Meeting Title: Q1 Internal Planning Meeting for Poolparts Date: 2025-01-28 Meeting participants: Nicolas Sucari, Uttam Kumaran, Payas Parab
WEBVTT
1 00:11:54.420 ⇒ 00:11:56.159 Payas Parab: Hey? What’s up, Nico? How are you?
2 00:11:58.120 ⇒ 00:12:00.699 Nicolas Sucari: Hey, Priyaz, how are you? How are you feeling.
3 00:12:00.880 ⇒ 00:12:03.175 Payas Parab: I’m alright. It’s just like a little bit of
4 00:12:03.670 ⇒ 00:12:10.440 Payas Parab: not like I’m not like dying. It’s just like I can’t. I wasn’t really able to sleep, and I kept like kept knocking out.
5 00:12:12.180 ⇒ 00:12:15.579 Payas Parab: Hey, guys, whatever medication I was taking for the flu symptoms.
6 00:12:17.710 ⇒ 00:12:19.270 Nicolas Sucari: Yeah, that’s fine.
7 00:12:20.150 ⇒ 00:12:24.859 Nicolas Sucari: Horrible, this when you feel like that kind of down right.
8 00:12:24.860 ⇒ 00:12:25.870 Payas Parab: Yeah, yeah.
9 00:12:30.810 ⇒ 00:12:31.759 Nicolas Sucari: How are you, Tom?
10 00:12:32.140 ⇒ 00:12:33.539 Uttam Kumaran: Hey? How are you guys.
11 00:12:35.040 ⇒ 00:12:39.609 Nicolas Sucari: Sorry I didn’t see your message was overly I was here, but I
12 00:12:39.930 ⇒ 00:12:42.379 Nicolas Sucari: just thought you were just yeah.
13 00:12:42.930 ⇒ 00:12:44.160 Uttam Kumaran: What was the message?
14 00:12:44.820 ⇒ 00:12:47.100 Nicolas Sucari: No, when you send me to our meeting. Yeah.
15 00:12:47.630 ⇒ 00:12:51.220 Uttam Kumaran: Oh, yeah, no. Sorry. I just like I’m I’m driving back. And
16 00:12:51.700 ⇒ 00:12:55.560 Uttam Kumaran: I thought I was gonna miss it. But I I didn’t realize it’s at 3 30. So perfect.
17 00:12:56.340 ⇒ 00:12:58.149 Nicolas Sucari: No worries. How would that meeting go.
18 00:12:58.380 ⇒ 00:13:04.919 Uttam Kumaran: It was good dude, I’m telling you. Meeting in person with clients is the number one priority.
19 00:13:05.230 ⇒ 00:13:08.889 Uttam Kumaran: I know we have a lot of number one priority campaign is number 2 priority. But
20 00:13:09.322 ⇒ 00:13:22.449 Uttam Kumaran: I don’t know. It was great like, like I learned a lot about them that I would not have been able to find out about Arthelper if if I had met the guy, and he’s honestly, really good, too, and it’s really clear like what their goals are. And
21 00:13:22.610 ⇒ 00:13:26.680 Uttam Kumaran: I think we’re gonna get a retainer deal out of it which we didn’t really
22 00:13:26.890 ⇒ 00:13:30.620 Uttam Kumaran: have a clear understanding of, like what? Where the client was going to go.
23 00:13:30.770 ⇒ 00:13:32.849 Uttam Kumaran: It’s also much more clear that they like.
24 00:13:32.950 ⇒ 00:13:40.810 Uttam Kumaran: They need less of like sort of dashboards. They really just want someone who can dig into the data as it comes in sort of ad hoc.
25 00:13:41.020 ⇒ 00:13:43.160 Uttam Kumaran: I’m on a retainer. So yeah, we’re gonna
26 00:13:43.760 ⇒ 00:13:48.520 Uttam Kumaran: sort of put something together for them. But it was really great. Yeah, we had sushi sushi.
27 00:13:48.520 ⇒ 00:13:48.890 Nicolas Sucari: Hi.
28 00:13:49.980 ⇒ 00:13:56.319 Uttam Kumaran: Wasn’t great Texas, but Miso Soup was good.
29 00:13:57.130 ⇒ 00:14:00.810 Nicolas Sucari: Nice if you like. Sushi. There are some places here that.
30 00:14:01.170 ⇒ 00:14:03.720 Uttam Kumaran: Yeah dude. We gotta go to that pizza.
31 00:14:04.130 ⇒ 00:14:07.809 Uttam Kumaran: We gotta go to the sushi. And then we’re we’re gonna go. Yeah, I need.
32 00:14:08.020 ⇒ 00:14:13.390 Uttam Kumaran: I need Malbec. I need some Malbec to bring home, and then.
33 00:14:14.220 ⇒ 00:14:16.369 Nicolas Sucari: I’ll take you to some good places. Yeah.
34 00:14:17.060 ⇒ 00:14:19.355 Uttam Kumaran: Yeah, that’d be great.
35 00:14:21.100 ⇒ 00:14:45.719 Nicolas Sucari: Excellent. Okay, hey? I don’t wanna spend a lot of time, you guys. I know. Perhaps you’re not feeling great under time you’re driving. I just want to talk about like maybe you some know a little bit more about pool parts, and what are the or what we can be working like as next projects. We have this queue, one that is a big one, that we are working right now. Obviously, that has different kind of faces.
36 00:14:45.985 ⇒ 00:14:53.939 Nicolas Sucari: It will be good to review that the big jump board that I created. Maybe I’ll send it to you. Pay us so that you take a look
37 00:14:53.940 ⇒ 00:15:16.909 Nicolas Sucari: after this meeting and see what else we can work towards that. Master list of skew that we are trying to to to build. But apart from that, I would like to understand. Where are we? What what other projects do you have in mind that we can work with Ben, Dan and or any one of pull parts I know they had.
38 00:15:17.285 ⇒ 00:15:25.539 Nicolas Sucari: We’ve spoken about something up regarding inventory a couple of yeah, months before, months ago. But I’m not sure if that’s
39 00:15:25.850 ⇒ 00:15:30.010 Nicolas Sucari: a project that we can. Yeah, just set up to work with them.
40 00:15:30.650 ⇒ 00:15:35.410 Uttam Kumaran: Yeah. So I guess here’s the overall on the company, and we’ve tried a lot of things over the past
41 00:15:35.700 ⇒ 00:15:45.140 Uttam Kumaran: like year with them. I think the biggest thing is one. They’re not good at looking at the dashboards. They don’t have an internal culture sort of around
42 00:15:45.730 ⇒ 00:15:49.230 Uttam Kumaran: like looking at data and iterating on it.
43 00:15:49.662 ⇒ 00:15:59.130 Uttam Kumaran: They really rely on us for any insights. So I do think one thing long term, and I’ll sort of set the stage this way is like one thing. Long term is, I do think that
44 00:15:59.930 ⇒ 00:16:12.529 Uttam Kumaran: I don’t know how helpful it is for us to just build dashboards and be like we have a dashboard. I think it has helped, you know, in times where they need data for us to quickly get it. But I do think that we have to move towards something that’s more
45 00:16:12.710 ⇒ 00:16:15.909 Uttam Kumaran: around us, finding the insights for them.
46 00:16:16.565 ⇒ 00:16:19.360 Uttam Kumaran: And you know, I think something
47 00:16:19.480 ⇒ 00:16:34.350 Uttam Kumaran: towards that area is best any project we take for them. We’re gonna have to drive home like even the skew work, as you can tell, like we’re gonna be driving it. That’s what they want to see from us. And so they don’t want us to wait. Some other clients, really.
48 00:16:34.450 ⇒ 00:16:38.020 Uttam Kumaran: we’re just there, and we sort of take orders. These guys rely on us
49 00:16:38.618 ⇒ 00:16:42.380 Uttam Kumaran: for their analytics and for insights. So one thing that I think we’ve done.
50 00:16:42.660 ⇒ 00:17:00.219 Uttam Kumaran: I’ll tell you what we’ve done a really good job. We’ve we’ve brought in all their data, we standardize. We have models. We haven’t done a good job at activating the insights meaning. They have dashboards, and nobody’s like nobody’s using it. Maybe Kim is using it occasionally, and they’re probably they’re still sitting on a lot of stuff they could probably change based on those insights.
51 00:17:00.440 ⇒ 00:17:04.760 Uttam Kumaran: Whether I would say the primary area is going to be around sales customers,
52 00:17:06.130 ⇒ 00:17:12.089 Uttam Kumaran: and costs. You know. So that’s kind of where I would start like I
53 00:17:12.190 ⇒ 00:17:17.130 Uttam Kumaran: I don’t know, but at some level it’s also not going to be helpful for us to go to them and be like.
54 00:17:17.260 ⇒ 00:17:23.220 Uttam Kumaran: what do you care about the the focus has always been number one. On looking at sales and marketing.
55 00:17:23.410 ⇒ 00:17:29.489 Uttam Kumaran: Where are we getting customers? Things like that the second thing that’s that’s gonna be helpful is to start looking at costs.
56 00:17:29.850 ⇒ 00:17:36.649 Uttam Kumaran: And now that we have cogs and we’re improving that we’ll we’ll start to. We’re gonna wanna understand? Like where those costs coming from.
57 00:17:37.120 ⇒ 00:17:50.000 Uttam Kumaran: And then we want to understand customers like, who who are these people that are spending a lot are. Who are they selling wholesale to things like that? But I don’t know. I don’t think the form factor for this is like we build you a dashboard where you can go do that self service.
58 00:17:50.260 ⇒ 00:17:54.400 Uttam Kumaran: I think the form factor is like, here’s a piece of analysis that we found.
59 00:17:54.660 ⇒ 00:18:03.949 Uttam Kumaran: So maybe, like I’ll kind of set the stage with that, and interested in your thoughts, Pius, on like how we can sort of architect our engagement with them to like
60 00:18:04.620 ⇒ 00:18:11.109 Uttam Kumaran: to be more towards that. They’re a unique client in that. Yeah, you know, in that way. But yeah.
61 00:18:11.870 ⇒ 00:18:27.819 Nicolas Sucari: I think they are expecting not only us to like, share that kind of information that we find or insights that we find, but also telling them what to do with with it. Right? Because if we share like, hey, we found that every time it rains you sell like more
62 00:18:27.820 ⇒ 00:18:43.599 Nicolas Sucari: tools they are expecting like. And and what do we need to do now? Like, maybe that’s where we need to focus like, Hey, we found out this, and we need to do this right now in order to achieve more sales, new customers reduce some costs on some stuff.
63 00:18:43.600 ⇒ 00:18:57.199 Nicolas Sucari: and they are expecting us to kind of yeah, drive or own kind of those projects, and and get get in in between all of their people and try to push as much as possible.
64 00:19:04.110 ⇒ 00:19:04.520 Payas Parab: Yeah, I think.
65 00:19:04.520 ⇒ 00:19:05.419 Nicolas Sucari: Good year for you.
66 00:19:05.730 ⇒ 00:19:08.260 Payas Parab: It’s tricky like, I’m curious if there’s like.
67 00:19:08.910 ⇒ 00:19:32.949 Payas Parab: in terms of like marketing, is there like outside of like getting all the data in place like, is there any precedent for, like the types of recommendations we’ve made to them? I know, like I trying to set up that experiment with Kim around like, Hey, the address stuff the weather stuff. Now that we have the data set, let’s get the dashboards. But who, Tom? We’d run into the same issue, right? Of like, okay, if there’s no culture of looking at the dashboards, then, like, yeah, maybe like setting up, the experiment will help.
68 00:19:33.080 ⇒ 00:19:44.819 Payas Parab: I like, you know, I I like. I’m curious if there’s any like from either sales and marketing like there’s like any use cases like so far that we’ve seen outside of just like data infrastructure organizing.
69 00:19:45.670 ⇒ 00:19:54.920 Uttam Kumaran: Yeah. I mean the stuff that the stuff that helped them a lot is is work we’ve done on sales. They’re always curious about like what’s selling. But really, again, like
70 00:19:55.070 ⇒ 00:19:57.019 Uttam Kumaran: they almost they almost
71 00:19:57.740 ⇒ 00:20:10.699 Uttam Kumaran: it’s tough, like they. They do have requests that are sort of ad hoc. But they almost need someone who’s just like sort of finding these nuggets for them. Right? And that’s what we’ve done. We’ve we’ve been good at is that like
72 00:20:10.810 ⇒ 00:20:21.260 Uttam Kumaran: we’re not. We’re not just engineering support, like we’ve provided strategy and sort of like sales help like one of the examples is their post pilot campaigns were some of the most efficient campaigns they did.
73 00:20:21.742 ⇒ 00:20:26.749 Uttam Kumaran: It costs about 53 cents to send a mail to a person, and these are.
74 00:20:27.070 ⇒ 00:20:37.040 Uttam Kumaran: you know, could be a couple of 100 to a thousand dollar orders. So one of the things that we wanted to work on is like, how do we scale that up. I put some pressure on, but I got kind of busy, and
75 00:20:38.240 ⇒ 00:20:51.719 Uttam Kumaran: that was something where it’s like I found we found that efficiency we learned we help them kind of segment, their customer base. And then we’re sort of like. Here’s how you take action on it. The problem is, I don’t know whether we have capacity to take on like more than 2 things at a time for them.
76 00:20:51.910 ⇒ 00:20:56.290 Uttam Kumaran: So that’s kind of like, what I want to stick to is like, I think right now, we’re focused on this weather thing.
77 00:20:56.420 ⇒ 00:21:06.470 Uttam Kumaran: We’re focused on the skew thing. The skew thing is number one. I do want to think about one other thing where we basically find a nugget, we chase it. We give them insight.
78 00:21:07.024 ⇒ 00:21:16.359 Uttam Kumaran: I think they’re, you know. They probably have some interest in like just knowing what the overall numbers are. But I do think that they they’re satisfied with their sort of reporting to date.
79 00:21:16.790 ⇒ 00:21:22.009 Uttam Kumaran: They’re definitely fiending for like insights. And like, what are we missing? You know.
80 00:21:22.170 ⇒ 00:21:24.059 Payas Parab: Yeah, I think there’s just an element of.
81 00:21:24.060 ⇒ 00:21:25.979 Uttam Kumaran: You know, customer service as well.
82 00:21:26.120 ⇒ 00:21:30.689 Uttam Kumaran: and we have all their Zendesk data. We have all the return data that could be on the cost side.
83 00:21:31.020 ⇒ 00:21:36.700 Uttam Kumaran: So it’s tough. It kind of takes an open mind to like, go and explore. Nice thing is we have ours.
84 00:21:37.100 ⇒ 00:21:40.859 Uttam Kumaran: But if we don’t use the hours. Then I we can’t bill for anything, so.
85 00:21:40.860 ⇒ 00:21:41.410 Payas Parab: Yeah.
86 00:21:43.070 ⇒ 00:21:46.330 Uttam Kumaran: It’s it’s tough. It’s a we don’t have a ton of direction.
87 00:21:46.860 ⇒ 00:21:51.349 Payas Parab: Yeah, I’m wondering if there’s like some sort of guidance like around.
88 00:21:51.470 ⇒ 00:22:03.549 Payas Parab: like we do have to do just some exploration right? It’s not like a clear thing of like, I want to know this and this right, but based on like what you know about the business, what you like. If you were like I am gonna start fracking for some insights right.
89 00:22:03.550 ⇒ 00:22:04.020 Uttam Kumaran: Yes.
90 00:22:04.020 ⇒ 00:22:15.960 Payas Parab: Like, where? Where might you start if you’re like, hey, here’s all the data we have. Is it like customer service? Is the one? Is it like returns and refunds? Is there like any any like? Just because with them for a while right like something
91 00:22:15.960 ⇒ 00:22:19.610 Payas Parab: yes, like, be interested in, and that
92 00:22:19.950 ⇒ 00:22:28.889 Payas Parab: like is a decent place to start. We have a lot of data already in place, and it’s like we’re likely to find something just by like moving some tables, or you know what I mean. Like.
93 00:22:28.890 ⇒ 00:22:29.960 Uttam Kumaran: Yes, yes.
94 00:22:29.960 ⇒ 00:22:33.153 Payas Parab: Is there? Is there like a list in your head of like
95 00:22:33.710 ⇒ 00:22:36.480 Payas Parab: the top ones that might come come to mind.
96 00:22:36.820 ⇒ 00:22:42.790 Uttam Kumaran: Yeah. So everything on like sales, which is like, how much are we selling of Xyz that they? They’re okay with
97 00:22:42.990 ⇒ 00:22:49.259 Uttam Kumaran: this, cogs. Work is gonna be important, because I don’t think they’ve yet to really look at product level profitability.
98 00:22:49.380 ⇒ 00:22:52.150 Uttam Kumaran: But again, this is the main blocker for that
99 00:22:52.440 ⇒ 00:22:54.940 Uttam Kumaran: cogs and looking at sort of like
100 00:22:55.110 ⇒ 00:22:59.649 Uttam Kumaran: profit per order, I think, is something that could use a lot of work.
101 00:22:59.850 ⇒ 00:23:05.449 Uttam Kumaran: The other thing is like we did some work on. We tried to help them to like a B test, some pricing.
102 00:23:05.610 ⇒ 00:23:08.050 Uttam Kumaran: They ended up being like nervous that like.
103 00:23:08.530 ⇒ 00:23:16.009 Uttam Kumaran: because they make a majority of their sales in like a couple of periods, and they didn’t want to test pricing in those moments, and.
104 00:23:16.160 ⇒ 00:23:16.590 Payas Parab: Okay.
105 00:23:16.590 ⇒ 00:23:21.770 Uttam Kumaran: Since we tested one time we tested it. We did Ab testing for pricing, and then they sort of like
106 00:23:22.100 ⇒ 00:23:30.339 Uttam Kumaran: Ben freaked out because he thought our our Ab testing was like causing a bunch of sales drops. It wasn’t us at all, but he was like, just turn it off.
107 00:23:30.840 ⇒ 00:23:55.908 Uttam Kumaran: so I don’t know you could hit. You could hit that again to figure out pricing. It’s again. It’s like what juice is worth the squeeze. I think. Also like, there’s probably something on the refunds and return side, like what one would be. Okay. First, st let’s get a sense of like, how does how, how much do refunds and returns, eat our bottom line? What are the top reasons, and is there? Is there? Are there any low hanging fruit for us to mitigate these?
108 00:23:56.710 ⇒ 00:24:01.499 Uttam Kumaran: that could be a great thing to just chunk out and and do like answer those basic questions.
109 00:24:01.500 ⇒ 00:24:01.870 Payas Parab: Yeah.
110 00:24:02.272 ⇒ 00:24:03.880 Uttam Kumaran: The thing is there
111 00:24:04.180 ⇒ 00:24:17.099 Uttam Kumaran: roughly, like those layer. One questions. They’re gonna know anecdotally. It’s a layer. 2 things which is like the recommendation or like, okay, we looked at this one ticket type. And then we looked at the customers. And we did some analysis shows that, like
112 00:24:17.220 ⇒ 00:24:34.759 Uttam Kumaran: customers with this problem, take some action. And so we submit. It’s like, that’s the sort of stuff that’s really juice for them. So I do think there’s probably something like that on the returns and refund side. That’s totally possible. And then the cogs work, I think, definitely nailing this cost of goods sold problem is huge. Marketing is tough, because
113 00:24:35.240 ⇒ 00:24:39.530 Uttam Kumaran: I don’t know. I every time we’ve tried to touch marketing like Kim.
114 00:24:39.530 ⇒ 00:24:41.019 Nicolas Sucari: Yeah, it’s it’s just.
115 00:24:41.020 ⇒ 00:24:42.189 Uttam Kumaran: It’s all over the place.
116 00:24:42.460 ⇒ 00:24:45.549 Uttam Kumaran: Yeah, like cus customers, like.
117 00:24:45.840 ⇒ 00:24:50.539 Uttam Kumaran: I think there’s still probably a lot of work we could do on customers, which is like
118 00:24:50.830 ⇒ 00:24:57.679 Uttam Kumaran: better segmentation, understanding, like maybe how we can do better targeting. But again, it’s like, I only want to take.
119 00:24:57.680 ⇒ 00:24:59.049 Payas Parab: Like some of these things.
120 00:24:59.050 ⇒ 00:25:00.010 Uttam Kumaran: A lot of work.
121 00:25:00.150 ⇒ 00:25:06.640 Payas Parab: Recurring, recurring customers, and like customer those that’s not like as big of a thing right for them? Or is it like.
122 00:25:07.290 ⇒ 00:25:09.720 Uttam Kumaran: They don’t have much. They don’t have much recurring at all.
123 00:25:09.720 ⇒ 00:25:14.820 Payas Parab: Cause you like, get one. You tell these things. And then you’re like, Okay, cool. My problem is solved, and I’m good for.
124 00:25:14.820 ⇒ 00:25:33.080 Nicolas Sucari: Yeah, we worked a couple of weeks ago, trying to identify, like old professionals, or more kind of people buying to resell those products to other people. We created some rules. We have that. But they I don’t know if they are giving, like the importance that the to that kind of segment, and try.
125 00:25:33.359 ⇒ 00:25:37.829 Uttam Kumaran: We didn’t get. We didn’t go. The yeah, we didn’t give them like, Okay, what now?
126 00:25:37.990 ⇒ 00:25:38.590 Uttam Kumaran: And it’s.
127 00:25:38.590 ⇒ 00:25:39.140 Nicolas Sucari: Yeah, because like.
128 00:25:39.140 ⇒ 00:25:46.540 Uttam Kumaran: I. We gave Kim, hey? There’s 2 segments of people exactly, and then we tried to do it. And then Ben was like, it’s not a priority. And
129 00:25:47.470 ⇒ 00:26:01.529 Uttam Kumaran: so it’s hard like. But but at the same time, like, I want to keep trying like these, these guys are skeptical. But also they’re they know us. So if we make a recommendation, they’ll go for it. This is why I kind of. Instead of we were working on like 4 or 5 things at a time for them. Now, I want to focus. And just do
130 00:26:01.760 ⇒ 00:26:07.899 Uttam Kumaran: think about 2 things. Think about that. We have, you know, basically like 10 to 20 HA week.
131 00:26:08.040 ⇒ 00:26:12.464 Uttam Kumaran: We can spend on 2 things. What are those 2 big rocks? We can move the needle on?
132 00:26:13.150 ⇒ 00:26:17.059 Uttam Kumaran: But I would say. The one area that I haven’t looked at a lot is
133 00:26:18.940 ⇒ 00:26:21.759 Uttam Kumaran: discounts, refunds, and returns.
134 00:26:23.580 ⇒ 00:26:24.730 Payas Parab: Got it. Okay?
135 00:26:25.440 ⇒ 00:26:30.139 Payas Parab: Like, of course, you’ll see that discount shopify data right? Like within the shopify data.
136 00:26:30.400 ⇒ 00:26:38.303 Uttam Kumaran: Yeah, the Amazon data is kind of like, roughly useless. Because you get no customer information. And
137 00:26:39.290 ⇒ 00:26:42.740 Uttam Kumaran: we, the return data will come from Amazon, though, like some people will.
138 00:26:42.920 ⇒ 00:26:48.019 Uttam Kumaran: Well, they’ll try to return their Amazon. They have to go to the site to return their Amazon shit.
139 00:26:48.780 ⇒ 00:26:50.100 Uttam Kumaran: but, like
140 00:26:50.360 ⇒ 00:26:55.690 Uttam Kumaran: the Amazon. They don’t give us anything until unless they happen to get in touch with us that way.
141 00:26:56.210 ⇒ 00:26:57.299 Payas Parab: Got it. Okay?
142 00:26:57.520 ⇒ 00:27:05.234 Payas Parab: So maybe maybe the returns are like, why are people returning? Is there like? Is there like a business case that like that is an issue that like
143 00:27:05.510 ⇒ 00:27:11.049 Uttam Kumaran: This is what it’s like you can. You can look at we. We will have like we have all the return data there modeled.
144 00:27:11.160 ⇒ 00:27:14.419 Uttam Kumaran: You can just run a sum and see like what the sort of value is.
145 00:27:14.420 ⇒ 00:27:15.100 Payas Parab: Yeah.
146 00:27:15.290 ⇒ 00:27:19.160 Uttam Kumaran: And then sort of say, like, I mean again. But this is also like sort of just doing a spike.
147 00:27:19.400 ⇒ 00:27:23.240 Uttam Kumaran: It’s kind of like you just turn, turn on like
148 00:27:23.540 ⇒ 00:27:28.889 Uttam Kumaran: you know your favorite album and be like cool. I’m just gonna just fucking. Spend like 3, 4 h just like
149 00:27:29.570 ⇒ 00:27:39.149 Uttam Kumaran: run in some queries and seeing what’s in the refunds data and being like asking some questions. And you report that out, hey? Like, I just took a stab at looking at this. Here’s what we found.
150 00:27:39.430 ⇒ 00:27:48.680 Uttam Kumaran: These things seem like normal. For these reasons these things seem like, possibly, and these seems like red flags. We’re gonna chase these down as soon as you send that
151 00:27:48.840 ⇒ 00:27:56.650 Uttam Kumaran: Ben and Dan will easily be able to be like, Okay, Yup, we already know that. Oh, and no idea. Oh, yeah, we have this other thing we did last year for this.
152 00:27:56.850 ⇒ 00:28:02.520 Uttam Kumaran: And like that will start that conversation going. It will take that sort of initial like activation, energy.
153 00:28:02.830 ⇒ 00:28:03.190 Payas Parab: That’s right.
154 00:28:03.190 ⇒ 00:28:11.459 Uttam Kumaran: Of work they need. They don’t need more. We modeled all their stuff. We may need maintenance, but they really need like a biz, like a business analyst.
155 00:28:12.000 ⇒ 00:28:12.430 Payas Parab: Yeah.
156 00:28:12.430 ⇒ 00:28:13.840 Uttam Kumaran: Type, type, stuff.
157 00:28:14.270 ⇒ 00:28:18.360 Payas Parab: Okay, so.
158 00:28:18.360 ⇒ 00:28:33.570 Nicolas Sucari: Talk about. Yeah. So if we talk about what we have right now, like activities, this queue stuff that will. I think it will go like matching with the costs and kind of cost of goods sold for every product and trying to. Yeah, work on that.
159 00:28:33.570 ⇒ 00:28:35.490 Uttam Kumaran: They want to do profitability by product.
160 00:28:36.250 ⇒ 00:28:38.610 Nicolas Sucari: Okay, so that’s the goal. Okay?
161 00:28:38.610 ⇒ 00:28:39.280 Uttam Kumaran: Yeah.
162 00:28:39.280 ⇒ 00:28:39.900 Nicolas Sucari: Okay.
163 00:28:40.250 ⇒ 00:28:51.310 Nicolas Sucari: we have the weather analysis. Then, too, that could be something that we once we finish that, then we can start working on refund, returns and discounts. What do you think? So that we can focus on 2 things.
164 00:28:51.580 ⇒ 00:28:54.670 Uttam Kumaran: Yeah, I would love for us to focus on 2 things at once. I don’t think
165 00:28:54.800 ⇒ 00:28:58.530 Uttam Kumaran: for this client doing more than that is possible or worth. It.
166 00:28:58.890 ⇒ 00:28:59.300 Nicolas Sucari: Yeah.
167 00:28:59.300 ⇒ 00:28:59.960 Nicolas Sucari: Also.
168 00:28:59.960 ⇒ 00:29:10.860 Nicolas Sucari: the skew stuff this new stuff is gonna be like, we will have to work on that for some more time to have that ready like for every platform, or like.
169 00:29:10.860 ⇒ 00:29:16.040 Uttam Kumaran: In in between us, waiting and stuff like that. I want us to keep billing hours and doing work for them, delivering
170 00:29:16.270 ⇒ 00:29:17.390 Uttam Kumaran: right? That’s why.
171 00:29:17.390 ⇒ 00:29:34.369 Nicolas Sucari: So the skew stuff is is priority number one, and it’s that’s like our like top thing in order to work on. But when we have that time to spend on another thing we should be working on whether or refunds, returns, discounts, or any other project that comes after that.
172 00:29:34.660 ⇒ 00:29:38.590 Uttam Kumaran: Yeah, how far are we on on, out? What’s remaining on the weather stuff
173 00:29:38.950 ⇒ 00:29:41.020 Uttam Kumaran: like our side is done right.
174 00:29:41.800 ⇒ 00:29:51.939 Payas Parab: So the only thing that’s remaining is so we looked at everything is like historical right now. It’s like pulling the forward forecast and then using that to generate like a list for a test for Kim.
175 00:29:52.320 ⇒ 00:29:54.789 Payas Parab: So we take the emails that we have.
176 00:29:55.170 ⇒ 00:30:18.789 Payas Parab: we run a test between like on the email marketing campaign. Here are the emails that are like upcoming weather. And here are the emails with not upcoming. It’s like, right now, it’s like the way that, like I ingested all that data for the historical. It’s not the same data set, like we sort of have to like find a different. It’s like a different endpoint. It’s like a different. So it’s kind of like, it takes a little bit more lift. And my my honest gut reaction was like.
177 00:30:20.350 ⇒ 00:30:24.799 Uttam Kumaran: What’s up your your gut reaction was, do it, or don’t! Don’t go forward.
178 00:30:25.210 ⇒ 00:30:30.309 Payas Parab: My, my gut reaction is like Kim doesn’t seem that interested in it, but that’s.
179 00:30:30.310 ⇒ 00:30:30.760 Uttam Kumaran: Okay.
180 00:30:30.760 ⇒ 00:30:53.470 Payas Parab: My gut reaction was, she was like, I don’t know what this is. So I I thought, like my, if there was like an important next step from there. That’s like, okay. If we had to pick a battle right? And it’s like getting her to run that experiment, I don’t know if is actually the best thing is like versus like, we’ve done this legwork to like process and tie their data to weather data. Maybe we get that sort of a visualization where, like
181 00:30:54.070 ⇒ 00:30:57.559 Payas Parab: maybe that like might be better, I don’t know like or but like.
182 00:30:57.560 ⇒ 00:31:22.140 Uttam Kumaran: No, your gut instinct is right. This is exactly what I want us to be talking about is, and this is where, like we struggled in the past because I was sort of the only one being like is the juice worth the squeeze. This is where like this is what we want to do with which you found out. Hey, there’s all this. There’s this. This is the opportunity on whether here’s what we could do if if she’s like, Hey, it’s not important. Then we put a PIN in it right? We found out about.
183 00:31:22.140 ⇒ 00:31:22.460 Nicolas Sucari: Yes.
184 00:31:22.460 ⇒ 00:31:49.679 Uttam Kumaran: Info, and that’s where we are. I think we should. What I can do like, and this is where like it’s more of a handoff to me. Right? I can go and be like, Okay, here’s like, kind of all the things we worked on in the past. Here’s what we’re doing in the future, and I’ll just I can tease this weather stuff, or when we go talk to Dan we’ll mention it, and we’ll see. Is there. Buy in from the top. On this, if not, then we did our job. We found out whether it was worth the squeeze or not, and it didn’t seem like it.
185 00:31:50.110 ⇒ 00:31:55.980 Payas Parab: Part of it was like Kim was like, Well, I need to check with Dan, and then, like I sent it to like many channels, like some of that.
186 00:31:55.980 ⇒ 00:31:57.009 Uttam Kumaran: Yeah, that’s yeah.
187 00:31:57.010 ⇒ 00:32:21.910 Payas Parab: And there’s like no reply by him. Right? So it’s like, there’s sort of like a okay. Well, like, if Dan had, Re responded, being like, okay, this is cool. Why is that this? Right? Or like, okay, what would we do? My brain would have been like, Okay, cool. This is like now, top priority. But then it’s like, Okay, well, the only thing I’ve heard from Dan is this, like skews? Right? So then let’s let’s answer in there. And there is actually like as you start digging in. There’s there’s quite a lot to figure out that again. They probably don’t appreciate. But it’s like we need.
188 00:32:21.910 ⇒ 00:32:22.410 Uttam Kumaran: Yes.
189 00:32:22.410 ⇒ 00:32:24.020 Payas Parab: Figure out. Now.
190 00:32:24.140 ⇒ 00:32:30.559 Payas Parab: my, my thing is like like as part of this handoff, right like in like, I’m just like thinking, like, there’s like.
191 00:32:31.540 ⇒ 00:32:58.769 Payas Parab: what data do we have? Right? That’s like bucket. One of like, what what do we have access to? To analyze? Right is like, step one. Then I think there’s like this like layer 2. Which is like, what have we tried? What have they shown? Some interest in? What have they like? Said, like doesn’t really matter. What are things that are like unexplored? Right? And then we go like where those 2 things overlap is like how we make a list right of like, okay, let’s do that. And let’s chunk it into like. And like you said, you want to build the hours. You want to get it going like.
192 00:32:58.860 ⇒ 00:33:09.530 Payas Parab: maybe it’s it’s worth doing that whole methodical exercise of like, okay, what data do you have like like bucket one, right? Bucket 2 is, what have you tried so that I don’t just try the same thing.
193 00:33:09.530 ⇒ 00:33:19.050 Uttam Kumaran: So. So this. So this is the thing I know. I have all those answers. If you could come up with a with a forum or a meeting structure for me to brain dump that I’ll give it to you.
194 00:33:19.050 ⇒ 00:33:19.440 Payas Parab: 3.
195 00:33:19.780 ⇒ 00:33:25.519 Uttam Kumaran: Like across every single domain. I’ll tell you all the things that we’ve tried, all the data we have access to.
196 00:33:25.700 ⇒ 00:33:27.190 Uttam Kumaran: and then
197 00:33:27.460 ⇒ 00:33:33.919 Uttam Kumaran: the one. The thing I’ll ask you is okay, like, where else can we go with this? And this is the question they’ll ask us like they don’t want.
198 00:33:34.040 ⇒ 00:33:40.310 Uttam Kumaran: They don’t want like the work we’re doing for for Javi, like they, they basically want the answer.
199 00:33:40.450 ⇒ 00:34:03.869 Uttam Kumaran: it’s which is a fun problem like this is what I really think is what we do well, compared to other people, is like, Okay, cool, like, if we put up for ourselves and running their business, what would we look at right? And that’s what they like us. The problem is, it’s a hard problem. And yeah, we’re dealing with with. We’re dealing with a bunch of characters. And you’re right on the skew. Thing is like, that’s the number one. And then we just have to keep digging for other stuff right?
200 00:34:03.870 ⇒ 00:34:04.420 Payas Parab: Sure, sure.
201 00:34:04.420 ⇒ 00:34:21.149 Uttam Kumaran: These these guys are great because they know that some we’re gonna go after some things that aren’t gonna work. And that’s fine. It’s as long as we’ve closed it out like. And then again, there’s there’s gonna be more opportunity. You’ll see that this whole business is open for us to go make an impact on including inventory shipping
202 00:34:21.280 ⇒ 00:34:25.570 Uttam Kumaran: like we’ve done a lot of work on shipping. Because and we, this is where we really
203 00:34:25.710 ⇒ 00:34:34.919 Uttam Kumaran: made a huge impact. We save them like hundreds of thousands of dollars on shipping. And so I think, like, let’s do that if you can come up.
204 00:34:35.110 ⇒ 00:34:37.530 Payas Parab: Yeah, let me let me write out like a doc, and then.
205 00:34:37.530 ⇒ 00:34:38.189 Uttam Kumaran: About it? Yeah.
206 00:34:38.190 ⇒ 00:35:03.420 Payas Parab: You want to brain dump it basically to me what I and I think there’s a point where we go through that brain jump, we find that intersection right of like data we have that isn’t as explored. Once we find that intersection, then we like make a laundry list, and we make our own version of the priorities, and like a timeline like for like for like the next 8 weeks, not just like a hey, we’re gonna try this over the next week, or whatever. Let’s let’s these are the next steps here. So then it
207 00:35:03.710 ⇒ 00:35:04.930 Payas Parab: it’ll be like
208 00:35:05.241 ⇒ 00:35:12.020 Payas Parab: it’ll be okay. Let me let me. Let me set up that document for you, and if you want to brain dump, if you want to like voice, memo it to me.
209 00:35:12.020 ⇒ 00:35:37.429 Uttam Kumaran: Yeah, exactly. And in that document do it like every part of the company. So what is the company? You have? Sales marketing, fulfillment labor product just like, write all this. Think about the entire life cycle of a product and of a customer. And these guys manufacture and ship. And then I’ll just I’ll I could just send you a voice note going down that list, basically like what we’ve done, what we haven’t done.
210 00:35:37.430 ⇒ 00:35:52.619 Payas Parab: Perfect. Yeah. And then let’s let’s identify those gaps. Let’s like, make that list, then, and be like this. How we want to like, think about the next 10 weeks of work with you, and then and then put it in front of them right? And we’re like, we think like not, just like you need to tell us whether this is like this is what we are going to do.
211 00:35:52.620 ⇒ 00:35:53.330 Uttam Kumaran: Yes.
212 00:35:53.330 ⇒ 00:36:02.469 Payas Parab: Do you want us to do it right like that’s, I think that’s you’re saying this client is different because they don’t really want that like you tell us what to do. We go do it. They want us to be so. I think we
213 00:36:02.890 ⇒ 00:36:15.670 Payas Parab: exercise. I will set that up for you, I’ll have you just go through the different aspects of the business. I’ll get all the data that we have. I’ll have you just kind of fill in the gaps of like what we’ve tried, what has worked, what hasn’t worked, what what you would have loved to do, more of.
214 00:36:15.670 ⇒ 00:36:16.030 Uttam Kumaran: Yes.
215 00:36:16.030 ⇒ 00:36:40.129 Payas Parab: And what we haven’t touched at all. Then we turn that into like an 8 week priority list that works in like 2 weeks sprints. Right? So then, it’s like we’re going to them with like a Hey, Dan, we want like an hour of your time just to like, think through and like, think through all this with us. And if he has, like a giant doc that has everything you’ve done for him over the last year, he also can like react to that of like, oh, I do remember that like what happened to that right like, let’s.
216 00:36:40.130 ⇒ 00:36:40.690 Uttam Kumaran: Okay.
217 00:36:40.690 ⇒ 00:37:01.080 Payas Parab: And I think then, and we turn it into like this long like, we want to fill the next few weeks with like doing some more exploratory analysis, spending more time, like trying to answer some questions and being like going going out on like fact, finding missions. We just want to like, make sure we’re at least like in the right, like right part of the mountain, right? Like we’re not like, we’re not just like digging anywhere we’re like
218 00:37:01.200 ⇒ 00:37:08.390 Payas Parab: at least want to be in like the right part of the mountain. You guys are interested in where we might strike gold, and we’ll spend some time doing it and provide like.
219 00:37:08.630 ⇒ 00:37:24.180 Payas Parab: you know, like, hey, like, like in my mind, there’s like 4 like we scope out 4 things of like these are 4 things we haven’t touched yet, or like we did a little bit could do more. And now we’re like in a position where we’re gonna spend 2 weeks on each of these have some sort of like a defined deliverable.
220 00:37:24.340 ⇒ 00:37:27.640 Payas Parab: and then get it to you, and then just be like, Hey.
221 00:37:28.100 ⇒ 00:37:37.989 Payas Parab: like you’ll we’ll kind of like. And maybe something builds out of there. Does that make sense like we’re like, we’re laying out what the next 8 weeks of exploratory looks like, and we like have a framework for like
222 00:37:38.290 ⇒ 00:37:45.349 Payas Parab: like, how we thought about it. Right? It’s not just like a Oh, that’s a cool idea, because we just thought like Pius was bored and like was like, let’s go
223 00:37:45.780 ⇒ 00:38:01.829 Payas Parab: take a look at the customer services like we look through like all this data we’ve set up for you. We’ve thought through what we’ve done. Here’s these spaces, and here’s how we can like without creating a huge burden on you like, keep progress moving on these for the next, I think, like, I think, like something like 8 to 10 weeks.
224 00:38:02.078 ⇒ 00:38:06.090 Uttam Kumaran: Think 2 weeks, I think 8 to 10 weeks is a great like 7.
225 00:38:06.090 ⇒ 00:38:10.919 Payas Parab: We choose, like 4 projects, 4 projects in 2 weeks sprints. And then we’re also not like
226 00:38:11.250 ⇒ 00:38:38.930 Payas Parab: there’s no like ambiguity, either. Right? It’ll be like project One, start date. Is this project one interim check in is this and project one end date. Is this right? And we’re just sort of like cranking through those. And like, maybe at the interim mark, we just say this isn’t actually worth anything. We move on right, and it like gives adequate Async time to me right? And it’s like clear and communicated to them what timelines they can expect as well. So it’s not just like ad hoc, when we feel like, or whatever.
227 00:38:39.340 ⇒ 00:38:40.530 Uttam Kumaran: Yeah, you’re spot on.
228 00:38:40.530 ⇒ 00:38:49.019 Payas Parab: Okay, excellent. Let me. That’ll be action item for me right now. So I know we’re about to jump into Javi planning as well, but let me let me see if we can get
229 00:38:49.370 ⇒ 00:39:12.320 Payas Parab: that set up right and, like Utama may just ask you to like kind of fill in. You can just voice note dump sections, and then we’ll get that translated into a doc. We’ll like, put that together. We’ll have a preliminary list of like project ideas to go on like 2 to 3 week sprints with. And then we just like, get Dan on the phone and like, have that all in a document for him to like. Look at. Live, you know, with us.
230 00:39:12.750 ⇒ 00:39:13.950 Uttam Kumaran: Okay, perfect.
231 00:39:13.950 ⇒ 00:39:15.310 Payas Parab: Awesome alright sounds, great.
232 00:39:15.920 ⇒ 00:39:24.199 Uttam Kumaran: Okay, let’s hop to the other one. And then, yeah, if we can get that going this week, I’ll I can do the voicemail, and then I’ll just use AI to basically write the put it all into place.
233 00:39:24.340 ⇒ 00:39:26.650 Payas Parab: Got it. Got it always using that AI.
234 00:39:26.910 ⇒ 00:39:27.710 Uttam Kumaran: Yes.
235 00:39:27.930 ⇒ 00:39:30.730 Payas Parab: Love it. Alright. I’ll jump to that. You guys.
236 00:39:30.730 ⇒ 00:39:32.060 Uttam Kumaran: Okay. Alright. Thanks. Guys.
237 00:39:32.060 ⇒ 00:39:32.830 Nicolas Sucari: Good day.