Meeting Title: Weekly-Sprint-Review Date: 2024-02-12 Meeting participants: Ryan Luke Daque, Uttam Kumaran
WEBVTT
1 00:01:37.430 ⇒ 00:01:38.310 Ryan Luke Daque: Hello.
2 00:01:41.570 ⇒ 00:01:42.420 Uttam Kumaran: Hara!
3 00:01:42.660 ⇒ 00:01:46.900 Ryan Luke Daque: Hi, Otem! How’s it going? Hey? Good! How are you doing? Great!
4 00:01:47.990 ⇒ 00:01:49.240 Uttam Kumaran: How’s the weekend?
5 00:01:49.780 ⇒ 00:01:57.260 Ryan Luke Daque: It was pretty far fun, I mean. Yeah, II celebrated my birthday last Saturday, so
6 00:01:57.280 ⇒ 00:02:01.100 Ryan Luke Daque: we had a we just ate out at a buffet.
7 00:02:01.460 ⇒ 00:02:04.700 Uttam Kumaran: Oh, nice, happy birthday. Thanks!
8 00:02:06.120 ⇒ 00:02:07.350 Uttam Kumaran: How do you feel?
9 00:02:07.520 ⇒ 00:02:08.650 Ryan Luke Daque: And
10 00:02:08.789 ⇒ 00:02:16.930 Ryan Luke Daque: I feel old. Yeah, I’m like, I’m almost 40 now. So it’s it’s
11 00:02:17.170 ⇒ 00:02:19.729 Ryan Luke Daque: scary to hear to, you know. I mean.
12 00:02:20.260 ⇒ 00:02:28.059 Ryan Luke Daque: my mind thinks I’m still like in my twenties, but I’m actually more, almost 40. So oh, that’s great, I mean.
13 00:02:28.150 ⇒ 00:02:34.640 Uttam Kumaran: like, how do you? When? So do you? Are you a big fan of birthdays. Like, I’m I’m kind of like you. I I’m
14 00:02:34.790 ⇒ 00:02:40.520 Uttam Kumaran: not the biggest fan, but it’s nice to reflect. You know the past. So
15 00:02:40.860 ⇒ 00:02:46.000 Ryan Luke Daque: yeah, I’m not the biggest fan, but it’s it’s like birthdays here in the Philippines, or like
16 00:02:46.340 ⇒ 00:02:54.159 Ryan Luke Daque: like a big thing. But I guess, like there’s always some birthday parties like kids always have birthday parties and stuff like that. And so
17 00:02:54.840 ⇒ 00:02:56.549 Ryan Luke Daque: yeah, it’s
18 00:02:56.740 ⇒ 00:03:01.070 Ryan Luke Daque: what I’ve been experiencing ever since I was a little child
19 00:03:01.150 ⇒ 00:03:11.809 Ryan Luke Daque: like even my father my father went here last weekend just to celebrate my birthday. Yeah, my father and my grandmother.
20 00:03:11.870 ⇒ 00:03:18.509 Ryan Luke Daque: They’re live actually living in a different city right now. But they they flew here just to celebrate.
21 00:03:18.670 ⇒ 00:03:20.610 Ryan Luke Daque: That’s really great.
22 00:03:23.180 ⇒ 00:03:24.329 Ryan Luke Daque: But yeah.
23 00:03:25.380 ⇒ 00:03:32.059 Ryan Luke Daque: other than that, I also like started doing some started going to the gym.
24 00:03:32.390 ⇒ 00:03:44.259 Ryan Luke Daque: so yeah, hopefully, I get back to being fit and stuff. That’s awesome. Man. Yeah, it just takes, like, you know, just a couple of days in a row, and
25 00:03:44.540 ⇒ 00:03:48.879 Uttam Kumaran: I don’t know. I don’t go, you know. I only go like 3 times a week.
26 00:03:49.120 ⇒ 00:03:53.649 Uttam Kumaran: and you know that’s more than enough. I feel like. So yeah.
27 00:03:54.360 ⇒ 00:04:04.630 Ryan Luke Daque: it’s definitely more than enough, like better than just doing nothing. Yeah. The one thing that I’m I’m I’m trying to start again is just going for more walks. I feel like.
28 00:04:05.000 ⇒ 00:04:05.900 Ryan Luke Daque: yeah.
29 00:04:09.420 ⇒ 00:04:10.150 Well.
30 00:04:10.710 ⇒ 00:04:14.249 Uttam Kumaran: great we can get started if you want to
31 00:04:14.350 ⇒ 00:04:26.169 Uttam Kumaran: pull up the board. I can talk a little bit on my end, and then a couple of things for this week. I think this is gonna be a little bit of a different type of week.
32 00:04:26.550 ⇒ 00:04:30.559 Uttam Kumaran: so we can talk through some of the tasks that are outstanding.
33 00:04:31.740 ⇒ 00:04:35.350 Ryan Luke Daque: Sure, let’s go to the current sprint.
34 00:04:35.890 ⇒ 00:04:37.750 Ryan Luke Daque: I can use my screen, by the way.
35 00:04:38.610 ⇒ 00:04:39.790 Uttam Kumaran: yes.
36 00:04:39.960 ⇒ 00:04:40.620 Ryan Luke Daque: cool.
37 00:04:41.690 ⇒ 00:04:43.270 Ryan Luke Daque: So yeah.
38 00:04:43.830 ⇒ 00:04:46.430 Ryan Luke Daque: yep, this was the current sprint.
39 00:04:47.450 ⇒ 00:04:48.450 Ryan Luke Daque: Basically.
40 00:04:51.590 ⇒ 00:04:52.390 Ryan Luke Daque: See, ya
41 00:04:53.140 ⇒ 00:04:58.870 Ryan Luke Daque: looks like you. You’ve cleaned all the tickets under your name. I guess. There.
42 00:04:58.930 ⇒ 00:05:06.590 Uttam Kumaran: so a couple of things on my end. I I’ve been trying to update the stuff related to
43 00:05:06.600 ⇒ 00:05:20.420 Uttam Kumaran: the Dvt workflow process. So all the things are running. I’m I’m just trying one thing. But I’m actually just gonna turn off some of the updates. One of the updates that I made that is causing those failures.
44 00:05:20.460 ⇒ 00:05:23.320 Uttam Kumaran: I’m gonna do that in the next 30 min.
45 00:05:24.300 ⇒ 00:05:25.110 Ryan Luke Daque: Okay.
46 00:05:26.050 ⇒ 00:05:31.520 Uttam Kumaran: but yeah, apart from that, the local dev environment is up and running.
47 00:05:31.760 ⇒ 00:05:36.369 Uttam Kumaran: like, I’m I’ve sped up the workflows a little bit.
48 00:05:36.660 ⇒ 00:05:38.860 Uttam Kumaran: And then.
49 00:05:40.060 ⇒ 00:05:43.830 Uttam Kumaran: yeah, we also now have light dash
50 00:05:43.860 ⇒ 00:05:45.090 Uttam Kumaran: previews.
51 00:05:45.330 ⇒ 00:05:47.290 Ryan Luke Daque: Yeah, I saw.
52 00:05:47.580 ⇒ 00:05:50.690 Uttam Kumaran: So my hopes. Yeah. My hope is that
53 00:05:50.790 ⇒ 00:05:53.850 Uttam Kumaran: you know, in addition to doing
54 00:05:53.860 ⇒ 00:05:58.950 Uttam Kumaran: that sort of stuff locally, when the Pr happens, I think it’s gonna be nice.
55 00:05:59.110 ⇒ 00:06:07.860 Uttam Kumaran: especially when we’re pushing new models to go into light dash preview and like, just check out how the new model is interacting.
56 00:06:08.100 ⇒ 00:06:09.070 Ryan Luke Daque: Hmm.
57 00:06:09.810 ⇒ 00:06:13.619 Uttam Kumaran: so I’m happy that that’s there.
58 00:06:13.920 ⇒ 00:06:27.970 Uttam Kumaran: and then that’s I would say, that’s the biggest thing I have some stuff I’m following up with. with getting the unleashed data done. And some other things. But I don’t think I’m gonna have
59 00:06:28.090 ⇒ 00:06:36.350 Uttam Kumaran: a whole lot this week. I think a lot of, and I can talk kind of talk about this week. But a lot of this week I want to spend time on some analysis.
60 00:06:36.570 ⇒ 00:06:38.990 Ryan Luke Daque: Hmm, okay,
61 00:06:39.260 ⇒ 00:06:40.799 Uttam Kumaran: so we can kind of.
62 00:06:41.060 ⇒ 00:06:50.540 Uttam Kumaran: you know, prioritize. I added a couple of tickets. But I think we’re gonna just want to do a little bit of a deep dive and do some analysis on some data. But
63 00:06:50.650 ⇒ 00:06:55.209 Uttam Kumaran: yeah, if you wanna go through your tickets we can kinda close that out.
64 00:06:55.500 ⇒ 00:06:56.250 Ryan Luke Daque: Sure.
65 00:06:56.930 ⇒ 00:07:02.520 Ryan Luke Daque: So first, it’s in review here right now. It’s the adding of the shipments table.
66 00:07:03.080 ⇒ 00:07:05.550 Ryan Luke Daque: So this does include
67 00:07:05.900 ⇒ 00:07:12.479 Ryan Luke Daque: basically just joining the shipments table and then joining it.
68 00:07:12.680 ⇒ 00:07:24.930 Ryan Luke Daque: you all order items using like dash join. So yeah, we should be able to see this here already in the light. Dash. okay.
69 00:07:25.090 ⇒ 00:07:25.840 Ryan Luke Daque: yeah.
70 00:07:26.680 ⇒ 00:07:28.409 Ryan Luke Daque: But and then.
71 00:07:28.660 ⇒ 00:07:31.100 Uttam Kumaran: okay, go ahead.
72 00:07:31.700 ⇒ 00:07:33.510 Ryan Luke Daque: Yeah, you go, you go ahead.
73 00:07:33.550 ⇒ 00:07:46.839 Uttam Kumaran: Yeah. So I think the only ticket, I added related to this was just starting to use this table where possible? Instead of showing stuff from all orders.
74 00:07:46.950 ⇒ 00:07:53.130 Uttam Kumaran: If we’re just showing shipment data, we should pull it from here. and then.
75 00:07:54.240 ⇒ 00:07:59.889 Ryan Luke Daque: yeah. And then that way, we can also remove some of the non aggregated shipment metrics
76 00:08:00.500 ⇒ 00:08:01.600 Uttam Kumaran: from
77 00:08:01.860 ⇒ 00:08:07.130 Uttam Kumaran: what? From the all orders.
78 00:08:08.220 ⇒ 00:08:12.989 Ryan Luke Daque: That’s probably something we could do later. I don’t know.
79 00:08:13.410 ⇒ 00:08:23.379 Ryan Luke Daque: Yeah, I was thinking about that as well. Like, since we have shipment data in all orders. So we might as well we might need to remove those. So we don’t
80 00:08:23.420 ⇒ 00:08:31.130 Ryan Luke Daque: get confused which to use like the one. Yeah, it’s just like, you know, where. Sometimes you notice that there is
81 00:08:31.220 ⇒ 00:08:37.520 Uttam Kumaran: duplication where you know where. There we have 2.
82 00:08:37.610 ⇒ 00:08:42.870 Uttam Kumaran: We have 2 items of the same item, and then there’s 2 different shipments.
83 00:08:43.549 ⇒ 00:08:49.280 Uttam Kumaran: like. I would like in that case for us to pull the shipment cost Theta from Shippens.
84 00:08:49.410 ⇒ 00:08:51.770 Ryan Luke Daque: and not from all orders, you know.
85 00:08:51.880 ⇒ 00:08:53.030 Ryan Luke Daque: Make sense.
86 00:08:54.010 ⇒ 00:09:02.050 Uttam Kumaran: and then some similarly, from like the Kpi tables, we should pull the total shipping cost now from shipments.
87 00:09:02.550 ⇒ 00:09:04.190 Ryan Luke Daque: Yeah, makes sense.
88 00:09:04.640 ⇒ 00:09:11.949 Uttam Kumaran: But that’s okay. For now I can maybe take a look at kind of pat some of those things, but I think we can close this one out
89 00:09:17.730 ⇒ 00:09:29.260 Ryan Luke Daque: next one here is adding the customer acquisition cost by attribution source. So this is also already live. I’ve already I’ve I’ve utilized the attribution
90 00:09:29.430 ⇒ 00:09:35.959 Ryan Luke Daque: table that we had a model to get the the attribution.
91 00:09:36.090 ⇒ 00:09:37.969 Ryan Luke Daque: I mean that the marketing cost.
92 00:09:38.390 ⇒ 00:09:41.599 Ryan Luke Daque: because initially, I only used the
93 00:09:41.910 ⇒ 00:09:43.740 Ryan Luke Daque: this shopify
94 00:09:44.890 ⇒ 00:09:46.810 Ryan Luke Daque: table that has to.
95 00:09:47.630 ⇒ 00:10:01.400 Ryan Luke Daque: That has the mic attribution without the cost. But yeah, now it should have it should show. I also updated the weekly monthly dashboard to show that one.
96 00:10:02.800 ⇒ 00:10:04.689 Ryan Luke Daque: We can look at it real quick.
97 00:10:09.180 ⇒ 00:10:19.400 Ryan Luke Daque: They’re still quite fine, though I think that’s just how it is like the acquisition costs like there’s like Facebook, for instance, is like 1,900
98 00:10:20.480 ⇒ 00:10:22.570 Ryan Luke Daque: per customer
99 00:10:22.710 ⇒ 00:10:26.469 Ryan Luke Daque: is. This is the marketing marketing cost 9,000 for
100 00:10:26.810 ⇒ 00:10:27.720 Ryan Luke Daque: Facebook.
101 00:10:28.030 ⇒ 00:10:29.680 Ryan Luke Daque: And then there was 5
102 00:10:29.990 ⇒ 00:10:33.210 Ryan Luke Daque: customaries. But I also included gossip
103 00:10:33.480 ⇒ 00:10:41.869 Ryan Luke Daque: sales. So you can see, this would be a negative right? Cause, like, we only had 600 cross sales for this.
104 00:10:42.310 ⇒ 00:10:44.730 Ryan Luke Daque: We but we. you know.
105 00:10:45.910 ⇒ 00:10:51.889 Ryan Luke Daque: yeah, yeah. So I think this is fine. For now I’m gonna take a look and do some analysis on this data.
106 00:10:52.090 ⇒ 00:11:00.679 Uttam Kumaran: And find out, like, okay. like, how much can we actually do attribution? So I’m gonna kind of work on that. But this is okay, for now
107 00:11:01.100 ⇒ 00:11:06.520 Ryan Luke Daque: sounds good. So I guess we can. Is this done as well?
108 00:11:07.060 ⇒ 00:11:07.920 Uttam Kumaran: Yeah.
109 00:11:10.310 ⇒ 00:11:15.930 Ryan Luke Daque: Last thing here that’s in progress is the creation of the filter for the
110 00:11:16.480 ⇒ 00:11:18.119 Ryan Luke Daque: 7 day. 30 day.
111 00:11:18.900 ⇒ 00:11:23.719 Ryan Luke Daque: but yeah, I did chat
112 00:11:23.920 ⇒ 00:11:33.770 Ryan Luke Daque: like dash about that. And they just replied today, and they don’t currently don’t have and currently don’t have the
113 00:11:34.790 ⇒ 00:11:46.090 Ryan Luke Daque: any way of doing that any way of doing that. But they did provide us some other option, which I will. I will have to look into like. This limited options
114 00:11:46.150 ⇒ 00:11:53.230 Ryan Luke Daque: thing. I’ll have to look into this if this is something that we can possibly use. But most likely
115 00:11:54.380 ⇒ 00:11:56.280 Ryan Luke Daque: it’s not
116 00:12:06.220 ⇒ 00:12:16.709 Uttam Kumaran: Oh, they’re asking for a product suggestion. Okay, whatever we can respond to that later. Okay, I mean, I think I think it’d be great still to add.
117 00:12:16.850 ⇒ 00:12:22.869 Uttam Kumaran: I think it’d be great still to add those as metrics.
118 00:12:23.500 ⇒ 00:12:28.110 Uttam Kumaran: And you can just create those as metrics right in the light. Dash, emo file.
119 00:12:28.810 ⇒ 00:12:30.670 Ryan Luke Daque: Yeah, that makes sense.
120 00:12:31.260 ⇒ 00:12:33.409 Uttam Kumaran: But let’s just do. Let’s just do that.
121 00:12:41.140 ⇒ 00:12:42.899 If you can just add
122 00:12:43.980 ⇒ 00:12:45.889 Uttam Kumaran: 7 days, 30 day
123 00:12:47.300 ⇒ 00:12:50.150 Ryan Luke Daque: year today for gross sales.
124 00:12:51.130 ⇒ 00:12:54.660 Uttam Kumaran: and then we can start with that
125 00:12:54.690 ⇒ 00:13:03.160 Uttam Kumaran: that way. We could see pro. And then the all the way to the pro. The primary way to do that is, you just do gross sales, and then you you’ll add a filter, or just add a.
126 00:13:03.300 ⇒ 00:13:07.610 Uttam Kumaran: Either you can add a filter option, or you can do a sequel.
127 00:13:08.820 ⇒ 00:13:15.269 Uttam Kumaran: like SQL. Case when and you can just do where order date is
128 00:13:15.970 ⇒ 00:13:17.789 Uttam Kumaran: in the last whatever
129 00:13:19.480 ⇒ 00:13:24.159 Uttam Kumaran: in the last 7 days or in the current year. Does that make sense?
130 00:13:26.950 ⇒ 00:13:29.160 Ryan Luke Daque: So this would be.
131 00:13:29.320 ⇒ 00:13:34.209 Ryan Luke Daque: and all Kpi like the monthly Kpi.
132 00:13:34.410 ⇒ 00:13:39.530 Uttam Kumaran: Yeah. So I would say, we should. I wanna do that in
133 00:13:40.960 ⇒ 00:13:50.259 Uttam Kumaran: I want to do that in all orders. Alright, I wanna do that in. because this this came out of wanting to see skew
134 00:13:50.500 ⇒ 00:13:53.509 Ryan Luke Daque: nails 7 days, 30 days.
135 00:13:53.710 ⇒ 00:13:57.430 Uttam Kumaran: So let’s do that in all what that’s in all order. Items. Right?
136 00:14:01.410 ⇒ 00:14:04.539 Ryan Luke Daque: Yeah, all order items. Cause this is product related.
137 00:14:05.020 ⇒ 00:14:12.909 Uttam Kumaran: Yeah, let’s do it all over items. And then we can copy it to the other ones as needed. So again, I think this week will be a lot of analysis.
138 00:14:12.930 ⇒ 00:14:16.179 Uttam Kumaran: So we’ll be making a lot of email updates this week.
139 00:14:17.240 ⇒ 00:14:17.970 That’s good.
140 00:14:18.500 ⇒ 00:14:22.710 Ryan Luke Daque: So in this case I’ll move this to this week’s event.
141 00:14:24.350 ⇒ 00:14:26.340 Uttam Kumaran: How they add these mythics.
142 00:14:27.760 ⇒ 00:14:28.470 Ryan Luke Daque: Okay.
143 00:14:29.650 ⇒ 00:14:33.730 Ryan Luke Daque: yeah, that’s about it. And this one’s the one that’s blocked.
144 00:14:35.230 ⇒ 00:14:44.599 Ryan Luke Daque: And aside from all the tickets here, I did have the meeting with 5 trans. Support last week. Yeah. So we
145 00:14:44.760 ⇒ 00:14:54.749 Ryan Luke Daque: she she was able to help me out. find the logic, basically, cause we we are getting the data. But apparently there’s logic being
146 00:14:55.290 ⇒ 00:15:02.339 Ryan Luke Daque: used by 5 grand, I mean by shopify. If ever there’s a refund or a commission refund.
147 00:15:03.020 ⇒ 00:15:14.150 Ryan Luke Daque: and also there, there’s like the categorizations for Fba that aren’t actually called Fba as the item type, or like fee type. But they’re actually Fba, something like that. So yeah.
148 00:15:14.740 ⇒ 00:15:19.889 Ryan Luke Daque: we can also add that for this print. I think I already created the
149 00:15:20.590 ⇒ 00:15:21.989 Ryan Luke Daque: think. It’s a bad thing.
150 00:15:23.170 ⇒ 00:15:33.259 Uttam Kumaran: Yeah. So I would say, those through the key modeling updates fees is honestly probably more pressing because I want to just close. II want to close that out completely.
151 00:15:34.360 ⇒ 00:15:35.200 Ryan Luke Daque: Okay.
152 00:15:38.790 ⇒ 00:15:40.349 Ryan Luke Daque: yeah, sounds good.
153 00:15:42.240 ⇒ 00:15:46.280 Uttam Kumaran: And so maybe we could take a look at some of the tickets that I created.
154 00:15:51.460 ⇒ 00:15:54.290 Ryan Luke Daque: These ones in the backlog.
155 00:15:54.950 ⇒ 00:15:57.210 Uttam Kumaran: yes.
156 00:15:58.030 ⇒ 00:16:01.219 Uttam Kumaran: it should be the ones about like discounts. Yeah.
157 00:16:01.480 ⇒ 00:16:05.859 Ryan Luke Daque: this one identify top reasons for high discounts. Okay.
158 00:16:05.900 ⇒ 00:16:14.490 Uttam Kumaran: so let me just give you kind of a overall thing is, I think we’re at a point now, like where I want to take a little bit of a pause on
159 00:16:14.620 ⇒ 00:16:18.420 Uttam Kumaran: new modeling and work on analysis.
160 00:16:18.520 ⇒ 00:16:29.060 Uttam Kumaran: So you know, we’ve done a lot of analysis for them on shipping. But I wanna do 3 kind of different things. I wanna analyze
161 00:16:29.200 ⇒ 00:16:33.090 Uttam Kumaran: the current like sales data. So
162 00:16:33.150 ⇒ 00:16:38.890 Uttam Kumaran: one thing I wanna do is like, have list out a bunch of questions about
163 00:16:38.910 ⇒ 00:16:42.009 Uttam Kumaran: this like the sales data that they have and
164 00:16:42.520 ⇒ 00:16:46.680 Uttam Kumaran: how we can actually help them answer a bunch of questions. For example.
165 00:16:47.140 ⇒ 00:16:51.310 Uttam Kumaran: a lot of the things that they have a trouble. Understanding is like, what are the top
166 00:16:51.390 ⇒ 00:16:55.299 Uttam Kumaran: sold skews which skews are doing better this year than last year?
167 00:16:55.550 ⇒ 00:16:59.040 Uttam Kumaran: Like, which skews are struggling
168 00:16:59.090 ⇒ 00:17:02.359 Uttam Kumaran: like which bundles are working
169 00:17:02.530 ⇒ 00:17:07.689 Uttam Kumaran: things like that. So those are all like sales. Really, the questions I want to answer
170 00:17:07.810 ⇒ 00:17:12.209 Ryan Luke Daque: on the I wanna answer also a lot of questions on the discounts and refund side.
171 00:17:12.300 ⇒ 00:17:23.529 Uttam Kumaran: So far this year discounts is way higher than it was last year. So I wanna be able to understand where those discounts are coming from, like which which
172 00:17:24.260 ⇒ 00:17:31.419 Uttam Kumaran: which products are having heavier discounts, why the discounts are happening like are those warranties? Are those like broken products.
173 00:17:31.490 ⇒ 00:17:44.619 Uttam Kumaran: So that’s a lot of looking at the at the Zendesk tickets. And like, kind of probably building some sequel to parse that out. And then also, like refunds, I want to know which products are getting refunded. The most
174 00:17:44.740 ⇒ 00:17:49.719 Uttam Kumaran: and what states things like that, and kind of compare it to last year?
175 00:17:50.010 ⇒ 00:17:58.970 Uttam Kumaran: And then on the last on the marketing side, I wanna do some analysis of the new shopify attribution data
176 00:17:58.980 ⇒ 00:18:03.760 Ryan Luke Daque: to see where traffic is coming from for shopify. Yeah.
177 00:18:04.470 ⇒ 00:18:19.060 Uttam Kumaran: you know. So it’s all those. It’s just like, kind of like, broadly 3 categories. I wonder if it’s best today that maybe we list out 10 or 15 questions. We wanna answer for each of those categories, and maybe me and you can work on that.
178 00:18:19.390 ⇒ 00:18:25.200 Uttam Kumaran: And then we can kind of go through and and use the tool to actually analyze.
179 00:18:28.160 ⇒ 00:18:29.580 Uttam Kumaran: What do you think about that?
180 00:18:29.590 ⇒ 00:18:31.010 Ryan Luke Daque: Yeah, that makes sense.
181 00:18:32.130 ⇒ 00:18:36.850 Uttam Kumaran: So yeah, let’s let’s have it. Let’s just have one.
182 00:18:37.070 ⇒ 00:18:38.490 Uttam Kumaran: Or maybe we could have
183 00:18:38.960 ⇒ 00:18:45.569 Uttam Kumaran: 3 tickets for each of like discounts and refund sales. And
184 00:18:46.880 ⇒ 00:18:50.070 Uttam Kumaran: yeah, it’s marketing. Yeah, and and marketing.
185 00:18:51.010 ⇒ 00:18:52.440 Ryan Luke Daque: Yeah, let’s do that.
186 00:18:53.290 ⇒ 00:18:54.620 Uttam Kumaran: And then
187 00:18:55.780 ⇒ 00:19:05.160 Uttam Kumaran: maybe in slack over the in slack, we can write down right now just a couple of the ones that I mentioned. But we could today in slack, we can just discuss what are some.
188 00:19:05.950 ⇒ 00:19:11.509 Uttam Kumaran: you know, appropriate questions to ask. And I’m also gonna ask Chat Gvt
189 00:19:11.810 ⇒ 00:19:15.729 Uttam Kumaran: to come up with to come up with some. And then
190 00:19:16.250 ⇒ 00:19:27.190 Uttam Kumaran: I wanna send them an email today this morning with like, Hey, these are all the questions we’re going after. If there’s any other questions, let us know. and they’ll they’ll send me some really good ones.
191 00:19:27.610 ⇒ 00:19:29.080 Ryan Luke Daque: Yeah,
192 00:19:30.970 ⇒ 00:19:32.679 So yeah.
193 00:19:40.460 ⇒ 00:19:45.210 Ryan Luke Daque: II just added the the base analysis here. Categories.
194 00:19:46.060 ⇒ 00:19:48.970 Uttam Kumaran: Okay, that’s great. So I think.
195 00:19:51.220 ⇒ 00:19:56.850 Uttam Kumaran: honestly, the top 2 tickets and the bottom 3. You can move to ready.
196 00:19:58.200 ⇒ 00:20:07.669 Uttam Kumaran: And then let’s just go after these this week. So hopefully. you can kinda like cruise through those 2 modeling things. And then let’s just spend a week on analysis.
197 00:20:07.750 ⇒ 00:20:15.889 Uttam Kumaran: and then, I think during that process, we’re also going to find a lot of light dash updates to make like labels and group labels, and.
198 00:20:15.980 ⇒ 00:20:19.850 Ryan Luke Daque: you know, changes. So I want to spend a ton of time this week.
199 00:20:19.870 ⇒ 00:20:23.340 Uttam Kumaran: purely using the tool and finding out these answers
200 00:20:23.540 ⇒ 00:20:27.889 Uttam Kumaran: and then saving that to each of the different areas.
201 00:20:28.000 ⇒ 00:20:34.060 Ryan Luke Daque: do your analysis? Do you like open up like dash and do it there?
202 00:20:34.510 ⇒ 00:20:40.219 Uttam Kumaran: Yeah. So I try to force myself to use light dash, because that’s the tool that
203 00:20:40.240 ⇒ 00:20:51.629 Uttam Kumaran: they have access to. So I try not to run a lot of custom sequel. So what I do. Yeah, I start with a question, right? And I kind of look at the data. And I’m like, Okay, here are like some reasonable questions that
204 00:20:52.160 ⇒ 00:20:59.230 Uttam Kumaran: I was like. If I was in their spot, I would want to know after selling this much, and it’s not really clear to me what the answers are.
205 00:20:59.260 ⇒ 00:21:02.979 Uttam Kumaran: And then I kind of just like, try to make it happen.
206 00:21:03.310 ⇒ 00:21:11.070 Uttam Kumaran: So it starts. It starts with asking really good questions. And that’s why I want to spend a little bit of time today noting those down and getting a little bit more
207 00:21:11.170 ⇒ 00:21:25.200 Uttam Kumaran: aside from them. And then I just yeah, I just go one by one and try to answer. and then, you know, you’ll quickly find out areas in light dash where we need to improve. You know
208 00:21:25.320 ⇒ 00:21:27.010 Uttam Kumaran: where it’s like we
209 00:21:27.170 ⇒ 00:21:36.480 Uttam Kumaran: like, oh, we we missed like we missed. We need to do more segmentation, or we need to add a filter or need to add a dimension. And so
210 00:21:36.710 ⇒ 00:21:46.249 Uttam Kumaran: this is the process, I think, will help us kind of make those updates really quickly. And then also, it’s nice for the client, because we can actually send them a lot of insights about the data.
211 00:21:47.210 ⇒ 00:21:48.480 Ryan Luke Daque: Yeah.
212 00:21:48.950 ⇒ 00:21:50.879 Uttam Kumaran: So that’s what I want to do.
213 00:21:52.710 ⇒ 00:21:54.859 Ryan Luke Daque: Okay, makes sense.
214 00:21:56.870 ⇒ 00:22:07.579 Uttam Kumaran: Okay, cool. So I so I’ll look to kind of chat with you on slack about that. The only other kind of process thing I wanna do is so one
215 00:22:07.670 ⇒ 00:22:15.740 Uttam Kumaran: I know we’ve been doing a daily audits. How’s that process going? I know some. We I know we’re not able to match everything right now.
216 00:22:15.820 ⇒ 00:22:24.770 Uttam Kumaran: I think maybe we could. We could slow down to doing that process every other day and that way.
217 00:22:25.220 ⇒ 00:22:30.930 Uttam Kumaran: cause I’ve been trying. I’ve been telling them that we’ve we’ve been checking, and they’ve been looking at the logs
218 00:22:31.050 ⇒ 00:22:35.600 Uttam Kumaran: so we can maybe move to every other day and hopefully, kind of like
219 00:22:35.640 ⇒ 00:22:40.000 Uttam Kumaran: make that more like every like once or twice a week. We kind of look at that.
220 00:22:40.260 ⇒ 00:22:46.249 Ryan Luke Daque: Yeah, sure, we can do that. So maybe Monday, Wednesday, Friday. We can do this.
221 00:22:46.310 ⇒ 00:22:56.409 Uttam Kumaran: Yeah, let’s let’s plan on that, for now and then. The other thing is, I’m gonna send. I’m gonna have slack set up like a end of day
222 00:22:56.570 ⇒ 00:23:02.000 Uttam Kumaran: reminder a notification that way, both of us at the end. It was just
223 00:23:02.090 ⇒ 00:23:26.600 Uttam Kumaran: for me. My! My day is forever so it’s it’s mainly like when when you log off we can both put on. Hey, this is what we got done today that way. It’s a good. If, for example, if you got stuck on something, I’m happy to take that on, or if I get stuck on something, and you wake up and see that you can kind of take that on. And so hopefully, it could just give us a status update of all the tickets and progress.
224 00:23:26.880 ⇒ 00:23:30.359 Ryan Luke Daque: That’s a good idea, actually.
225 00:23:30.480 ⇒ 00:23:38.990 Uttam Kumaran: what time? So right now, here it’s what time is it now? It’s 8, 54 in the morning.
226 00:23:39.180 ⇒ 00:23:42.499 Ryan Luke Daque: What time do you think is a good time to
227 00:23:43.740 ⇒ 00:23:45.080 Uttam Kumaran: to do that.
228 00:23:45.530 ⇒ 00:23:46.750 Ryan Luke Daque: I guess.
229 00:23:47.370 ⇒ 00:23:50.490 Ryan Luke Daque: maybe
230 00:23:50.900 ⇒ 00:23:56.099 Ryan Luke Daque: 2 or 3 Pm. Your time would that would be like 4 or 5 am
231 00:23:56.380 ⇒ 00:23:57.430 Ryan Luke Daque: my time.
232 00:23:57.840 ⇒ 00:23:58.550 Ryan Luke Daque: Yeah.
233 00:23:58.910 ⇒ 00:24:02.119 Uttam Kumaran: So why don’t we do? Why don’t we do 2 pm.
234 00:24:02.250 ⇒ 00:24:03.060 Ryan Luke Daque: yeah.
235 00:24:03.330 ⇒ 00:24:12.019 Uttam Kumaran: And then, yeah, pretty much. It’s like the opposite of the morning stand up, which is just like. what? What did you get dialed? Is there? Is there anything that’s blocked
236 00:24:12.080 ⇒ 00:24:13.600 Uttam Kumaran: or any issues?
237 00:24:14.060 ⇒ 00:24:14.870 Ryan Luke Daque: Sure.
238 00:24:15.610 ⇒ 00:24:16.740 Uttam Kumaran: Cool. Okay.
239 00:24:17.350 ⇒ 00:24:34.520 Uttam Kumaran: The only other thing that’s happening in my world is, I’m I’m having a friend of mine who is a really really good dashboarding data visualization person interview with that company to see whether he can come on, and.
240 00:24:34.690 ⇒ 00:24:42.009 Uttam Kumaran: you know, work on some dashboards and like kind of make our dashboards look way better. So, having a call with them today about that.
241 00:24:42.080 ⇒ 00:24:44.859 Uttam Kumaran: The second thing that
242 00:24:44.900 ⇒ 00:25:06.110 Uttam Kumaran: I’m spending a quite a bit of time this week on some marketing stuff for for brain force for the company. So really, I think I’m gonna be spending up like as much time as I can doing analysis. But I’m kinda gonna need to lead on you a little bit, because I’m spending a ton of time on some external content and marketing and things like that. So
243 00:25:08.010 ⇒ 00:25:12.539 Uttam Kumaran: yeah, mainly, I’m I’m just not taking on a too many tickets this week, because
244 00:25:12.550 ⇒ 00:25:16.290 Uttam Kumaran: kind of need to focus on some stuff externally and some sales stuff. So
245 00:25:17.010 ⇒ 00:25:18.019 Ryan Luke Daque: no way. So.
246 00:25:18.590 ⇒ 00:25:26.020 Uttam Kumaran: yeah. So I think it’s gonna be interesting week, I wanna see what they think about spending this week doing that sort of
247 00:25:26.280 ⇒ 00:25:36.709 Uttam Kumaran: during doing like trying to spend time doing this analysis. And then hopefully, we can write up A. We can kind of send them questions and stuff as we find out answers. But I think it’ll be effective.
248 00:25:37.670 ⇒ 00:25:38.540 Ryan Luke Daque: Okay.
249 00:25:40.130 ⇒ 00:25:40.880 Ryan Luke Daque: yeah.
250 00:25:42.750 ⇒ 00:25:44.979 Uttam Kumaran: okay, cool anything else.
251 00:25:47.150 ⇒ 00:25:58.030 Ryan Luke Daque: Yeah, I think I’m good. II guess for today I’ll I’ll work on these data models. And if I complete these by today, we can. I can start working on
252 00:25:58.080 ⇒ 00:25:59.690 Ryan Luke Daque: the analysis as well.
253 00:25:59.980 ⇒ 00:26:03.330 Ryan Luke Daque: Like, maybe even just add questions here.
254 00:26:03.370 ⇒ 00:26:06.289 Ryan Luke Daque: That would be like, yeah, related piece.
255 00:26:06.610 ⇒ 00:26:08.870 Uttam Kumaran: Okay, okay, perfect. Cool.
256 00:26:10.030 ⇒ 00:26:12.080 Uttam Kumaran: I think
257 00:26:13.240 ⇒ 00:26:17.549 Uttam Kumaran: that’s it. I wonder if there’s anything else I may have thought of something.
258 00:26:18.730 ⇒ 00:26:21.000 Uttam Kumaran: Yeah, I think that’s it, for now
259 00:26:21.150 ⇒ 00:26:27.290 Ryan Luke Daque: sounds good. Just slack if you have to think of anything else. Certainly. Yeah. You
260 00:26:28.060 ⇒ 00:26:29.310 Uttam Kumaran: okay, definitely
261 00:26:29.680 ⇒ 00:26:30.550 Ryan Luke Daque: goes back.
262 00:26:31.220 ⇒ 00:26:32.650 Uttam Kumaran: Okay, thanks, Ryan.
263 00:26:32.810 ⇒ 00:26:35.200 Ryan Luke Daque: Thanks of them. Have a nice
264 00:26:35.290 ⇒ 00:26:36.730 Uttam Kumaran: you, too. Delay, bye.