sales. So that’s on me. I gotta go look at why those aren’t. I saw that this morning. I didn’t make sense that that actually is in line with the returns of the heat pumps. Yeah.
96 00:13:40.970 ⇒ 00:13:45.169 Daniel Schonfeld: no, yeah. So I think we just have to be real clear also, like.
97 00:13:45.520 ⇒ 00:13:54.149 Daniel Schonfeld: and this is where I get confused, and I just give up when I go to vital signs. I see 1411. I’m not really sure if that is a gross
98 00:13:54.320 ⇒ 00:13:55.740 Daniel Schonfeld: daily cells.
99 00:13:55.990 ⇒ 00:14:16.609 Daniel Schonfeld: Minus anything else is what what would be great if it was. And I I’m guessing it is is just the complete raw sales number. If you just take complete top line, forget discounts, returns, even warranties. Just how many total units went out the door that well, I shouldn’t say warranty, but total
100 00:14:17.000 ⇒ 00:14:31.190 Daniel Schonfeld: units that were purchased
101 00:14:31.400 ⇒ 00:14:42.720 Daniel Schonfeld: the number 3 on the dashboard. Information profit equal salesman, I think something like that where it’s a little hover over, or even just it says it at the top. Just so there’s no confusion. And this sells
102 00:14:42.800 ⇒ 00:14:51.510 Daniel Schonfeld: daily roll, you know, something like gross roll up of total or whatever of gross sales from Amazon, Walmart, and shopify.
103 00:14:51.920 ⇒ 00:14:56.170 Daniel Schonfeld: and it’s just the raw, gross number of sales, and then we can
104 00:14:56.270 ⇒ 00:15:08.250 Daniel Schonfeld: cascade downwards. The daily profit obviously, obviously, is clear by Number 3. But maybe put that in the box. We don’t even need that up there alright. So Cody confirmed.
105 00:15:09.790 ⇒ 00:15:19.209 bencohen: They use the discount feature on shopify when they’re sending a warranty replacement. That is the reason for so that’s the reason for that.
106 00:15:19.310 ⇒ 00:15:27.550 bencohen: Black and Decker, 3 horsepower issue. And it’s probably also similar on the heat pump. We need to consider a better way to classify those.
107 00:15:27.570 ⇒ 00:15:40.959 Daniel Schonfeld: Yeah, let’s let’s table that one for this discussion. But we’ll I’m gonna we’re gonna have to look into best practices from other e-commerce companies of how they how they process warranties. And I wonder if shopify even has a plugin or something?
108 00:15:41.300 ⇒ 00:15:53.180 Daniel Schonfeld: But we’re not the first ones to process warranties on on shopify. So there’s gotta be people doing this all day every day. I think we just have to look into what those best practices are. It might be the way we’re doing it, I’m not sure but
109 00:15:53.700 ⇒ 00:16:09.049 Daniel Schonfeld: Let’s just let’s just look into that. And and maybe even have Cody do a little bit of research. It’d be better if he figures it out and make sure that I’m sure there’s a number of different ways to do it. But I’m sure quick Google search warranties to shopify will will produce a lot of
110 00:16:09.140 ⇒ 00:16:10.220 Daniel Schonfeld: results for him.
111 00:16:10.650 ⇒ 00:16:20.849 Daniel Schonfeld: But we should do that in a timely fashion, because it is throwing off when I try to run pnls of what’s going on. It just look Wonky, especially when we had 3 grand in sales or 2,500
112 00:16:20.910 ⇒ 00:16:24.100 Daniel Schonfeld: for that one skew and half of it went to a discount.
113 00:16:24.230 ⇒ 00:16:34.759 Daniel Schonfeld: okay, so so that’s so. That’s my biggest thing right now. I can’t even go deep into this reporting, because I’m not sure
114 00:16:35.200 ⇒ 00:16:39.929 Daniel Schonfeld: the integrity of the data at the moment. And what certain numbers actually
115 00:16:40.100 ⇒ 00:16:45.709 Daniel Schonfeld: mean like I like, I was just saying, with the daily sales, daily profits clear.
116 00:16:46.290 ⇒ 00:17:11.190 Uttam Kumaran: so one thing I’m also gonna do is I will actually have the links to these reports, because I’m now looking directly at these every day. So I’m gonna up the links directly here. So the reconciliation back to the source will be really easy. And then I’m gonna go through and verify that this is all gross. And then we’ll indicate that here as well.
117 00:17:11.329 ⇒ 00:17:23.769 Uttam Kumaran: and then and then I’m continuing to work on kind of the drill downs. So you’ll be able to click in and kind of be able to go directly to see the components. Individual orders.
118 00:17:25.430 ⇒ 00:17:41.279 Daniel Schonfeld: Yeah, II think if you look at this the way I look at it, at least for the initial part, and just pretend you’re me, and you’re coming at 9 am. And you just say, Okay, what would happen yesterday? And you just go to the true source of things and say, what was it? Where were the total sales for yesterday.
119 00:17:41.310 ⇒ 00:17:44.850 Uttam Kumaran: Yeah, how much did it cost me in marketing? How many returns did we have?
120 00:17:44.920 ⇒ 00:18:03.410 Daniel Schonfeld: What did we give discounts to just the basic questions you would ask when running a business and how do we? How do we see that here? So we can get a quick snapshot? And I can just see if things are off like I just. I had a probably spent 20 min to get to this, the whole discount thing, and I just texted Ben. Hey, do me a favor. Can you look into? Why, we discounted
121 00:18:03.440 ⇒ 00:18:07.539 Daniel Schonfeld: half of the revenue for these 2 skews? It was only 2 units.
122 00:18:07.850 ⇒ 00:18:18.479 Daniel Schonfeld: and I actually think it’s a really good time of year to do this, because it’s smaller. And we can catch things now. So when things get really really crazy. We don’t miss anything big.
123 00:18:18.480 ⇒ 00:18:39.230 Daniel Schonfeld: So it’s actually good that it’s a big blaring number when it when one is missed, because now we’ll we’ll be ultra sensitive to it because a lot of stuff gets lost in the mix. You know, when we’re when we’re in Peak. So this is a good time to actually be doing this stuff. Yeah, that that makes a lot of sense. I’m actually gonna lean on a little bit more of the tables and actually have
124 00:18:39.230 ⇒ 00:18:42.089 Uttam Kumaran: that access to each individual order from
125 00:18:42.610 ⇒ 00:19:08.119 Uttam Kumaran: like right here. So you don’t have to go in and look for those individual orders. And I can add that. And then, additionally, we have some breakdowns of, you know each of the major components, but on things like refunds and discounts I’ll break out even further, because cogs are just fixed, associated with the product and then marketing costs gets rolled up. So for refunds and discounts. I will also break out individually
126 00:19:08.840 ⇒ 00:19:18.910 Daniel Schonfeld: okay, and our cogs being pulled from unleashed. Or are you using? Because I noticed in shopify some of the data is off for the cogs? I’m not really sure how that actually happens. But
127 00:19:19.290 ⇒ 00:19:24.340 Uttam Kumaran: I noticed that some of the costs are off in shopify.
128 00:19:25.880 ⇒ 00:19:29.670 Uttam Kumaran: It’s all coming from unleashed right now.
129 00:19:30.170 ⇒ 00:19:34.370 Daniel Schonfeld: I was gonna say, that is that we have to make sure the data is coming from the true source.
130 00:19:34.430 ⇒ 00:19:44.299 Uttam Kumaran: Yeah, the other issue with shopify sometimes is their definitions of some of these are also different. An example I’ll give, and I kind of indicated before is
131 00:19:44.500 ⇒ 00:19:49.310 Uttam Kumaran: when they do a return. They consider it. They take it out from the past.
132 00:19:49.750 ⇒ 00:19:54.540 Uttam Kumaran: So they they almost netted out from the sale before.
133 00:19:54.550 ⇒ 00:20:05.699 Uttam Kumaran: So the way I’ve I’ve had folks decide is whether we want the return to happen on the day of the return right than the negative, or if we want the return to go, affect the past.
134 00:20:05.780 ⇒ 00:20:12.499 Uttam Kumaran: if it affects the past, you could still see the return date. But of course the money comes out of then
135 00:20:13.390 ⇒ 00:20:17.610 Uttam Kumaran: III don’t know. I I’ve done both. It kind of it’s more of a
136 00:20:18.090 ⇒ 00:20:21.930 Uttam Kumaran: perspective question. But there’s a decision making, right? So
137 00:20:22.090 ⇒ 00:20:23.720 Daniel Schonfeld: yeah, I think,
138 00:20:24.780 ⇒ 00:20:27.400 Daniel Schonfeld: yeah, I’m gonna think about that one but
139 00:20:27.460 ⇒ 00:20:30.240 Daniel Schonfeld: What what is it doing now? How do we do it? In our system?
140 00:20:30.370 ⇒ 00:20:34.339 Uttam Kumaran: We’re just considering the time of return as when the return is made.
141 00:20:34.480 ⇒ 00:20:42.780 Uttam Kumaran: But, for example, like, let’s say, there’s a ton of returns today. Those returns are associated with orders, of course, from weeks ago.
142 00:20:42.820 ⇒ 00:20:52.160 Uttam Kumaran: and so those counted as sales, and so you may have a, and the way this kind of manifests is, you may have a high profit day, but then, actually, there were returns associated with that day in the future.
143 00:20:52.200 ⇒ 00:20:53.500 Uttam Kumaran: And so
144 00:20:54.080 ⇒ 00:20:59.460 Uttam Kumaran: I can send a little bit of a the blurb. II wrote on that again, and then, yeah, I’m
145 00:20:59.840 ⇒ 00:21:11.359 Daniel Schonfeld: right now. It’s defaulting to the day of the return the day right? So it would all lump it. If there were 10 grand in return. Today we’d show a major loss for a day.
146 00:21:11.470 ⇒ 00:21:18.499 Daniel Schonfeld: yeah. II wonder if the return was sent back to a previous day? We we might not see it at all
147 00:21:18.540 ⇒ 00:21:34.929 Daniel Schonfeld: right? Because, yeah, I think so, because I don’t know. Then we’d have to just like randomly search for return throughout the entire year we could search for the return created day as today. But I’m actually more specifically talking about the amount
148 00:21:35.600 ⇒ 00:21:40.370 Uttam Kumaran: like the amount will either go and reflect past that past order.
149 00:21:40.550 ⇒ 00:21:46.970 Uttam Kumaran: or it will reflect as a negative. Today. Right now, we’re doing the latter. Which is.
150 00:21:47.010 ⇒ 00:21:51.220 Uttam Kumaran: it just comes in as a negative for today, although the order may have been in the past.
151 00:21:51.580 ⇒ 00:22:02.560 Daniel Schonfeld: Yeah, it it should always be attributed from a financial standpoint. I have to check. That’s why I need to just check. I need to make obviously make sure in 0 when I run my actual financing
152 00:22:02.810 ⇒ 00:22:05.140 Daniel Schonfeld: software that it’s
153 00:22:05.420 ⇒ 00:22:13.600 Daniel Schonfeld: being discounted or taken off at the time from the original sale. But for reporting purposes I’d wanna know
154 00:22:13.630 ⇒ 00:22:23.419 Daniel Schonfeld: if 20 returns came in today. And then I could do the research and figure out where those returns came from. But let me
155 00:22:23.840 ⇒ 00:22:27.750 Daniel Schonfeld: let me come back to you on that one. If you can just bring that up again.
156 00:22:28.130 ⇒ 00:22:29.430 Daniel Schonfeld: in the future.
157 00:22:30.750 ⇒ 00:22:37.769 Daniel Schonfeld: That would be helpful. Just so, we see. But we definitely want to know on any given daily basis. How many returns were processed or
158 00:22:38.060 ⇒ 00:22:41.190 Daniel Schonfeld: requested. Excuse me.
159 00:22:43.140 ⇒ 00:23:04.419 Uttam Kumaran: okay, so let me make those modifications to this, and I’ll I can do that all today. And then, Ben, I’m gonna follow up on that that sales number so I’ll wrap that all up today, and then, like noted on kind of coming in in the morning and looking at this. Now, I’m kind of gonna go through and do the same stuff, which is pretty much looking at each of these, and also
160 00:23:04.500 ⇒ 00:23:32.079 Uttam Kumaran: try move this breakdown up. So we have each of these components. And then and then it’s actually helpful to know that you’re going through and looking at each order, because that’s that’s exactly what I’m gonna put on here, which is every single order from each platform for the day. And then also the breakdowns of which orders had discounts which orders and then returns will be included there. Yeah, I think the goal is to initially is just to make sure that the data. Integrity is there, and it’s timely. And we’re all looking at it on the same timeframe.
161 00:23:32.390 ⇒ 00:23:39.589 Daniel Schonfeld: but for now do it in accordance with how the other platforms work like their timeframes. So
162 00:23:39.680 ⇒ 00:24:01.629 Daniel Schonfeld: we can compare apple to apples and get that level of data integrity. And then we can once we feel very comfortable after a certain amount of time has passed that everything is matching up. Then we can say alright. Now we can do this, manipulate on our terms. We know the data. Integrity is there. But now we can manipulate to look at it the way we wanna look at it. To make the most sense.
163 00:24:03.720 ⇒ 00:24:08.200 Uttam Kumaran: the only other things to cover today. Were
164 00:24:08.550 ⇒ 00:24:13.719 Uttam Kumaran: we? Added the product class. So sorry. I now have a ton of tasks.
165 00:24:13.740 ⇒ 00:24:15.269 Uttam Kumaran: This is like the
166 00:24:15.580 ⇒ 00:24:19.259 Uttam Kumaran: the product class sales for the last
167 00:24:19.550 ⇒ 00:24:43.860 Uttam Kumaran: 7 days. So now, instead of having to skews, I I’m gonna lean more on these. So we Consolidated to here, and I’ll I can add a little bit of we, ben we do have that mapping sheet. I don’t know if it’s best. Maybe I can create a little bit. I’ll I’ll like, have that link somewhere. That in case we wanna reference, what’s going into what?
168 00:24:44.650 ⇒ 00:24:46.420 bencohen: yeah, yeah, I mean.
169 00:24:46.890 ⇒ 00:24:52.070 bencohen: but for the most part it’s it’s not gonna change. There’s moments where.
170 00:24:52.340 ⇒ 00:24:56.620 bencohen: like like one skew that comes to mind like a booster pump.
171 00:24:58.610 ⇒ 00:25:05.510 bencohen: I don’t know where that goes. Currently, I can’t remember what we put for the sheet. If that was even on the sheet, we haven’t sold it in a while, so it might not even be relevant. But
172 00:25:05.750 ⇒ 00:25:10.799 bencohen: for the most part they’re not gonna change ever. We might just add some stuff to it.
173 00:25:10.960 ⇒ 00:25:15.290 bencohen: and that’s something we need to think about. By the way, when we add a new product to the mix.
174 00:25:15.340 ⇒ 00:25:20.350 Daniel Schonfeld: Well, yeah, I was gonna say, we’re we’re about to introduce filter systems, filtration systems.
175 00:25:20.940 ⇒ 00:25:26.199 Daniel Schonfeld: Which are coming in very soon. It should arrive in the next month or 2. We need to create a new category for that.
176 00:25:27.110 ⇒ 00:25:42.179 Uttam Kumaran: Okay? So as a default, things will come up as uncategorized, and that that’ll be like, catch it. But if we proactively know to, and I can get those the skews and the names. I can add those in.
177 00:25:42.360 ⇒ 00:25:50.199 Daniel Schonfeld: Yeah, I would do it. What will on so will it catch it automatically? I think you were just saying that. So let’s say, we added in
178 00:25:50.600 ⇒ 00:26:01.340 Daniel Schonfeld: 5 new skews. They were filtration systems, and they were put into unleashed entered in and then pushed out to shopify and Amazon. Would it show up in this system? Would it start pulling through
179 00:26:01.490 ⇒ 00:26:22.670 Uttam Kumaran: it? Yeah, it would show in terms of this categorization. This, like you consider on top. So this it would just come up as uncategorized because we haven’t accounted for it unless it doesn’t fill fill any of the other kind of categories, and then we would catch it. But that would have no ramification on it showing up on the from the raw data.
180 00:26:22.770 ⇒ 00:26:29.230 Daniel Schonfeld: Okay, I was gonna save cause. So I just I’m reluctant to give you any kind of
181 00:26:29.300 ⇒ 00:26:48.649 Daniel Schonfeld: skew information or model information, because the way I tell it to you may come through differently from unleash, like maybe there’ll be a space between the Bd. X. Whatever, and then it doesn’t map it properly. You might as well just wait for it to come in. We’ll tell you when we enter to unleash, and probably have just Chuck. Send you in excel sheet downloaded right from.
182 00:26:48.750 ⇒ 00:26:54.809 Daniel Schonfeld: If that’s helpful, or is it easier to just let it come? We’ll tell you. Hey? Those sales are starting today? You’ll start to see them.
183 00:26:55.270 ⇒ 00:26:58.190 Daniel Schonfeld: We’ll just tell you what each one is.
184 00:26:58.470 ⇒ 00:27:17.760 Uttam Kumaran: It’s a pretty. It’s a pretty quick fix on my end. So if if it’s easiest, and I agree. The names across all the platforms like are really vary. So I’ll just wait for it to come in, and then I’ll catch it. Yeah, we’ll we’ll give you a heads up when we when they enter the get to the warehouse, or sorry
185 00:27:17.950 ⇒ 00:27:22.630 Daniel Schonfeld: getting entered to skews into and to unleash which they may already
186 00:27:22.710 ⇒ 00:27:30.859 Daniel Schonfeld: be in there. If chuck he may have already inserted them. yeah, so we can. We can send you that. But
187 00:27:30.910 ⇒ 00:27:32.280 Daniel Schonfeld: just just a
188 00:27:32.510 ⇒ 00:27:47.260 Daniel Schonfeld: just to recap real quick. I’m gonna wait and not go into the system really at all. I’m gonna continue to do my own analysis through raw data. By just logging into shopify into Amazon as best as I can, and try to get the numbers.
189 00:27:47.520 ⇒ 00:27:48.579 Daniel Schonfeld: And when you
190 00:27:49.050 ⇒ 00:27:53.289 Daniel Schonfeld: you should do the same and do that the same way I do it. And once you feel
191 00:27:53.380 ⇒ 00:27:56.330 Daniel Schonfeld: that the data is mapping as close as possible.
192 00:27:56.520 ⇒ 00:28:02.010 Daniel Schonfeld: Then I’ll start going back back in and and doing my own
193 00:28:02.080 ⇒ 00:28:09.960 Daniel Schonfeld: you know, kind of checks between the 2 systems. I’m gonna not log in for a little bit. I’m gonna wait till you tell me you feel it’s at a point where
194 00:28:10.600 ⇒ 00:28:14.720 Uttam Kumaran: you you think I could start pulling those reports accurately.
195 00:28:17.290 ⇒ 00:28:26.679 Uttam Kumaran: The other thing to cover briefly, was just the ups data. So one thing I wanted to share apologies.
196 00:28:26.770 ⇒ 00:28:37.280 Uttam Kumaran: So we hadn’t been shipping a ton of volume with ups. This was our ups cost per pound by zone for the last.
197 00:28:37.670 ⇒ 00:28:53.659 Uttam Kumaran: you know, 10 weeks or so, and you can see, you know, we just had a couple of zones that were shipping, and after we we migrated we now have shipping across each zone. And what what we’re seeing is oh, geez! We’re seeing that on average, like our
198 00:28:53.660 ⇒ 00:29:11.909 Uttam Kumaran: we were at around like a dollar $2 for ups. And now we’re closer to like 50 cents on days of like really high volume for for price per pound. What’s the what’s the orange?
199 00:29:12.050 ⇒ 00:29:14.040 Uttam Kumaran: The orange? Is the ship in wait?
200 00:29:14.230 ⇒ 00:29:23.409 Uttam Kumaran: The weight of the parcel. Yeah. So for me, for me to normalize across all shipments and the rate chart it works at a
201 00:29:23.730 ⇒ 00:29:32.449 Uttam Kumaran: at a zone and a price per pound. So if I’m governing getting rid of zone, I mainly was just. That’s just like a raw price per pound.
202 00:29:32.790 ⇒ 00:29:37.579 Uttam Kumaran: So the pounds it just means that there’s more volume shipping because the pounds went up.
203 00:29:37.600 ⇒ 00:30:01.830 Uttam Kumaran: But the price the blue is the price per pound, which should obviously go down. But the orange should go up because we’re shipping more right? That’s the total pounds of what we shift. Exactly. And I think the consideration to make is to like look at it compared to Fedex. So this is our, this is our Fedex price per pound, which is in the blue, the same weight. And I’m working on getting this into one chart. There was a we have a lot of different
204 00:30:01.860 ⇒ 00:30:04.159 Uttam Kumaran: shipping services like
205 00:30:04.210 ⇒ 00:30:17.909 Uttam Kumaran: which is like sure post Amazon ground Amazon ups ground. So I now categorize them. So we’ll see this all in one chart, Fedex, for cps, but you can see that our price per pound on Fedex is around like
206 00:30:18.450 ⇒ 00:30:25.790 Uttam Kumaran: like 380 or so, and they’re all quoting pretty evenly, and then we
207 00:30:26.260 ⇒ 00:30:30.329 Uttam Kumaran: on the ups side, it’s quite a bit cheaper.
208 00:30:31.920 ⇒ 00:30:47.210 Uttam Kumaran: And so one thing I wanna look at for the next 4 weeks is cause we’re now moving volume onto ups. So I want, I wanna one thing I’m looking at is on a per it’s on a per zone basis. And I think chart here. That kind of shows that although it’s a little bit small.
209 00:30:47.250 ⇒ 00:30:54.250 Uttam Kumaran: What we’re we’re able to do is actually look on a per zone base and see what we’re shipping. And so I’ll be able to kind of. Let you know that
210 00:30:54.470 ⇒ 00:31:17.789 Uttam Kumaran: this is this is just all the orders that we shipped by zone. I’ll be able to let you know which zones where work we’re not competitive between Ups and Fedex and the price per pound per zone, which is, which is ultimately like, I think, the most a really great indicator of by zone, by Provider. What’s our price per pound to ship?
211 00:31:18.080 ⇒ 00:31:24.899 Uttam Kumaran: So we’re that’s great. At the end of yeah.
212 00:31:25.180 ⇒ 00:31:28.959 Daniel Schonfeld: Where where is this data being sourced from, how are you getting this data?
213 00:31:29.160 ⇒ 00:31:39.820 Uttam Kumaran: So everything’s coming from ship station? So we get all the dimension, the box dimension information from ship station we get the
214 00:31:40.020 ⇒ 00:31:56.869 Uttam Kumaran: whoever like the provider that was being shipped from ship station, and then I’m everything that’s Ltl is is separate, and then I’m Cal. We’re calculating. Actually, the zone based on the shipping address. So we take the lat. Long of the zone
215 00:31:57.040 ⇒ 00:32:16.530 Uttam Kumaran: in relation to the the lat. Long of the shipping address in relation to the the DC. And then calculate the zone, which is, which is like pretty much how they’re doing it as well, and then we map. We mapped it out so like they have the thing where it’s like, if it’s greater than 50 miles or within certain ranges. It’s the next zone.
216 00:32:16.740 ⇒ 00:32:18.700 Uttam Kumaran: So that’s the entire
217 00:32:18.770 ⇒ 00:32:23.669 Uttam Kumaran: pipeline. And then for every for all the shipment data, we link back to the, to the orders
218 00:32:23.720 ⇒ 00:32:25.990 Daniel Schonfeld: awesome. This is great. Thank you.
219 00:32:30.000 ⇒ 00:32:32.919 Daniel Schonfeld: Okay, great. I mean, it looks significantly
220 00:32:33.220 ⇒ 00:32:39.459 bencohen: less. Yeah, I did a manual review with Chuck yesterday.
221 00:32:39.600 ⇒ 00:32:40.540 bencohen: It was.
222 00:32:40.720 ⇒ 00:32:49.759 bencohen: I mean, it was, it’s a lot less. And you know it. It’s like, because we’re not selling a ton. Now, numbers are not crazy. But by by percent.
223 00:32:50.240 ⇒ 00:32:51.660 bencohen: It is significant.
224 00:32:52.330 ⇒ 00:33:01.949 Daniel Schonfeld: Yeah, it’s it’s more than a 30% reduction. I’m reading right? But it also depends. I’m assuming this is more cover pumps right this time of year. It’s some
225 00:33:02.130 ⇒ 00:33:12.759 Daniel Schonfeld: black and decker sprinkled in I mean the variable speeds. But really using unis now for the larger heat pumps.
226 00:33:13.340 ⇒ 00:33:26.590 Daniel Schonfeld: I’m really curious if we can look at this on a per pound by class. So I could see there cause variable speeds is gonna be our probably our biggest shipping call that, and heat pumps during peak.
227 00:33:26.720 ⇒ 00:33:28.629 Daniel Schonfeld: I’m curious what we’re
228 00:33:28.750 ⇒ 00:33:40.250 bencohen: seeing in a reduction on larger packages such as the variable speed. Specifically, I saw I speak that would be I. Spot checked that.
229 00:33:40.260 ⇒ 00:33:48.830 bencohen: It was like like like dollars less for the variable speed. There. There weren’t that many sold, but it was just
230 00:33:48.870 ⇒ 00:33:53.749 bencohen: without looking at the sheet like if it was $36 before. It was like 29 now.
231 00:33:55.360 ⇒ 00:34:05.459 Daniel Schonfeld: But we need that. You know. I think we get a major savings there, that’s where I think we’re gonna get the biggest. No, why didn’t we? It doesn’t seem big enough. It only seems like
232 00:34:05.690 ⇒ 00:34:14.770 bencohen: 10. What is that? What you say went from 30. It was same percent I was making up those numbers. I can look at the actual numbers.
233 00:34:15.230 ⇒ 00:34:22.879 Daniel Schonfeld: Yeah, that’s where we’re gonna see the biggest savings. it’s not cover. I’m assuming this is a lot of cover pumps again, just an assumption
234 00:34:23.429 ⇒ 00:34:34.349 Daniel Schonfeld: but variable speeds is where we’re gonna save the most money heat pumps were already trying to move most the units
235 00:34:34.360 ⇒ 00:34:35.860 bencohen: I’m assuming
236 00:34:36.110 ⇒ 00:34:44.649 bencohen: to ship a variable speed pump from Long Island to Arizona is around 35, $36, which I knew. So there was one here.
237 00:34:45.280 ⇒ 00:34:54.190 bencohen: $35, 84 cents, or just call it 36. And the new fee was 2769. So it was like
238 00:34:54.980 ⇒ 00:34:57.099 bencohen: $9 less. That’s
239 00:34:57.220 ⇒ 00:34:58.300 bencohen: a lot.
240 00:34:59.010 ⇒ 00:35:02.320 Daniel Schonfeld: That’s a lot that’s great.
241 00:35:02.940 ⇒ 00:35:06.909 bencohen: There was one that was even more severe to California.
242 00:35:07.060 ⇒ 00:35:15.639 bencohen: It was a $14 savings. These are some of these are really beefy. Yeah, you’re right. The variable speeds are. I mean, obviously, they’re heavy.
243 00:35:18.070 ⇒ 00:35:29.660 Daniel Schonfeld: Okay, that’s well, that’s where the biggest gains are. Gonna come, we can get a 2030 reduction. You’re talking about a few 100 grand or more. 6 could be 500 grand. We would save this year.
244 00:35:30.860 ⇒ 00:35:32.100 bencohen: Oh, yeah.
245 00:35:32.630 ⇒ 00:35:34.410 Daniel Schonfeld: that’s that’s a major difference.
246 00:35:35.630 ⇒ 00:35:38.489 Uttam Kumaran: So yeah, let me see if I can get it by
247 00:35:38.840 ⇒ 00:35:42.910 Uttam Kumaran: by product class, II mean, II
248 00:35:43.160 ⇒ 00:35:53.950 Uttam Kumaran: II think I’m close. But yeah, I can send that over. Okay, yeah, just something to put in list. It doesn’t have to be done immediately, but that’s something that will enhance it. If we got a dropdown here. or
249 00:35:54.170 ⇒ 00:35:58.769 Daniel Schonfeld: and or a spark line, or something like that, that would go along.
250 00:35:59.310 ⇒ 00:36:02.860 Daniel Schonfeld: See where the different ones make up that pound.
251 00:36:03.300 ⇒ 00:36:28.279 Uttam Kumaran: Yeah, I’ll I’ll I’ll actually look at. We can almost be look at some sort of area chart of like which classes contribute to the entire pound. And yeah. You know. Again, I think about this. You know, we have a couple of different levers. Right? We have the zone. We have the the shipment provider and we have like, if there’s any additional fees which we’ve reduced pretty significantly, and so
252 00:36:28.470 ⇒ 00:36:39.010 Uttam Kumaran: just trying to normalize it across there. But I’ve now, because we had all these different shipment providers like, for example, even to just give you like a little context, we have
253 00:36:39.420 ⇒ 00:36:42.969 Uttam Kumaran: like all these sorts of service codes.
254 00:36:42.980 ⇒ 00:36:44.500 Daniel Schonfeld: Yeah. And
255 00:36:44.560 ⇒ 00:36:49.769 Uttam Kumaran: the one thing I’m trying to do is like, Okay, let’s bucket these into related services.
256 00:36:49.960 ⇒ 00:37:01.340 Uttam Kumaran: because some stuff may be really like they just, you know, there’s, of course, nuances. Not only just across Amazon, Fedex, but maybe like type. So that’s what I’m working on next, similar to how we segmented product class.
257 00:37:01.430 ⇒ 00:37:14.869 Uttam Kumaran: and that will have a we’ll have just a couple of variables to be able to kind of identify by zone, by product, class, and by shipment provider like, where? What’s the price per pound? Which is, I think, the nice normalization. So
258 00:37:15.240 ⇒ 00:37:24.860 Daniel Schonfeld: yeah, this will be very, very helpful also, with my analysis with Eunice. If we find that one of the zones is out of whack, we just can’t get good rates. That might be a good indicator
259 00:37:25.320 ⇒ 00:37:32.330 Daniel Schonfeld: for me to speak with them about opening up a different DC. And it’ll be easy for me to compare the rates when they give me theirs.
260 00:37:32.470 ⇒ 00:37:37.030 Daniel Schonfeld: you know, so calculating what it what it costs to get those goods there
261 00:37:37.080 ⇒ 00:37:40.359 Uttam Kumaran: potentially like, let’s say, California is the culprit
262 00:37:40.750 ⇒ 00:37:45.139 Daniel Schonfeld: and, like Ben was saying, maybe it’s a let’s say it’s 28 bucks.
263 00:37:45.210 ⇒ 00:37:53.980 Daniel Schonfeld: We may find it’s worth it to ship variable speeds direct from China right there to the port in California, but in a Unis, DC.
264 00:37:54.240 ⇒ 00:38:02.100 Daniel Schonfeld: And it would be much easier now for me to pull those as long as I can pull the raw data from here and say, Show me
265 00:38:02.390 ⇒ 00:38:07.810 Daniel Schonfeld: all shipments to California by class broken out by class is probably the analysis I’d want to do.
266 00:38:08.070 ⇒ 00:38:11.340 Daniel Schonfeld: and I’d probably suck out all the variable speeds.
267 00:38:11.650 ⇒ 00:38:19.529 Daniel Schonfeld: and then make a calculation based on that over 2 year period. Maybe I don’t know when the data starts here. It’s probably a year or so. Right?
268 00:38:19.990 ⇒ 00:38:24.209 Uttam Kumaran: Yeah, I would have to look at where the ship station stuff starts.
269 00:38:24.610 ⇒ 00:38:30.930 Daniel Schonfeld: Okay, well, we we can get a good sense. And at least create some kind of a model where I can say, Okay, it’s got it.
270 00:38:31.040 ⇒ 00:38:35.529 Daniel Schonfeld: you know. Be less than 28 bucks. If anything less than 20 bucks is. Gonna be
271 00:38:35.710 ⇒ 00:38:40.060 Daniel Schonfeld: gonna be gravy. And also we’ll have goods on the West Coast.
272 00:38:40.440 ⇒ 00:38:46.690 Daniel Schonfeld: you know, for more timely. I’m sure that that’s another factor is, how long does it take for it to get from the appank
273 00:38:46.790 ⇒ 00:38:51.509 Daniel Schonfeld: to California versus when it’s in California, we might be able to get same day versus 2 day.
274 00:38:51.750 ⇒ 00:39:18.820 Uttam Kumaran: Exactly. So we’ve we have. We’ve modeled out some of the shipping speeds, but has haven’t like brought anything into any charts. But that’s exactly right. We can. And then again, the nice thing about Cha, the ship station on our new like Etl service is that on the new like data services. We get everything. We get almost the dimensions for every package. So you can almost test out like, Hey, if this is the package with this dimensions go from here to here
275 00:39:18.860 ⇒ 00:39:37.279 Daniel Schonfeld: with this is this. These are the last 10 times that we did that right. So we’re almost gonna get really granular, which is really nice. Yeah, there’s a bunch of different data. We’re gonna all have to look at to make an informed decision. It’s not just gonna be a monetary decision. We may find that there’s more return suddenly, because packages are being damaged.
276 00:39:37.410 ⇒ 00:39:41.560 Daniel Schonfeld: We have to look at time to delivery. We may find that ups is
277 00:39:41.690 ⇒ 00:39:53.109 Daniel Schonfeld: maybe they say one or 2 days, but it actually gets there in 3. So there’s other ancillary components to making this decision. But this is definitely the very first start. Obviously, you want to save money, but not at the expense of the customer experience.
278 00:39:53.130 ⇒ 00:39:55.939 Uttam Kumaran: So that’s one thing I’m gonna be. I’m talking to Cody
279 00:39:56.490 ⇒ 00:39:59.250 Daniel Schonfeld: at 11 today, and I’m gonna make him aware
280 00:39:59.260 ⇒ 00:40:05.879 Daniel Schonfeld: if he’s not already, I’m sure he is, but that we’re using a new provider. We’re not a new provider. We’re we’re leaning more heavily on ups.
281 00:40:06.020 ⇒ 00:40:08.129 Daniel Schonfeld: and for him to also be in the loop.
282 00:40:08.480 ⇒ 00:40:24.509 Daniel Schonfeld: you know, to to communicate to all of us if he starts seeing an uptick in packages that typically weren’t damaged. Now they’re being damaged more often. You know, that’s something we have to be cognizant of once when putting more weight on a different provider.
283 00:40:25.430 ⇒ 00:40:29.939 Uttam Kumaran: Yeah. And I’ll even need to look at when we get the return reason.
284 00:40:30.300 ⇒ 00:40:41.729 Uttam Kumaran: we haven’t been using the return reason, Field. II don’t know whether I saw that it wasn’t like being filled out, or I don’t know but we’ll we’ll need to look at that more once we get dissected into the individual return.
285 00:40:41.940 ⇒ 00:40:47.420 Daniel Schonfeld: Yeah, II was looking at Cody’s. Cody sends a report. I was looking at his yesterday. He’s trying to automate stuff.
286 00:40:47.830 ⇒ 00:40:55.639 Daniel Schonfeld: I actually don’t know where he gets this information. I’m gonna talk to him. I’m speaking to him at 11 to try to better understand his world.
287 00:40:55.760 ⇒ 00:40:57.040 Daniel Schonfeld: with Zendesk.
288 00:40:57.190 ⇒ 00:41:02.390 Daniel Schonfeld: But I’m wondering if maybe there’s some kind of a connection there. Have you spoken to Cody at all.
289 00:41:02.720 ⇒ 00:41:09.300 Uttam Kumaran: I haven’t spoken to Cody
290 00:41:09.360 ⇒ 00:41:33.159 Uttam Kumaran: on on the Zendesk side. If it’s if it’s customer email, then it’s pretty easy to be linked, or if there’s an order. Id, then it’s pretty. The the thing is I’ve just noticed in working with previous people is like sometimes companies. They don’t enter in the return reason, or there, there’s like that’s the last part, but it’s in. It’s it’s there, it’s not in shopify. So it’s on.
291 00:41:33.440 ⇒ 00:41:38.170 bencohen: So th. The information is definitely somewhere. But this is, you know.
292 00:41:38.820 ⇒ 00:41:43.299 bencohen: Yeah, especially as we deal with warranties.
293 00:41:43.320 ⇒ 00:41:46.990 Daniel Schonfeld: The the different reasons are critical to us. We need to understand.
294 00:41:47.520 ⇒ 00:41:58.919 Daniel Schonfeld: For charging for an extended warranty. Eventually, people are gonna start claiming those. And what? At what rate, we can’t just go make any assumptions. Really, when it comes to those numbers, cause as the business grows
295 00:41:59.430 ⇒ 00:42:03.060 Daniel Schonfeld: and and it becomes more national.
296 00:42:03.420 ⇒ 00:42:17.759 Daniel Schonfeld: you know, those numbers could literally make a break. The entire company. If we’re if we’re if we get those numbers wrong of what we charge for warranties, how we warranty products on very expensive items. Again yesterday. That’s why today was a concern for me
297 00:42:17.800 ⇒ 00:42:20.449 Daniel Schonfeld: that we shipped out to heat pumps for free
298 00:42:20.590 ⇒ 00:42:27.240 Daniel Schonfeld: it could be indicative of a bigger problem. It could be an anomaly. I don’t know. It could be a new provider.
299 00:42:27.330 ⇒ 00:42:31.960 Daniel Schonfeld: Those are things that really send off alarms for me.
300 00:42:32.400 ⇒ 00:42:40.299 Daniel Schonfeld: To see if there anomalies or not. But that’s something that’ll be critical to to show in our reporting system. I have a big red flag saying
301 00:42:40.510 ⇒ 00:42:45.899 Daniel Schonfeld: there were 2 returns on a for $6,000. That’s something you wanna
302 00:42:46.220 ⇒ 00:42:56.709 Daniel Schonfeld: and that maybe in time have the system be intelligent enough. That’s where we get down the line to more learning machine learning. Is it looking at an or anomalies?
303 00:42:56.820 ⇒ 00:43:00.300 Daniel Schonfeld: And watching for them and saying, Okay, there were 2
304 00:43:00.360 ⇒ 00:43:01.789 Daniel Schonfeld: heat pumps today.
305 00:43:02.620 ⇒ 00:43:10.870 Daniel Schonfeld: There’s never been a return on that specific skew. But then tomorrow there’s one Thursday. There’s there’s one that the system would be intelligent enough to say
306 00:43:11.460 ⇒ 00:43:33.340 Daniel Schonfeld: in its 2 year. History there’s never been returns. Suddenly, in the last 4 days there’s been 5. This is something that should be a big red alert on the screen. So you need to look into this now, doesn’t be smart enough to understand why or how just needs to tell us that there is a problem to look into it, at least, for now. And that’s where this feedback loop, maybe from Zendesk
307 00:43:34.010 ⇒ 00:43:37.849 Daniel Schonfeld: would help us to to decipher
308 00:43:38.550 ⇒ 00:43:46.600 Daniel Schonfeld: if it was a discounted product, if it was a sale discount or a return slash warranty.
309 00:43:46.790 ⇒ 00:43:53.739 Uttam Kumaran: Yeah. And even if there’s just 2, you know, to be able to link directly here for you to go open the Zendesk ticket
310 00:43:53.800 ⇒ 00:43:56.410 Uttam Kumaran: and scan through right.
311 00:43:56.430 ⇒ 00:44:17.579 Uttam Kumaran: That’s we just need the URL, and then the order Id, or to do that. And that’s what I that’s what we’ve done, you know, done in the past. A lot even similar to one thing I’m planning on doing is trying to open up directly to the shopify order or the ship station order directly from line item, because it’s a lot of the reconciliation work that I do, I go and type in the order Id, and so
312 00:44:18.120 ⇒ 00:44:22.590 Uttam Kumaran: almost be able to just click in and open that. So on the map.
313 00:44:22.940 ⇒ 00:44:26.399 Daniel Schonfeld: Okay, great and as we’re building this again.
314 00:44:27.330 ⇒ 00:44:49.870 Daniel Schonfeld: I I’m all in on this with this system. I want it to be like the Holy Grail for us that we don’t go anywhere else. So if you need to have a backup person, cause I know we’re trying to build rapidly. But the data integrate to to me and to everybody, I’m sure, on this call is paramount above all else. Every number makes sense and is accurate and timely.
315 00:44:50.300 ⇒ 00:45:15.790 Daniel Schonfeld: If you need to have someone come in in Qa for a little bit every single day, or have an overseas company do that. But that sounds like something, for, like a virtual system or something like we can pay them a nominal fee for them to literally. You go in, you log into shop by pull this data, match it up and send a report every morning. I’m sure it’ll cost like 20 bucks for us to do that with a virtual assistant. Feel free to just do that because it’s it’s a waste of your
316 00:45:16.100 ⇒ 00:45:24.449 Daniel Schonfeld: your you know your time and your expertise, and mine too, for me to pull this that might actually be a good
317 00:45:24.570 ⇒ 00:45:27.940 Daniel Schonfeld: resolution for us is, have them go do that.
318 00:45:28.300 ⇒ 00:45:33.960 Daniel Schonfeld: you know, and and let let me know you think about that. Actually, that might actually be a good idea. Ben is to just
319 00:45:34.160 ⇒ 00:45:38.489 Daniel Schonfeld: have one of those virtual assistants go to that logging in.
320 00:45:38.580 ⇒ 00:45:46.819 bencohen: Yeah, I think it’s a great idea for 10 bucks an hour. You can have somebody spend 10 HA month, and that’s probably even more than necessary.
321 00:45:47.080 ⇒ 00:45:48.760 Daniel Schonfeld: Just every couple of days
322 00:45:48.990 ⇒ 00:45:56.719 bencohen: spot, check and make sure that we’re tied. Then you don’t have to worry about it. I was gonna say, for the first 30 day, just do the stuff that I do at 9 in the morning.
323 00:45:56.830 ⇒ 00:46:06.170 Daniel Schonfeld: Maybe they could just do that overnight. So when I get in at 9 is just done, it’s just an excel sheet in my inbox, it says, here we we logged into shopify. Here’s the raw number we logged into the discounts.
324 00:46:06.740 ⇒ 00:46:08.010 Daniel Schonfeld: Just we give them
325 00:46:08.330 ⇒ 00:46:21.260 Daniel Schonfeld: certain logins access, read only to shopify, etc. They go perform those very simple tasks. And it’s just in our inbox in the morning, and it goes to utam me, you
326 00:46:21.740 ⇒ 00:46:32.029 Daniel Schonfeld: whoever wants to be on a distribution list, and you can use that as your true North for the daily bootam. It’ll just be teed up for you, and you’ll you could. Just you have to go looking for that information.
327 00:46:32.390 ⇒ 00:46:57.109 Uttam Kumaran: Yeah, on the on the infra side. We know we’ve done a lot in the last 3 weeks on the alerting. And that’s actually helped, like, actually nip a lot of issues that would have came up. And so we’ve we’ve kept. We’ve caught a lot of things proactively. But you’re right, I think, especially for this like morning audit. I’ve been doing it, and then, like for a few days. It it’s all working, and then there’s an issue. So that’s something. Let me consider over the weekend, and then let me see if we can bring on to do that
328 00:46:57.110 ⇒ 00:47:04.149 Uttam Kumaran: even just again, just to say, Go to these 5 6 platforms, do the line by line math, and then
329 00:47:04.150 ⇒ 00:47:19.600 Uttam Kumaran: just take a just like again. Here are like things that shouldn’t be happening if there’s something happening, indicate like, here’s a here’s pretty much like the we typically call like a run book, for it’s like check. If the jobs are running check of the data has loaded check.
330 00:47:19.600 ⇒ 00:47:32.619 bencohen: There was a couple of emails we’re getting from like some of these vendors that something is down. So we’ll go through that checklist before saying like, Okay, there’s a larger issue. We need like a 24 h to figure out virtually unlimited
331 00:47:32.640 ⇒ 00:47:39.179 bencohen: people on like upwork or fiber that are more than qualified to to to take care of this.
332 00:47:39.440 ⇒ 00:47:40.620 Daniel Schonfeld: Yep.
333 00:47:41.180 ⇒ 00:47:42.819 Uttam Kumaran: okay, let’s take it on.
334 00:47:43.210 ⇒ 00:47:50.990 Daniel Schonfeld: Yeah, think about it. And like, but let’s just make a decision in the next by early next week on, if we’re gonna do this. I just don’t want to Linker, and I think it’d be helpful.
335 00:47:51.450 ⇒ 00:47:57.790 Daniel Schonfeld: But I wanted to make sure that it’s it is helpful. It’s not actually more of a nuisance.
336 00:47:57.990 ⇒ 00:47:58.780 Daniel Schonfeld: okay.
337 00:47:58.830 ⇒ 00:48:16.810 Uttam Kumaran: the final thing. Sorry. Last thing. And then I think I I talked to him yesterday and we re we recreated, although it’s it’s a little bit blend. We recreated just her weekly report here, and I met with her. And
338 00:48:16.810 ⇒ 00:48:35.469 Uttam Kumaran: she’s like, Yeah, I’m totally down to take this on for direct mail and affiliates. I’m still getting it manually. So she has a Google form that she can fill out. And that data goes straight to here, and she said it should save her like about few hours. Kind of putting this together.
339 00:48:35.700 ⇒ 00:48:38.890 Uttam Kumaran: so I met with her yesterday, and then I’m kind of gonna
340 00:48:38.960 ⇒ 00:48:50.340 Uttam Kumaran: come around back with her early next week for some feedback and kind of I’ll show her how to add stuff to this, but all of the data she has there is pretty much mimicked.
341 00:48:50.490 ⇒ 00:48:53.030 Uttam Kumaran: so seems that’s awesome.
342 00:48:53.510 ⇒ 00:48:57.260 Uttam Kumaran: She just said. She’ll she’ll try to run with this for the next meeting, I think.
343 00:48:57.370 ⇒ 00:49:02.250 Daniel Schonfeld: Yeah, let’s let’s ben the next meeting, the weekly meeting when we do it. Monday or Tuesday.
344 00:49:03.340 ⇒ 00:49:05.389 bencohen: Mine was Kim and Mike.
345 00:49:05.460 ⇒ 00:49:07.670 bencohen: Monday, 1130. I think it is.
346 00:49:07.790 ⇒ 00:49:23.799 Daniel Schonfeld: Alright. I’ll be on that. Let’s let’s have her use it right from here. Let’s or have her spot. Check it obviously before, to make sure it’s accurate with what she normally does. She’ll probably have to duplicate a little bit, not duplicate. So let’s do her normal report, but she should compare it to this. And if this starts working, let’s just
347 00:49:23.840 ⇒ 00:49:24.969 Daniel Schonfeld: we’ll go with this.
348 00:49:28.010 ⇒ 00:49:34.680 Daniel Schonfeld: Yeah, this is great. And I think also in the future, like you said, we can link this to different things if we want to look at the campaign right into it.
349 00:49:34.920 ⇒ 00:49:41.759 Daniel Schonfeld: But yeah, that’s we don’t need that today. But in the future I could see us doing that, or even just showing the creative that’s used like in a pop up
350 00:49:41.850 ⇒ 00:50:09.579 Uttam Kumaran: something like that. But again, that’s more nice to have down the road. So that so that’s exactly what I told her, I said. Just take a look. And then, if think about what the next thing you would do, and then I can let you know whether that’s possible really easily within here. But I think going to the Ui, especially on the outside. It’s like, really, it’s like, super quick. So, but this is pretty much all the same data. So at least recreating. That shouldn’t be an issue. And then I got access to impact. And so we’re still working through getting some of that data.
351 00:50:09.600 ⇒ 00:50:28.289 Uttam Kumaran: But I was like, let’s just let’s just continue to Google form to get a couple of data points in and flowing through here. And then I told her I said, I really like I read a lot of the insights stuff that she puts up in there. And so, even seeing that somehow but I was like, I think that’s that’s definitely like, more important. So
352 00:50:29.070 ⇒ 00:50:34.569 Daniel Schonfeld: alright, great job, Utah, this is awesome. Once once we get the data like really humming and accurate.
353 00:50:34.710 ⇒ 00:50:44.779 Daniel Schonfeld: This thing’s gonna be a beast, and then we can have, like a Ui designer type, come in and make it a little more flashy and more, little more intuitive. But right now
354 00:50:44.860 ⇒ 00:50:49.290 Daniel Schonfeld: raw data is what I care about the most, and it being again accurate and timely
355 00:50:50.020 ⇒ 00:50:56.219 Uttam Kumaran: awesome. Great. Thank you. And I will send. I’ll send you this
356 00:50:56.270 ⇒ 00:51:02.269 Uttam Kumaran: video so you can check it out. And then, yeah, I’ll follow up early next week with some actions.
357 00:51:02.540 ⇒ 00:51:07.329 Daniel Schonfeld: Awesome, Buddy. Thank you so much. Talk to me about that.