Meeting Title: Revenue Mart Review Date: 2025-07-31 Meeting participants: Uttam Kumaran, Emily Giant, perry
WEBVTT
1 00:00:05.720 ⇒ 00:00:08.210 Uttam Kumaran: Hello! Sorry for the delay.
2 00:00:08.730 ⇒ 00:00:09.320 perry: Hi!
3 00:00:10.970 ⇒ 00:00:11.720 perry: Oh, peace!
4 00:00:12.660 ⇒ 00:00:14.179 Emily Giant: Hi! Oh, wow!
5 00:00:14.180 ⇒ 00:00:14.820 Uttam Kumaran: Hi.
6 00:00:14.820 ⇒ 00:00:15.509 Emily Giant: The rustic.
7 00:00:15.510 ⇒ 00:00:16.640 Uttam Kumaran: Backgrounds.
8 00:00:16.640 ⇒ 00:00:17.190 perry: Yeah.
9 00:00:17.330 ⇒ 00:00:20.249 Emily Giant: It’s real, too. You can tell by the reflection in the door.
10 00:00:21.140 ⇒ 00:00:25.889 perry: That’s I had to. I had to. I have to move down the table as the day goes on, because the sun.
11 00:00:26.020 ⇒ 00:00:29.259 Emily Giant: Show me the Tetons one more time. They’re like my heart.
12 00:00:30.320 ⇒ 00:00:31.360 Uttam Kumaran: Yeah.
13 00:00:31.360 ⇒ 00:00:32.350 Emily Giant: So beautiful.
14 00:00:32.350 ⇒ 00:00:34.970 perry: Which really the most important is these 2.
15 00:00:35.270 ⇒ 00:00:36.160 perry: Yes.
16 00:00:36.720 ⇒ 00:00:38.500 Emily Giant: Are those both King Charles.
17 00:00:38.780 ⇒ 00:00:40.880 Uttam Kumaran: Yeah, they are. This is Montauk.
18 00:00:41.200 ⇒ 00:00:41.760 Emily Giant: Yeah, this is.
19 00:00:41.760 ⇒ 00:00:44.689 perry: And that’s Charlie over there.
20 00:00:44.690 ⇒ 00:00:46.619 perry: Oh, my God!
21 00:00:46.620 ⇒ 00:00:53.950 perry: Were sitting over on the couch. They like to sunbathe like cats. Normally they’re yapping away. I’m so impressed that they haven’t yapped at all in like 40.
22 00:00:53.950 ⇒ 00:00:55.989 Uttam Kumaran: Run towards the mountains forever, you know.
23 00:00:55.990 ⇒ 00:00:58.090 perry: They have little electric fence collars
24 00:00:58.580 ⇒ 00:01:02.300 perry: scared of them, which is good, because otherwise they would not be outside. Right now with me.
25 00:01:03.210 ⇒ 00:01:06.699 Emily Giant: Can we have a place in Montauk that’s like another like I live.
26 00:01:06.968 ⇒ 00:01:16.371 perry: We don’t. My! So these are my aunt, my dad’s sister’s dogs. And they have a place out in Amagansett. So these are their dogs. Yeah. So them and their 3 kids.
27 00:01:16.910 ⇒ 00:01:20.839 perry: no, our place is in Annapolis. That’s where our water. That’s where we go.
28 00:01:21.800 ⇒ 00:01:31.170 Emily Giant: Place your water place there or jobs there. Yeah, I met, I actually met Matt in Montauk at liars. I was.
29 00:01:31.170 ⇒ 00:01:32.560 perry: No way.
30 00:01:32.790 ⇒ 00:01:35.370 Emily Giant: Yeah, that’s so funny.
31 00:01:35.370 ⇒ 00:01:38.729 Emily Giant: If you ever make it to Montauk you will wind up at liars.
32 00:01:38.730 ⇒ 00:01:39.290 Uttam Kumaran: Yeah.
33 00:01:39.290 ⇒ 00:01:43.540 Emily Giant: You want to or not. Also memory motel, which is disgusting, but.
34 00:01:43.710 ⇒ 00:01:46.069 Uttam Kumaran: Flyers kind of like losers in Nashville.
35 00:01:47.000 ⇒ 00:01:48.030 Emily Giant: Probably.
36 00:01:48.030 ⇒ 00:01:48.850 Uttam Kumaran: Yeah.
37 00:01:49.070 ⇒ 00:01:51.160 Emily Giant: Why are notable. Notable. Bar?
38 00:01:51.160 ⇒ 00:02:03.700 Emily Giant: Yeah. Well, it’s our carpet, and it’s a fisherman’s bar, and it’s not the weekenders it’s for, like the real fishermen. So you walk, and you’re just like, Oh, my God! But they do karaoke every Friday, and I took it like super seriously. When I lived there. It was like 10.
39 00:02:03.700 ⇒ 00:02:04.580 perry: You should.
40 00:02:04.910 ⇒ 00:02:09.030 Emily Giant: But I was doing like a killer. Britney spears, impression, and prize.
41 00:02:09.030 ⇒ 00:02:09.629 perry: Oh, my God!
42 00:02:09.979 ⇒ 00:02:13.459 Emily Giant: That’s his history, like I think I can do it like.
43 00:02:13.460 ⇒ 00:02:14.940 perry: Fell in love with you right then and there.
44 00:02:14.940 ⇒ 00:02:15.670 Emily Giant: I don’t know.
45 00:02:15.670 ⇒ 00:02:17.396 Uttam Kumaran: Yes, yes.
46 00:02:18.260 ⇒ 00:02:19.409 perry: Oh, my God!
47 00:02:20.000 ⇒ 00:02:20.834 Emily Giant: Sorry.
48 00:02:21.670 ⇒ 00:02:27.380 perry: That’s the funny one, though. Yell running around, yelling Monty! Monty is really like a entertaining thing.
49 00:02:27.700 ⇒ 00:02:30.510 Emily Giant: They are cute and chill.
50 00:02:30.690 ⇒ 00:02:34.040 perry: They’re older now. They’re like 7 or 8 or something like that.
51 00:02:34.680 ⇒ 00:02:38.395 Emily Giant: That thin mountain air. They’re probably just like.
52 00:02:38.860 ⇒ 00:02:41.810 perry: 5 min ago, sun basking. So I’m guessing they’re a little hot right now, which is why they.
53 00:02:41.810 ⇒ 00:02:43.090 Uttam Kumaran: Yeah, they’re overheated.
54 00:02:44.730 ⇒ 00:02:49.722 Emily Giant: Well, I am deeply jealous. I have not been out there for like a couple of years, but
55 00:02:50.190 ⇒ 00:02:51.830 Emily Giant: I’ll get back there soon.
56 00:02:51.830 ⇒ 00:02:55.249 perry: Getting so crowded honestly like I was there last summer.
57 00:02:55.420 ⇒ 00:03:12.130 perry: and my aunt had my other aunt rented a place in Amiganza just for like 2 weeks, and we honestly just stay in the house. I was like, I don’t want to go to town. I was like, it’s so chaotic and like overrun. I was like, I don’t want to fight my way through dinner. I just literally want food like I don’t care. I just need.
58 00:03:12.130 ⇒ 00:03:15.940 Emily Giant: Air drop all of Brooklyn there on Friday, Saturday, Sunday.
59 00:03:15.940 ⇒ 00:03:19.570 perry: All of Brooklyn, half of Miami, half of California. It’s fucking.
60 00:03:19.570 ⇒ 00:03:22.329 Uttam Kumaran: Yeah, it’s all figured out place now, like, I don’t know.
61 00:03:22.980 ⇒ 00:03:23.410 Uttam Kumaran: Okay.
62 00:03:23.410 ⇒ 00:03:24.820 Emily Giant: There’s nowhere else to go like it’s.
63 00:03:24.820 ⇒ 00:03:29.029 Uttam Kumaran: And it’s hard to get there. It’s a pain to get to New York. Yeah.
64 00:03:29.030 ⇒ 00:03:44.470 perry: Whole thing with like surf lodge and all that. And I’m like there are like local places that definitely don’t get as crowded. But I’m just like we’ve turned all these places that they’re fine restaurants. They’re good restaurants, but like they’re not worth this amount of money like, it’s a pretty sunset on a bay. Everybody. It’s a pretty sunset on.
65 00:03:44.470 ⇒ 00:03:48.969 Uttam Kumaran: But it’s also see, I grew up on the west coast, and it’s like an East coast
66 00:03:49.160 ⇒ 00:03:51.340 Uttam Kumaran: beach which is like not like
67 00:03:51.340 ⇒ 00:03:53.930 Uttam Kumaran: real beach just like it’s not a real beach.
68 00:03:53.930 ⇒ 00:04:01.109 Uttam Kumaran: If you go to like any beach in California, you’re kind of like. Oh, my God! This like you go to San Diego. You’re like, Oh, my God! This is incredible!
69 00:04:01.340 ⇒ 00:04:11.189 perry: That’s the thing that always like. That’s a part of it that makes me so confused that I’m like all these influencers are like. Oh, my God! I must go! I must go, and I’m like, but you all are rich enough that you all go to like.
70 00:04:11.190 ⇒ 00:04:14.289 Uttam Kumaran: They’re in La in like Malibu or Miami Beach.
71 00:04:14.290 ⇒ 00:04:22.070 perry: Not by tart to be in the Bahamas, and yet you think I know you’ve seen a real beach. I know you’ve seen a real thing. I don’t know why we’re so.
72 00:04:22.070 ⇒ 00:04:25.349 Uttam Kumaran: Cloud of the tag. Yeah, being able to tag it that you’re there.
73 00:04:25.350 ⇒ 00:04:36.190 Emily Giant: Yeah, no, it’s it’s so overhyped, so, so so. But again I was there like 10 years ago. So I and I would live there from like March to like October.
74 00:04:36.190 ⇒ 00:04:59.080 Emily Giant: because I would run stores and like go back and forth during the weekend. And it was so. I had a job that kept me on the straight and narrow, but like I got it down to a science one summer and was like, I’m going to produce a play. Because why not? I have time. And I wound up doing like a children’s theater production at the Memory Motel, because I was Bffs with the owner, and he was like, I don’t care sure nothing happens here in the day so like
75 00:04:59.150 ⇒ 00:05:10.010 Emily Giant: the amount of like pissed office that, like a children’s play, was being done at the memory motel like I wasn’t trying to cause like town drama, but they’re all.
76 00:05:10.010 ⇒ 00:05:11.460 perry: Inadvertently cause, town drama.
77 00:05:11.460 ⇒ 00:05:15.459 Emily Giant: Board that like they’ll talk about anything, and like the memory motels.
78 00:05:15.660 ⇒ 00:05:17.740 Uttam Kumaran: Sued for noise, pollution like.
79 00:05:18.180 ⇒ 00:05:39.770 Emily Giant: By 15 different like townships. And anyway, it was really really fun. But like, I wonder if your aunt may have heard the gossip about like the the upstart from New York or Brooklyn, that, like produced a play at the dirtiest, most disgusting place I had. The bartenders come in. Parents were like getting wasted during Hansel and Gretel. It was great.
80 00:05:39.770 ⇒ 00:05:44.389 perry: Oh, my God, that’s hilarious! I’ll have to ask her when they get back.
81 00:05:45.690 ⇒ 00:05:47.459 Emily Giant: There’s no way they. There’s no way they remember.
82 00:05:47.460 ⇒ 00:05:51.880 perry: Well, my cousin would have been 9 like my youngest could they would have been like 9, 10, and 12.
83 00:05:52.280 ⇒ 00:05:54.679 Emily Giant: You should ask just to see.
84 00:05:55.080 ⇒ 00:05:57.889 perry: They might have been. That’s the thing is, I can’t remember when they came back from London, though
85 00:05:58.600 ⇒ 00:06:00.489 perry: I live in London for a couple years.
86 00:06:00.490 ⇒ 00:06:04.949 Emily Giant: I just can’t believe how old I am like every day I’m like, how did this happen?
87 00:06:05.320 ⇒ 00:06:13.230 perry: My youngest cousin can legally drink. I’m going to be 31, and I, despite the fact that that’s really only 2 years away, I can’t handle it
88 00:06:13.640 ⇒ 00:06:15.000 perry: doesn’t sit with me.
89 00:06:15.120 ⇒ 00:06:15.750 Emily Giant: No.
90 00:06:16.610 ⇒ 00:06:17.300 perry: I like it.
91 00:06:17.300 ⇒ 00:06:19.649 Emily Giant: You’re not. You’re not 30 yet, are you? You’re 29.
92 00:06:19.650 ⇒ 00:06:20.640 perry: Until march.
93 00:06:20.640 ⇒ 00:06:23.319 Emily Giant: Okay, you’re gonna love it. The 30.
94 00:06:23.320 ⇒ 00:06:27.549 perry: I just really don’t like the fact that she’s gonna be 21. And I’m gonna be 31 when I don’t.
95 00:06:28.080 ⇒ 00:06:35.169 perry: It’s a it’s a i don’t feel like I’m that much older than her. You know what I mean. It just doesn’t match my perception of our age. Gap.
96 00:06:35.170 ⇒ 00:06:42.530 Emily Giant: And you probably look the same. But once you hit like 36, it’s like suddenly, you’re like, Oh, my God! Why do I? Why am I like concave
97 00:06:42.720 ⇒ 00:06:45.589 Emily Giant: like? Why don’t I have any collagen left in my face?
98 00:06:45.846 ⇒ 00:06:46.359 Uttam Kumaran: Like that.
99 00:06:46.360 ⇒ 00:06:49.000 Emily Giant: Like cute and cheeky, just like like.
100 00:06:49.000 ⇒ 00:07:03.229 perry: That’s the one thing I haven’t been a size 0 since I was 14 years old. And so that’s the thing my face doesn’t really like. I just carry a lot of babyface. I literally looked like I was 16 until I was about 25. I didn’t like. I literally none of my baby fat went away until, like 5 years ago.
101 00:07:03.230 ⇒ 00:07:06.610 Emily Giant: Yeah, keep carrying a girl like the time you still have, like a bunch of collagen in your.
102 00:07:06.610 ⇒ 00:07:07.780 Uttam Kumaran: I do. I do.
103 00:07:09.060 ⇒ 00:07:09.830 Uttam Kumaran: Yeah.
104 00:07:09.830 ⇒ 00:07:14.440 Emily Giant: Like something happens when you get like in the upper thirties. You just start like getting all like.
105 00:07:14.440 ⇒ 00:07:14.880 perry: He goes.
106 00:07:14.880 ⇒ 00:07:17.299 Emily Giant: And like Madonna sinewy, it’s not.
107 00:07:17.620 ⇒ 00:07:19.140 perry: Surveying his kingdom.
108 00:07:19.290 ⇒ 00:07:20.119 Uttam Kumaran: He just hangs.
109 00:07:21.114 ⇒ 00:07:24.960 Emily Giant: Sorry, Tom. I know you’re busy, but sometimes you just have to talk shop.
110 00:07:24.960 ⇒ 00:07:31.890 Uttam Kumaran: No, this is fine. This is great. I think this will be a quick meeting, because we we’ve had a lot of conversations about this stuff, anyway. So
111 00:07:34.210 ⇒ 00:07:36.449 Uttam Kumaran: yeah, I have. So I have this, Doc
112 00:07:36.750 ⇒ 00:07:38.930 Uttam Kumaran: Perry. I just sent it in
113 00:07:39.360 ⇒ 00:07:45.210 Uttam Kumaran: zoom. If you scroll sort of down towards the middle. So it should be.
114 00:07:45.210 ⇒ 00:07:45.580 perry: Yeah.
115 00:07:45.580 ⇒ 00:07:46.450 Uttam Kumaran: Dock.
116 00:07:46.580 ⇒ 00:07:53.780 Uttam Kumaran: There’s like kind of a list of questions that we’re gathering, and from sort of all the team generally about
117 00:07:54.200 ⇒ 00:08:01.220 Uttam Kumaran: revenue reporting. Maybe I’ll give you just like a couple of minutes to scan through.
118 00:08:01.370 ⇒ 00:08:01.900 Uttam Kumaran: Yeah.
119 00:08:01.900 ⇒ 00:08:03.499 perry: Emily Emily actually shared with me.
120 00:08:03.500 ⇒ 00:08:03.920 Uttam Kumaran: Okay. Cool.
121 00:08:03.920 ⇒ 00:08:23.080 perry: On. We hopped on the other day, and then it turned into just a chit chat. But she did share this with me just so I could review it ahead of time. So I wasn’t like caught really off guard, I think. A lot of this. We’ve covered so you just have, like forecast by skew, actual. There’s forecast variance skews overstocked or at risk. That’s like
122 00:08:24.020 ⇒ 00:08:26.460 perry: the Dean. Put these in, or what did.
123 00:08:30.400 ⇒ 00:08:36.146 Uttam Kumaran: We may. We probably put it in from either past notes or yeah. The team.
124 00:08:36.530 ⇒ 00:08:36.950 perry: Okay.
125 00:08:36.950 ⇒ 00:08:38.019 Uttam Kumaran: Put them in. I don’t think Dean.
126 00:08:38.020 ⇒ 00:08:43.729 perry: No, just the way it reads. It’s just the way it reads. It reads like Dean cause. It’s like one of those things that it’s like.
127 00:08:47.340 ⇒ 00:08:49.310 perry: Are we fulfilling orders on time?
128 00:08:49.510 ⇒ 00:08:50.109 perry: That’s like.
129 00:08:50.110 ⇒ 00:09:01.779 Uttam Kumaran: So so, yeah, yeah, I get what you mean. Some of the the reason why we’re sort of at this level is because I want the team to track which table is going to solve this problem.
130 00:09:01.780 ⇒ 00:09:02.450 perry: Yeah, yeah, yeah.
131 00:09:02.450 ⇒ 00:09:10.930 Uttam Kumaran: Versus do the tables first, st and then think about the questions later. So literally as when we write the tickets for these tables, it’s like.
132 00:09:11.030 ⇒ 00:09:14.950 Uttam Kumaran: which question is this gonna answer? So that’s the main thing.
133 00:09:15.160 ⇒ 00:09:16.899 perry: I think a lot of the stuff like
134 00:09:17.040 ⇒ 00:09:19.110 perry: admittedly one of the benefits of
135 00:09:19.610 ⇒ 00:09:40.290 perry: the fact that everyone else seemed to not be submitting tickets for the last 6 months is that I’ve been the only one submitting tickets for, like the last 6 months to some degree. So a lot of my stuff is like fine. I think a lot of the the inventory stuff is in a place it needs to be. I think these are like kind of the right questions. I think my biggest thing that I would add to this list is we need to be able to tell sales
136 00:09:40.400 ⇒ 00:10:04.740 perry: prior to forced upgrade and after forced upgrade. So what did the customer actually order originally? And then what was ultimately fulfilled by the company? Those are 2 different sales perspectives. So if during mother’s day we have an issue with Peony Sunrise and a hundred, 300 of those orders have to get upgraded to double palazzos like happened a couple of years. When we look at our data right now, we reflect those 200 or 300 palazzos.
137 00:10:04.870 ⇒ 00:10:09.959 perry: And so I’m gonna plan as if those palazzos got sold to customers, when in reality the customer didn’t pick that.
138 00:10:09.960 ⇒ 00:10:12.129 Uttam Kumaran: So there isn’t another column that’s like
139 00:10:13.980 ⇒ 00:10:18.409 Uttam Kumaran: the whatever 1st order selected order, and, like the actual delivered.
140 00:10:19.900 ⇒ 00:10:25.830 Emily Giant: Different data sets. But they’re not in looker in a way that’s like usable, the way that
141 00:10:26.030 ⇒ 00:10:27.100 Emily Giant: the team needs it.
142 00:10:27.100 ⇒ 00:10:34.289 perry: Had built a measure before he left for merge. That was like orders like before, like non-forced upgrade orders.
143 00:10:34.290 ⇒ 00:10:34.940 Emily Giant: Hmm.
144 00:10:34.940 ⇒ 00:10:36.750 perry: So I know he started some of that work.
145 00:10:38.710 ⇒ 00:10:41.419 perry: I think we need to come to alignment on like
146 00:10:42.770 ⇒ 00:10:49.145 perry: what? When we talk about like total revenue generating orders by skew, is it? Pre or post?
147 00:10:49.610 ⇒ 00:11:08.520 perry: forced upgrade? I think it should be post, because that’s actually what got fulfilled. And that’s what actually went out the door. The prior is kind of like a demand thing, and so if you do it post, it might not necessarily tie, and then it’s kind of like messy which I think is how we do things now. Let me just look at this. I think weekly forecasts by skew.
148 00:11:08.590 ⇒ 00:11:30.837 perry: what is our actual 1st forecast variance? One area is subscriptions. By Geo, that’s something that we’ve been missing since the relaunch. That’s how we plan. The subscription units that we add to our forecast. So right now, we’re just kind of like sweeping it across the net network. But that ideally would be more specific. I have an example link of that that I can ask Steph to pull up and find
149 00:11:32.400 ⇒ 00:11:35.769 perry: Are we fulfilling on time which skews are overstocked or at risk. Yeah.
150 00:11:36.220 ⇒ 00:11:59.100 perry: I think when skews stock out, we used to have something called the in stock model which had weighted in stock rates and stuff like that. I would love to be able to bring that back. That says like, what are your in stock rates? What were your sell through by day, and that’s kind of like a timestamping mechanism that we never really were able to implement because of buffer release. So like you would sell out a Tuesday, and then buffers would get released. So then you’d actually sell out Wednesday.
151 00:11:59.100 ⇒ 00:12:21.439 perry: and then that would it would Update and override it, and so you would see like, oh, like so if you got buffers released, but then didn’t sell them till Friday. You might have sold out on Tuesday and had 5 buffer units all the way till Friday. And we what we really want to keep track of is that Tuesday action, that it was basically everything but that. So something that would help us answer that kind of question, like when skews are stocking out is a skew habitually stocking out early stuff like that would be helpful.
152 00:12:21.726 ⇒ 00:12:25.160 perry: Those are. That’s, I think, a business question that would be good.
153 00:12:25.545 ⇒ 00:12:27.560 perry: So by category, we have that
154 00:12:29.440 ⇒ 00:12:32.790 perry: weeks of supply. I’ll defer to Felipe on that, because that’s kind of his world
155 00:12:33.540 ⇒ 00:12:37.869 perry: cancellations and plaque supply forecasts. What did the customer order? Yeah.
156 00:12:38.630 ⇒ 00:13:01.789 Emily Giant: And Tom a lot of what she was saying with the like. The initial conversation you and I had when I was like. These are the business cases that like we need brain forge, because, like I can’t with what we’ve got. But like back when I was in Columbia, and you and I chatted through like the component data tables, and like this is fulfilled, and we need a column, for like this is what was fulfilled. This is what was ordered like. I don’t know if we still have notes on that meeting, but I think that one really encapsulates like what is missing from
157 00:13:01.840 ⇒ 00:13:14.245 Emily Giant: forecast analysis, and and you can find pieces of it. But it’s not clear. And then snapshot data we do have captured in a ticket because it’s been requested by every single like stakeholder.
158 00:13:14.760 ⇒ 00:13:16.290 Emily Giant: across the business.
159 00:13:16.480 ⇒ 00:13:17.090 Uttam Kumaran: Okay.
160 00:13:18.270 ⇒ 00:13:23.713 perry: Yeah, I think as I’m just kind of reading through some of the marketing stuff also for inspiration, too.
161 00:13:24.190 ⇒ 00:13:34.369 perry: I think if we could get to a point where skew sales by channel is something that we could have or what campaign they came in through. That would be really helpful for our team, understanding what
162 00:13:34.370 ⇒ 00:13:54.919 perry: a good example is like the Margo it’s in the wire cutter article. And so when wirecutter fluctuates, we see fluctuations in the Margot sales. And so that would really help us be able to say, like, Hey, like we’re seeing trailing sales on the Margo because of wirecutter. We don’t think that’s like I can go to marketing. Say, do you think this is going to get better? They can say yes or no, and then we can adjust our forecast. Otherwise it’s just like
163 00:13:54.920 ⇒ 00:14:02.190 perry: where, like, what’s changing about like the traffic aspect of how these Skus are selling so that kind of ties to theirs.
164 00:14:06.650 ⇒ 00:14:08.370 perry: okay.
165 00:14:09.500 ⇒ 00:14:12.919 perry: Promo logic. What subscriptions? Oh, God!
166 00:14:13.870 ⇒ 00:14:27.649 perry: A lot of their stuff, I think, is also like in the subscription stuff definitely wanted to be able to like go down to the skew level. So any of their subscriptions, like tiers. By Geo. By skew, all those kind of cuts. So it’s just more of an extension of their stuff.
167 00:14:27.650 ⇒ 00:14:28.230 Uttam Kumaran: Yeah.
168 00:14:29.782 ⇒ 00:14:32.199 perry: A long time ago we used to have.
169 00:14:34.470 ⇒ 00:14:36.020 perry: How did it go
170 00:14:41.120 ⇒ 00:14:45.120 perry: trying to remember this? I pulled. It was so long ago, and it’s just like it was. It was.
171 00:14:47.360 ⇒ 00:14:49.239 perry: Where’s the other dog, Charlie?
172 00:14:52.570 ⇒ 00:14:54.991 perry: I have to go find him in a minute.
173 00:14:57.330 ⇒ 00:15:09.550 perry: Like. There was a there was a looker, link that I was. I was I was in, and I think it was old, like cat Roush stuff that Steven worked on. It was kind of like in the retention cohort world, or like 1st order, second order. But it was basically
174 00:15:09.730 ⇒ 00:15:15.770 perry: someone’s 1st order being the unicorn, like when the 1st order was the product that they got with the Unicorn.
175 00:15:16.640 ⇒ 00:15:22.760 perry: What their then cohort like, what the size of that cohort, what was over time, so you could see, like of the people that bought
176 00:15:23.320 ⇒ 00:15:24.913 perry: the Unicorn.
177 00:15:25.900 ⇒ 00:15:48.169 perry: how people came back over time versus like, if you did the entire assortment, you could see that like. Oh, the people who bought the Unicorn is their their 1st product actually had a lower like month to month retention rate, like they actually dropped off way more than when you just look at all the products in the assortment. That was something that was a really long time ago that you might be able to find, dig up somewhere.
178 00:15:48.200 ⇒ 00:16:07.413 perry: and we could kind of adjust it or modified it to work currently. But that, I think, would be interesting to be able to see, especially as like we have quality concerns and stuff like that, like tracking people who got the Margo and Mother’s day or May, and being able to say like, Oh, did we send a bunch of people bad product? And did it really impact our ability to like bring people back after that.
179 00:16:09.540 ⇒ 00:16:10.880 perry: so I think those are.
180 00:16:12.290 ⇒ 00:16:14.490 perry: That’s kind of everything that comes to mind right now.
181 00:16:19.510 ⇒ 00:16:20.339 perry: I think.
182 00:16:20.340 ⇒ 00:16:25.920 Uttam Kumaran: Okay, cool. Yeah. Probably the only thing that was heavier than I expected was just all of everything around subscriptions.
183 00:16:26.410 ⇒ 00:16:30.510 Uttam Kumaran: But otherwise, yeah, I think doing cohorts by 1st order, type,
184 00:16:32.690 ⇒ 00:16:36.780 Uttam Kumaran: and then, yeah, starting to really get a good grasp on
185 00:16:36.950 ⇒ 00:16:44.779 Uttam Kumaran: dates. And then your flag on what was, what did they get? What did they actually get sent versus? What do they buy? Yeah.
186 00:16:44.780 ⇒ 00:17:07.730 perry: Yeah, because I would in a perfect world, I’d love to be able to flip a lot of our inputs to the non forced upgrade stuff so that we just don’t plan into skews. That were because the reality of a forced upgrade is the reason we then a lot of the times, especially during peaks. We tell care to use a product that’s not selling. So it undercuts the it basically overstates the performance of a product that’s not selling, anyway. And so you’re doubling down on the issue. Then next year you might over plan a product that didn’t sell
187 00:17:08.123 ⇒ 00:17:18.980 perry: cause you’re relying on institutional knowledge of which I’m usually perfectly fine at handling. But I don’t want that to be a business baked in thing, because that’s not smart, and I might lose my mind eventually.
188 00:17:19.190 ⇒ 00:17:30.389 Uttam Kumaran: Yeah. So ideally, we wanna we wanna have both. You want to have like what was actually sent. And the if there was, if there was an upgrade, yes or no. What was the upgrade to? And then you could do whatever you need. Yeah.
189 00:17:30.390 ⇒ 00:17:31.270 perry: Yeah,
190 00:17:34.950 ⇒ 00:17:44.044 perry: I think that’s everything. I’ll ask Steph, too. If you guys don’t mind, I’ll share this with her. And kinda like, get her thoughts on it. She’ll be in office next week. I’m out starting tomorrow.
191 00:17:44.860 ⇒ 00:17:49.539 perry: but she should be able to give you guys kind of some. If there’s anything that I missed
192 00:17:53.490 ⇒ 00:17:56.370 perry: And do you guys have time with Felipe set because his stuff is a little different than mine?
193 00:17:56.370 ⇒ 00:17:57.560 Uttam Kumaran: We did. We already met with him. Yeah.
194 00:17:57.560 ⇒ 00:18:01.519 perry: Okay, cool. Yeah, perfect. You’re on mute, Emily. If you’re talking.
195 00:18:05.160 ⇒ 00:18:15.930 Emily Giant: Do we need to review anything with like when a kit is broken, and how revenue works there like, I know that that was kind of an issue. For example, like there’s a kit.
196 00:18:16.760 ⇒ 00:18:24.649 Emily Giant: it’s a Flr dash, K, and then send a different bouquet. It will still say the kit was sent because portions of the kit were sent.
197 00:18:24.780 ⇒ 00:18:29.830 Emily Giant: But then a different bouquet is this skewing revenue, and
198 00:18:31.650 ⇒ 00:18:47.070 Emily Giant: I can check if it’s skewing revenue. But essentially I want to know if like, should there be a is there a need for like was a kit? Is a broken kit that was sent like in pieces, or some kind of flag to indicate that, like.
199 00:18:48.340 ⇒ 00:18:49.080 perry: Yeah.
200 00:18:49.080 ⇒ 00:18:50.750 Emily Giant: Reporting on that level.
201 00:18:50.750 ⇒ 00:18:54.590 perry: What I want. The answer to be is that care just doesn’t touch those orders. But that’s unrealistic.
202 00:18:54.590 ⇒ 00:18:55.105 Emily Giant: Right.
203 00:18:56.300 ⇒ 00:18:58.390 Emily Giant: Sometimes it just be like that.
204 00:19:02.340 ⇒ 00:19:03.530 perry: Let me noodle on that.
205 00:19:03.530 ⇒ 00:19:04.240 Emily Giant: Okay.
206 00:19:06.940 ⇒ 00:19:08.199 perry: Let me noodle on that.
207 00:19:09.710 ⇒ 00:19:13.079 Uttam Kumaran: And then do you happen to know anything about Markdowns versus discounts.
208 00:19:14.130 ⇒ 00:19:17.489 perry: Boy? Do I, boy? Do I.
209 00:19:17.710 ⇒ 00:19:21.209 Uttam Kumaran: Is, is our markdowns considered
210 00:19:21.410 ⇒ 00:19:25.510 Uttam Kumaran: like, do they take the the markdown price
211 00:19:25.650 ⇒ 00:19:28.139 Uttam Kumaran: for revenue, or do they take their original price.
212 00:19:28.140 ⇒ 00:19:29.400 perry: So
213 00:19:30.770 ⇒ 00:19:48.509 perry: like when we discount the firecracker. Firecracker reels, retails at 74. If we just put it on sale at 70, like we just change this price on site. That is just a hit to gross revenue. We just never capture those dollars. We were never going to capture those dollars. So doubles and triples that full fledged price of single times 3.
214 00:19:48.660 ⇒ 00:20:11.470 perry: That is not what we collect in gross revenue, we collect the pre discounted price. So if a firecracker is 222, and then we would retail that for probably I think it’s a 15% discount. So we actually, the customer’s probably paying $189, 189 is what hits gross.
215 00:20:11.470 ⇒ 00:20:30.720 perry: If the customer has a discount code on top of it, we would reflect 189 on Gross, and the discount would be the promo code. So if they got 10% off, you would take 10% of 189, that would be your discount value. But that 222 that red strike through price. That’s imaginary. It, doesn’t it? Never! It’s just gross revenue. We were never gonna capture. We’re
216 00:20:31.340 ⇒ 00:20:33.099 perry: lying to the customer for all intensive.
217 00:20:33.100 ⇒ 00:20:34.150 Uttam Kumaran: Yeah, yeah, okay.
218 00:20:34.424 ⇒ 00:20:41.285 perry: And then so like, if they just decide to put the Juliet on sale for 60 bucks you do 60 is just your gross revenue.
219 00:20:41.620 ⇒ 00:21:04.409 perry: discounts are only when codes credits anything like that is applied. It’s like you have to use a code to get that lower price is what’s in the discount and promotion world, the difference between different discount and promotion entirely up to what marketing wants to do it. Promotion is historically anything that is code driven. So P. And E. 50 welcome flow. All those things that are code driven are all promotions.
220 00:21:04.510 ⇒ 00:21:27.370 perry: Discounts were a little bit of a black box for everybody. It was kind of a Steven thing, something like employee discounts automatic when you get an email, if you’re in a customer group and your email associates, an automatic discount. So all of our employee discounts. That’s automatic. You don’t have a code to do it. It’s associated to your email. That was my general understanding historically, of how we separated those 2 buckets, but it’s up to them on how they want that
221 00:21:27.370 ⇒ 00:21:49.930 perry: within the promotional scheme of things, how they want that all to report but our general barrier was anything. Code driven was a promo and anything automatic to emails, vip investors, stuff like that was a discount. But discounts are not. Site discounts, site discounts. If you just change the price on the site. That is just a reduction to gross revenue.
222 00:21:50.040 ⇒ 00:21:54.749 perry: and it just never. It never existed to us. The revenue we didn’t capture because we changed the price.
223 00:21:55.060 ⇒ 00:21:57.480 Uttam Kumaran: Okay, okay, that was a question, really.
224 00:21:58.840 ⇒ 00:22:03.119 Uttam Kumaran: So I don’t think we have any measurement, though, of like, what is being marked down.
225 00:22:03.390 ⇒ 00:22:10.720 Uttam Kumaran: And I don’t also don’t think a lot of teams know that that’s the answer. But that’s probably what we’ll try to circulate as part of that.
226 00:22:10.720 ⇒ 00:22:18.700 perry: Stupid, and I’ll be frank. They should absolutely know that because I yelled at them for 5 years about it. So they absolutely should know that that’s the answer. And the fact that they’re having short term memory loss.
227 00:22:18.700 ⇒ 00:22:19.959 Uttam Kumaran: Makes the question a lot.
228 00:22:20.020 ⇒ 00:22:23.799 perry: But I’m not their babysitter. So that’s their monkey, their circus.
229 00:22:23.800 ⇒ 00:22:28.460 Uttam Kumaran: Yeah, I just think I I agree with you. It’s like for me I was like, is this a fake? Discount or not? That’s all.
230 00:22:28.460 ⇒ 00:22:30.510 perry: Okay, 1st off. 1st and foremost.
231 00:22:31.170 ⇒ 00:22:37.839 perry: they are the shopify team. They can go check, shopify. If they just looked at a shopify order, they would find exactly what I just told you.
232 00:22:37.840 ⇒ 00:22:43.349 Uttam Kumaran: I think they’re just like wondering if there’s any other way like that price ends up in the data like.
233 00:22:43.750 ⇒ 00:22:44.560 perry: It doesn’t.
234 00:22:44.560 ⇒ 00:22:49.870 Uttam Kumaran: Basically like, yeah, I guess they’re just asking, like, does the difference come in at all?
235 00:22:50.060 ⇒ 00:22:55.149 perry: Like one if they if they want anything where we’re tracking the variance to the intended retail.
236 00:22:55.620 ⇒ 00:23:16.231 perry: I think the way that my mind goes to solving. That problem is ingesting the intended refill retail from the pim. And then you can just have a column that you can do calcs off of and looker. But or I mean, the the cleaner way is shopify. But I don’t know how the price changing structure works in shopify. If it’s like they actually have to like, overwrite the existing price, or they can have a separate field. I have no idea.
237 00:23:16.940 ⇒ 00:23:33.120 perry: but when they do that markdown behavior it’s that old price didn’t exist. It doesn’t exist. We’re not saying, oh, we’re capturing this at 1 one moment is 70, and that’s just until they change that.
238 00:23:33.120 ⇒ 00:23:39.050 Uttam Kumaran: Okay. Okay, okay, okay. I think that’s all. I had.
239 00:23:39.050 ⇒ 00:23:43.809 perry: Wrong I’ll be that’d be hilarious if everything just changed, and I’m so incorrect.
240 00:23:44.170 ⇒ 00:23:46.140 Uttam Kumaran: No, I think you’re probably okay.
241 00:23:47.552 ⇒ 00:23:51.170 Uttam Kumaran: Emily, do we have anything else to ask about?
242 00:23:51.430 ⇒ 00:24:03.370 Emily Giant: I don’t think so. I have a feeling that Perry is going to be very involved throughout the process with this one. So if we have other questions. We have, Perry, but I think that that covers, like the base of what we need to look at for the plan.
243 00:24:04.440 ⇒ 00:24:06.619 Uttam Kumaran: Okay, cool. So we’re probably gonna yeah.
244 00:24:06.760 ⇒ 00:24:08.899 perry: One of the things is I just I was going through this with Emily
245 00:24:09.240 ⇒ 00:24:38.129 perry: a lot of the stuff from the marketing stuff. Just as I was perusing it, I said this to Emily. A lot of it exists in structure. It exists, and it was just never ticketed to be reconnected. So if you guys need any help on like pointing in where we can find this, I’m happy to tell you, too, like this exists. And this is what I told Emily. If you want to add to that table, I’m happy to go through and market when I’m back from Pto, and just say, like existing net, new existing net, new of like what is actually just reconnecting old stuff that’s broken versus net new to the business. Because when I took a gander through this. A lot of it is like
246 00:24:38.130 ⇒ 00:24:49.609 perry: sticky, and now loop and then shop salesforce shopify like it existed a year and a half ago, and it. Just it deprecated. And they’re not ticketing their own things for whatever reason. And that’s worked out great for me. But
247 00:24:50.340 ⇒ 00:24:51.860 perry: it might reduce some of your guys work.
248 00:24:51.860 ⇒ 00:25:03.959 Uttam Kumaran: Okay, perfect. Yeah, I assume, because some of these I’m like, okay, this has to be somewhere. Mainly, it’s like, we’re, gonna I just wanna have all of these questions, even in one place. And then we’re gonna start looking through.
249 00:25:04.750 ⇒ 00:25:05.420 Uttam Kumaran: Yeah.
250 00:25:07.550 ⇒ 00:25:09.609 perry: I can see your dog walking around in the.
251 00:25:09.610 ⇒ 00:25:15.295 Uttam Kumaran: Yeah, here’s what walking upstairs, we have had enough. No more revenue for me.
252 00:25:16.470 ⇒ 00:25:21.139 Uttam Kumaran: Okay, great. Alright, I’ll let you guys go, that’s all I had. We’ll chat, slack.
253 00:25:22.630 ⇒ 00:25:23.029 perry: Talk, later.
254 00:25:23.280 ⇒ 00:25:23.900 Uttam Kumaran: Yeah.
255 00:25:23.900 ⇒ 00:25:24.959 Uttam Kumaran: Enjoy the break.
256 00:25:25.360 ⇒ 00:25:25.950 perry: Yeah, I’m gonna go.
257 00:25:25.950 ⇒ 00:25:27.420 Uttam Kumaran: Traps and bears. Yeah.
258 00:25:28.780 ⇒ 00:25:29.400 Emily Giant: Bye, bye.
259 00:25:29.720 ⇒ 00:25:30.040 Uttam Kumaran: Bye.