Meeting Title: [Javvy] Daily Standup Date: 2025-03-31 Meeting participants: Aakash Tandel, Annie Yu, Robert Tseng, Caio Velasco
WEBVTT
1 00:00:23.920 ⇒ 00:00:25.049 Caio Velasco: Hello! Rakesh!
2 00:00:36.970 ⇒ 00:00:38.000 Aakash Tandel: Hello! How’s it going
3 00:00:38.580 ⇒ 00:00:39.589 Caio Velasco: Good! How are you?
4 00:00:40.870 ⇒ 00:00:42.000 Aakash Tandel: Pretty good.
5 00:00:42.280 ⇒ 00:00:44.880 Aakash Tandel: Let me get the board up
6 00:01:14.990 ⇒ 00:01:30.199 Aakash Tandel: alright. I know that away. She isn’t able to meet today because he’s out sick, so I think it’s just the 3 of us. It’ll be an easy stand up. That’s always nice. Annie, how’s it going you want to start with you.
7 00:01:38.290 ⇒ 00:01:42.080 Aakash Tandel: saw you come off mute and then mute again. Maybe I can start with Kyle
8 00:01:42.810 ⇒ 00:01:43.980 Caio Velasco: Yes, perfect.
9 00:01:44.470 ⇒ 00:01:53.040 Aakash Tandel: Sure. Okay, let’s do it. Let me switch it to Kyle. All right.
10 00:01:53.220 ⇒ 00:01:58.170 Aakash Tandel: You got some awesome stuff done which is cool. Do you want to talk through these real, quick.
11 00:01:58.760 ⇒ 00:02:01.000 Aakash Tandel: gross margin, dashboard.
12 00:02:01.000 ⇒ 00:02:12.601 Caio Velasco: Yeah, I think the 1st one. It’s it’s done across. Everything that has. They had total product cost in their names of the models. I think those are done.
13 00:02:13.840 ⇒ 00:02:19.259 Caio Velasco: and the other one. Yes. So yeah, the same thing. Yeah, yeah.
14 00:02:19.480 ⇒ 00:02:26.949 Aakash Tandel: Okay, cool. Next couple are, bring in Amazon cogs sheet into dim products data model.
15 00:02:27.723 ⇒ 00:02:31.059 Aakash Tandel: Saw you message about this. So it sounds like this is in progress.
16 00:02:31.500 ⇒ 00:02:48.169 Caio Velasco: Yes, I saw Robert’s message today, and then I write a message Aman about it. I tried to be very concise and organized so that he can help us. And now I understand what we wanna do. Basically, what we wanna do is like, build a call
17 00:02:48.980 ⇒ 00:02:57.389 Caio Velasco: a assumptions and costs sorry cost and fees assumptions. To calculate
18 00:02:57.882 ⇒ 00:03:16.510 Caio Velasco: sqs, calls, level data kind of thing. So that’s what we wanna do. And we already had something for shopify nice with some more organized spreadsheet. But now the Amazon one, it’s a bit messy, so we’re trying to see if they can help us with that
19 00:03:17.080 ⇒ 00:03:41.699 Aakash Tandel: Okay, cool. Yeah. So it sounds like the cogs. Data is coming from some sort of spreadsheet that they’re using. Is that that’s correct. Okay, yeah. So a lot of this stuff like, so the fully loaded cogs here is also the same thing. Okay, cool. Alright. That’s good to know. Awesome. Yeah, hopefully, long term, we can find a better way than a spreadsheet. But you know, for now this makes sense. Yeah.
20 00:03:41.700 ⇒ 00:03:54.729 Caio Velasco: Yeah. And I think, based on those things. After we also have all the skews we are using for Amazon, and and the one we already have for shopify, and then we are bringing that into a table called the Product. That would be the other thing
21 00:03:55.350 ⇒ 00:04:20.360 Aakash Tandel: Okay, cool. Yeah. So okay, cool. We’ll just wanna make sure that we’re like, very clear with the client like, Hey, some of these data things like, you guys need to be buttoned up. Otherwise it comes downstream. So so that we don’t. You know, they’re like the data is here is wrong. It’s like, whatever, okay, cool. Let’s let’s find the source of that data. Is it the Api that we’re pinging? Or is it actually the the Google sheet that we’re using that type of thing. So
22 00:04:20.649 ⇒ 00:04:21.289 Caio Velasco: Okay.
23 00:04:22.150 ⇒ 00:04:26.969 Aakash Tandel: Sounds good. Okay? And then confirm with them on, okay, so that’s that’s these are kind of tied together
24 00:04:27.170 ⇒ 00:04:28.329 Caio Velasco: Yeah, all of them
25 00:04:28.720 ⇒ 00:04:45.738 Aakash Tandel: Oh, all 3 of them. Okay? Oh, yeah. Okay. Cool. Alright. This sounds like you got good progress in there. Let me know if you need me to step in with Combo with Aman. I’m meeting with him today to talk about the roadmap thing that we showed on. Maybe Friday.
26 00:04:46.710 ⇒ 00:04:59.329 Aakash Tandel: yeah, so I can. I can let ask him just to. If he hasn’t responded to your thing. I’ll just Hey, I’ll say, Hey, we’re working on that bit of information. So once you have that kind of keep working
27 00:04:59.590 ⇒ 00:05:00.780 Caio Velasco: Okay, perfect.
28 00:05:01.050 ⇒ 00:05:01.720 Aakash Tandel: Cool.
29 00:05:01.990 ⇒ 00:05:02.370 Caio Velasco: Okay.
30 00:05:02.970 ⇒ 00:05:08.120 Aakash Tandel: Yeah, thanks. Okay. And then let me switch over to Annie
31 00:05:09.520 ⇒ 00:05:20.130 Annie Yu: Yeah. Hi, sorry about the rookie mistake. I saw. So I guess we can start with the the one that I
32 00:05:20.560 ⇒ 00:05:25.109 Annie Yu: we are able to close is the migrating dashboard. This one is done
33 00:05:29.700 ⇒ 00:05:30.300 Aakash Tandel: Okay.
34 00:05:30.300 ⇒ 00:05:32.480 Annie Yu: And another one that’s
35 00:05:33.020 ⇒ 00:05:43.900 Annie Yu: add tab to Gorgea dash to answer questions. So I was able to kind of update the dashboards with the model that always built. So this is
36 00:05:44.240 ⇒ 00:05:46.929 Annie Yu: done. But also when I was
37 00:05:47.150 ⇒ 00:05:49.549 Annie Yu: testing kind of the data, I
38 00:05:49.730 ⇒ 00:06:10.449 Annie Yu: send a Pr for a ways to review. And there was just like a couple of things that I request to change. It should be very easy to understand. I’m not sure if we should wait for him to review, and I also just got this done last Friday after he was off, so I’m not sure if we
39 00:06:10.660 ⇒ 00:06:11.620 Annie Yu: should wait
40 00:06:11.840 ⇒ 00:06:24.630 Aakash Tandel: Yeah, let’s wait for him to review it. And then, once that gets merged, then we’ll close this ticket out he’s yeah, like like I mentioned. He’s I think he’s out sick today. So this got pushed a little. That’s okay.
41 00:06:25.300 ⇒ 00:06:30.950 Aakash Tandel: cool alright. And actually, I’ll just say edit.
42 00:06:31.330 ⇒ 00:06:37.330 Aakash Tandel: Once the Pr is merged, we will close this ticket.
43 00:06:37.870 ⇒ 00:06:40.870 Aakash Tandel: Cool. Okay, and
44 00:06:41.410 ⇒ 00:07:04.870 Annie Yu: And then another thing is the monthly cohort retention inspired by lifetimely. And this was my bad. I think I underestimated the time. But then I eventually realized we needed to build a model. So I met with last Friday and he, I think he will be working on building out the model. And then we can get that moving
45 00:07:05.550 ⇒ 00:07:18.369 Aakash Tandel: Okay, actually, I’m gonna discard. Okay? So Annie and a leash
46 00:07:18.490 ⇒ 00:07:21.560 Aakash Tandel: met on 3. Was that 28
47 00:07:21.560 ⇒ 00:07:22.700 Annie Yu: 28, yeah.
48 00:07:23.630 ⇒ 00:07:32.439 Aakash Tandel: To discuss building model. Does oas have a ticket for that by any chance
49 00:07:33.530 ⇒ 00:07:35.960 Annie Yu: Probably not.
50 00:07:36.260 ⇒ 00:07:38.559 Robert Tseng: I don’t think he does. I was looking at it this morning.
51 00:07:38.710 ⇒ 00:07:43.570 Aakash Tandel: Oh, here we are. Okay, cool. I will.
52 00:07:44.020 ⇒ 00:07:45.170 Aakash Tandel: I’ll create a ticket
53 00:07:50.610 ⇒ 00:07:51.540 Aakash Tandel: at least.
54 00:07:51.800 ⇒ 00:07:52.690 Aakash Tandel: Tickets.
55 00:07:54.850 ⇒ 00:07:56.220 Aakash Tandel: Voice.
56 00:07:57.070 ⇒ 00:07:59.789 Aakash Tandel: Oh, it should. Okay.
57 00:08:00.270 ⇒ 00:08:11.970 Aakash Tandel: Alright, that sounds good. So that so this is I’m gonna put this as blocked because technically, you can’t move forward on it until we make that model change. So I’m gonna move that there.
58 00:08:13.490 ⇒ 00:08:14.210 Aakash Tandel: Okay.
59 00:08:14.853 ⇒ 00:08:20.926 Aakash Tandel: and then this is almost done. And then so this is the only thing kind of like in flight for you. Then I guess
60 00:08:22.234 ⇒ 00:08:33.230 Annie Yu: Yeah, we me and wish also talk about this. And I think, he said, because he’s never touched this data so he will have to explore it before we can
61 00:08:33.850 ⇒ 00:08:36.159 Annie Yu: need and know more about it.
62 00:08:36.309 ⇒ 00:08:42.499 Aakash Tandel: Okay, I’m gonna say, so, maybe we’ll so we
63 00:08:43.509 ⇒ 00:08:51.139 Aakash Tandel: to learn about data. And then sync with Danny.
64 00:08:51.379 ⇒ 00:09:00.589 Aakash Tandel: And they, okay, that sounds fine. I know that this is one of the items
65 00:09:01.119 ⇒ 00:09:08.659 Aakash Tandel: north beam dashboard. Aman said, that we’ve already modeled the data. Is that true?
66 00:09:09.209 ⇒ 00:09:11.799 Aakash Tandel: I don’t know. I wish isn’t on this call, so I guess she
67 00:09:11.800 ⇒ 00:09:12.899 Robert Tseng: No, it’s not true.
68 00:09:13.110 ⇒ 00:09:14.380 Aakash Tandel: Okay, so that’s not
69 00:09:14.380 ⇒ 00:09:22.612 Robert Tseng: But we have been telling him that we’ve been working with it for a while, so it’s I don’t know. It’s kind of a how honestly we wanna be about this
70 00:09:22.870 ⇒ 00:09:28.510 Aakash Tandel: Okay, okay. So we’ve been working with north beam specifically, but just not building models for it.
71 00:09:29.270 ⇒ 00:09:29.950 Robert Tseng: Yeah.
72 00:09:30.080 ⇒ 00:09:30.680 Aakash Tandel: Okay.
73 00:09:31.839 ⇒ 00:09:32.149 Aakash Tandel: Okay.
74 00:09:32.150 ⇒ 00:09:37.689 Annie Yu: And in the meantime, is there any data tables I can look into or not?
75 00:09:37.690 ⇒ 00:09:40.649 Aakash Tandel: That’s exactly what I was. Gonna ask let me see.
76 00:09:41.000 ⇒ 00:09:42.470 Aakash Tandel: Robert, do you know the answer to that
77 00:09:42.880 ⇒ 00:09:52.120 Robert Tseng: Yeah, I don’t know the progress of how much of the north data we brought in like we had the Sync before through 5 Tran, but I don’t. I don’t know if we have it on right now.
78 00:09:53.510 ⇒ 00:09:57.090 Aakash Tandel: Let me see if there’s there’s other creds in one password.
79 00:09:58.400 ⇒ 00:09:58.910 Aakash Tandel: Probably
80 00:09:58.910 ⇒ 00:09:59.830 Robert Tseng: Personally.
81 00:10:00.557 ⇒ 00:10:01.780 Aakash Tandel: For north beam
82 00:10:02.120 ⇒ 00:10:04.840 Robert Tseng: For north beam. Yeah, we we do.
83 00:10:05.660 ⇒ 00:10:06.310 Aakash Tandel: Okay.
84 00:10:06.590 ⇒ 00:10:07.919 Robert Tseng: It’s Nico’s account
85 00:10:07.920 ⇒ 00:10:19.720 Aakash Tandel: Okay, cool. So, Andy, yeah, we can spend some time, Annie, to learn about Earth beam data.
86 00:10:20.180 ⇒ 00:10:25.759 Robert Tseng: I think the amplitude dashboard. If you look at it later. It pretty much is just like
87 00:10:26.230 ⇒ 00:10:27.936 Robert Tseng: channel level.
88 00:10:29.950 ⇒ 00:10:36.709 Robert Tseng: that like spend. I think that’s not even spent. I think it’s just which orders came from which channels so.
89 00:10:37.060 ⇒ 00:10:37.740 Robert Tseng: Oh.
90 00:10:39.010 ⇒ 00:10:46.490 Robert Tseng: if we I don’t even know how much we need from north Beam, like if we just have labels on the orders of like.
91 00:10:47.270 ⇒ 00:10:54.519 Robert Tseng: I don’t know Utms, or some something like it’s probably just like one or 2 fields that we need to bring into our orders table
92 00:10:54.750 ⇒ 00:10:56.050 Aakash Tandel: Okay, that’s my guess.
93 00:10:57.550 ⇒ 00:11:04.371 Aakash Tandel: Might be a few utm parameters to bring into the orders table.
94 00:11:05.160 ⇒ 00:11:13.069 Annie Yu: Does that mean? I can work, start working on replicating the visuals
95 00:11:14.030 ⇒ 00:11:16.760 Annie Yu: with the Meta base. Is that what you’re saying, Robert?
96 00:11:17.601 ⇒ 00:11:22.780 Robert Tseng: Yeah, I mean, they want to bring the amplitude reporting into metabase.
97 00:11:24.360 ⇒ 00:11:28.080 Robert Tseng: I I would say, like, if the modeling is unclear. Yeah, definitely have
98 00:11:29.060 ⇒ 00:11:33.599 Robert Tseng: a way to like, figure out what we have and don’t have. And like I, we can.
99 00:11:34.090 ⇒ 00:11:40.520 Robert Tseng: We don’t have to rush, rush this one like. I don’t. I don’t think we need to like build a design before we have data
100 00:11:40.690 ⇒ 00:11:41.540 Annie Yu: Yes.
101 00:11:41.690 ⇒ 00:11:42.320 Robert Tseng: Gotcha
102 00:11:44.170 ⇒ 00:11:46.629 Aakash Tandel: Okay, okay, actually.
103 00:11:46.630 ⇒ 00:11:56.889 Robert Tseng: This is something I want to talk to you separately about as well. But I I was. I was telling you, Tom, over the weekend just just quite quick tangent for the team. But like.
104 00:11:57.100 ⇒ 00:12:02.869 Robert Tseng: I feel like it’s not the best use of our time to do custom modeling for these marketing
105 00:12:03.860 ⇒ 00:12:05.170 Robert Tseng: data sources
106 00:12:06.050 ⇒ 00:12:12.099 Robert Tseng: I I feel like it’s just like a lose- lose situation.
107 00:12:12.900 ⇒ 00:12:18.730 Robert Tseng: Marketing data is like always changing and like
108 00:12:19.000 ⇒ 00:12:23.059 Robert Tseng: there are out of the box ways to get them
109 00:12:23.720 ⇒ 00:12:35.549 Robert Tseng: visibility into their marketing data like I might even push back and just be like, dude them on. Just have the team use north beam like, why do we need to bring it in it? Just
110 00:12:35.660 ⇒ 00:12:41.120 Robert Tseng: yeah, until we have a really clear understanding of like what we’re gonna be showing. That’s not
111 00:12:41.370 ⇒ 00:12:51.689 Robert Tseng: that they can’t see in North Beam already. Like I I wanna I don’t. I don’t want us to get hung up on the marketing data, since it’s not really like
112 00:12:52.170 ⇒ 00:12:52.970 Robert Tseng: that.
113 00:12:53.350 ⇒ 00:13:00.089 Robert Tseng: like there’s not. There’s not really that much we can do, like custom work on it that really demonstrates our value.
114 00:13:03.420 ⇒ 00:13:07.279 Aakash Tandel: That makes that makes a lot of sense. Yeah, okay,
115 00:13:08.060 ⇒ 00:13:12.637 Aakash Tandel: So I think, I’m gonna save that comment.
116 00:13:14.220 ⇒ 00:13:18.859 Aakash Tandel: in terms of. Should Annie look into that? Or should we like have
117 00:13:19.070 ⇒ 00:13:20.119 Aakash Tandel: pause? Because I don’t want to
118 00:13:20.120 ⇒ 00:13:35.830 Robert Tseng: Yeah, we can pause that on any. And I think that’s on me, and that you to kind of like figure out what once we understand what capabilities we already have. But if this is like a waste needs like a week of work to like, get the data in, and then we can replicate it like I don’t know if I would commit to that
119 00:13:36.650 ⇒ 00:13:37.270 Aakash Tandel: Yeah.
120 00:13:38.390 ⇒ 00:13:39.240 Aakash Tandel: Okay.
121 00:13:39.880 ⇒ 00:13:52.016 Aakash Tandel: Also, if you guys hear squeaking, that is my child. He is. He’s trying to watch Miss Rachel, but he’s also squeaking about Miss Rachel. I don’t know if you guys know who Miss Rachel is, but it’s the only thing I can keep him like preoccupied with. So it’s pretty funny.
122 00:13:53.350 ⇒ 00:14:04.919 Aakash Tandel: okay, so I’m gonna say, I’m gonna say for me and Robert to reassess
123 00:14:05.300 ⇒ 00:14:15.540 Aakash Tandel: needs with Aman and determine what the end State looks like for this work.
124 00:14:16.775 ⇒ 00:14:18.680 Aakash Tandel: Don’t want to.
125 00:14:20.984 ⇒ 00:14:28.640 Aakash Tandel: Oh, gracious time on something that isn’t super.
126 00:14:29.200 ⇒ 00:14:31.420 Aakash Tandel: I value. Okay?
127 00:14:31.650 ⇒ 00:14:35.210 Aakash Tandel: Cool. Yeah. Okay. So backing up
128 00:14:35.210 ⇒ 00:14:40.600 Annie Yu: Sorry. Can we actually jump back to that? Cohort retention
129 00:14:40.600 ⇒ 00:14:41.260 Aakash Tandel: Sure.
130 00:14:42.010 ⇒ 00:14:49.229 Annie Yu: I do have 1 1 more question for Robert. I. The reason I asked about the net sales, because
131 00:14:49.390 ⇒ 00:14:52.180 Annie Yu: here I think we are trying to
132 00:14:53.730 ⇒ 00:14:58.389 Annie Yu: get this done with gross margin, gross profit and net sales.
133 00:14:58.780 ⇒ 00:15:04.170 Annie Yu: So I’m not sure. Yeah, for this one. Should we do like sales, revenue and
134 00:15:04.170 ⇒ 00:15:09.469 Robert Tseng: Yeah. So I responded to your threads. Thanks, for I don’t know if you if you looked at the messages this morning. But
135 00:15:10.230 ⇒ 00:15:12.490 Robert Tseng: or just did you? I don’t have to repeat myself
136 00:15:14.390 ⇒ 00:15:15.210 Annie Yu: Okay.
137 00:15:15.570 ⇒ 00:15:16.130 Aakash Tandel: Is, that
138 00:15:17.510 ⇒ 00:15:20.648 Robert Tseng: Okay, well, I I guess sounds like you haven’t read it. But basically
139 00:15:21.020 ⇒ 00:15:36.269 Robert Tseng: we don’t actually calculate net sales right now, because, like net sales is truly sales revenue minus refunds and cancellations. And we’re not really doing that anywhere we we could. That would be a net new request.
140 00:15:36.270 ⇒ 00:15:51.959 Robert Tseng: I would say, like we should just use the metrics that we already have. We have gross profit, gross margin. We have sales revenue, so we should just do that. And if they come back and they’re like, Oh, we also want net sales. Then we can talk about bringing in.
141 00:15:52.040 ⇒ 00:15:58.030 Robert Tseng: Then then we can. Then we can build in that calculation. But right now we we don’t actually report on that sales anywhere else.
142 00:15:58.030 ⇒ 00:16:06.520 Annie Yu: Okay, okay, gotcha, to build the model for this one. We had to add
143 00:16:07.110 ⇒ 00:16:17.830 Annie Yu: the columns for cumulative sum. That’s why we kind of have to figure out the calculations before Oish can like successively build that model.
144 00:16:18.810 ⇒ 00:16:24.769 Annie Yu: So for now I guess we’ll just stick with those 3 sales revenue, gross profit, and gross margin
145 00:16:25.140 ⇒ 00:16:31.439 Robert Tseng: Yeah, and the shopify gross margin dashboard, like has the has, like the definitions we’re using.
146 00:16:31.932 ⇒ 00:16:37.189 Robert Tseng: I like, went in and cleaned up the language one more time this this morning as well. So
147 00:16:37.560 ⇒ 00:16:39.210 Robert Tseng: if you refresh.
148 00:16:43.620 ⇒ 00:16:44.830 Robert Tseng: it’s weird that it was like
149 00:16:44.830 ⇒ 00:16:45.260 Aakash Tandel: Oh!
150 00:16:45.260 ⇒ 00:16:47.710 Robert Tseng: Showing. Huh? Wonder why it’s
151 00:16:54.030 ⇒ 00:16:54.910 Robert Tseng: Shoot.
152 00:16:55.480 ⇒ 00:16:56.500 Robert Tseng: That’s not.
153 00:16:57.300 ⇒ 00:16:58.732 Robert Tseng: It’s kind of strange
154 00:16:59.090 ⇒ 00:17:03.450 Aakash Tandel: Let me see if it’s on definitely this one
155 00:17:03.980 ⇒ 00:17:05.240 Robert Tseng: Yeah, yeah, I mean, I guess
156 00:17:07.210 ⇒ 00:17:11.500 Robert Tseng: why, the 30 days seems to break the most popular thing. But
157 00:17:11.880 ⇒ 00:17:12.579 Aakash Tandel: Oh, okay.
158 00:17:12.589 ⇒ 00:17:13.569 Robert Tseng: I’ll look into that.
159 00:17:20.919 ⇒ 00:17:22.479 Robert Tseng: Oh, I see why
160 00:17:28.699 ⇒ 00:17:31.579 Aakash Tandel: Cool. Okay, let me go back to here.
161 00:17:31.960 ⇒ 00:17:34.969 Aakash Tandel: Okay, so for now we’re gonna stick with kind of what we have
162 00:17:35.990 ⇒ 00:17:36.620 Robert Tseng: Yeah.
163 00:17:36.820 ⇒ 00:17:38.389 Annie Yu: Okay, that sounds good.
164 00:17:43.490 ⇒ 00:17:50.294 Aakash Tandel: Alright. So backing up Annie, it looks like this is in progress.
165 00:17:51.490 ⇒ 00:17:56.100 Aakash Tandel: and it’s not really on you. It’s actually, I’m gonna reassign this to a wish.
166 00:17:59.650 ⇒ 00:18:03.070 Aakash Tandel: So we’re done with this. These are kind of blocked.
167 00:18:04.072 ⇒ 00:18:15.500 Aakash Tandel: So okay, that sounds fine. I think you might have more bandwidth for Eden and other projects than at the moment. But yeah, I think we’re kind of in a holding pattern for some of these, so that works
168 00:18:16.700 ⇒ 00:18:17.490 Annie Yu: Thanks.
169 00:18:17.930 ⇒ 00:18:21.620 Aakash Tandel: Thanks. Robert, do we? Should we look at your stuff, too.
170 00:18:22.700 ⇒ 00:18:23.590 Robert Tseng: Yeah.
171 00:18:26.190 ⇒ 00:18:28.420 Aakash Tandel: Where do you want to start testing
172 00:18:29.800 ⇒ 00:18:42.329 Robert Tseng: yeah. So that’s that’s the on the shopify side. I I guess I can kinda share basically, like, there are a few changes that I was working on like late last week. One was I worked with the ways to clean up the
173 00:18:42.760 ⇒ 00:18:54.459 Robert Tseng: We Consolidated the they were like 4 or 5 platform fees. It was kind of confusing. So now we just have one platform fee, one processing fee. So this is in the fact orders tables, and that model. And that should.
174 00:18:54.700 ⇒ 00:18:56.410 Robert Tseng: Yeah, that should be simpler.
175 00:18:56.880 ⇒ 00:19:01.989 Robert Tseng: And then, yeah, I think Annie actually called out a change for me. It was like.
176 00:19:02.520 ⇒ 00:19:16.365 Robert Tseng: should sales revenue be like. It was a bit inconsistent, like we were, including tax in revenue, but then we were excluding it in profit, and actually we should just exclude it overall. So I just took out
177 00:19:17.780 ⇒ 00:19:21.735 Robert Tseng: tax from anything that is revenue.
178 00:19:22.920 ⇒ 00:19:25.709 Robert Tseng: yeah, it’s a bit tedious, because like
179 00:19:26.750 ⇒ 00:19:36.399 Robert Tseng: total price is like the simple field that we were using for sales revenue before. But that includes tax. And so it’s a bit more involved to like.
180 00:19:36.800 ⇒ 00:19:43.680 Robert Tseng: do to get to the Pre tax calculation. But anyway, I I did that. And I think
181 00:19:44.220 ⇒ 00:19:48.310 Robert Tseng: I mean it’s it’s also quite tedious to Update it across every
182 00:19:48.650 ⇒ 00:19:56.999 Robert Tseng: every report on that dashboard because you have to go and click buttons across like 20 things. But I did, and I think that’s should should be good. Now
183 00:19:57.390 ⇒ 00:20:03.539 Robert Tseng: that was like the main thing. I was like hopefully closing out the
184 00:20:03.880 ⇒ 00:20:07.159 Robert Tseng: shopify dashboard. So no more changes have to be made there
185 00:20:14.110 ⇒ 00:20:17.449 Aakash Tandel: That sounds good. Where did I just lost that guy?
186 00:20:18.914 ⇒ 00:20:27.949 Aakash Tandel: This day. Okay, cool. That sounds good. Let me go back. So this is so this is waiting for review from Aman. Is that what’s happening
187 00:20:28.975 ⇒ 00:20:29.569 Robert Tseng: Yeah.
188 00:20:29.690 ⇒ 00:20:30.410 Aakash Tandel: Okay, cool.
189 00:20:31.290 ⇒ 00:20:33.279 Aakash Tandel: So waiting.
190 00:20:36.160 ⇒ 00:20:43.929 Aakash Tandel: Okay, that makes sense. Why, it’s in testing. Okay, cool. Anything on this guy. The cancellation orders
191 00:20:44.481 ⇒ 00:20:51.270 Robert Tseng: Yeah, no, I never ended up sending that to them on. So I started putting together a doc. But I’ll I’ll probably do that today.
192 00:20:51.270 ⇒ 00:20:52.160 Aakash Tandel: Okay, so this is
193 00:20:52.160 ⇒ 00:20:56.819 Robert Tseng: I don’t really know how much we’re gonna find from this, but this is really just to put him at ease at like.
194 00:20:57.860 ⇒ 00:21:09.780 Robert Tseng: Well, we. We started this investigation on the Amazon cancellation stuff. I think the main points are really just to share some of Kyle’s findings, and then, like, have some
195 00:21:10.390 ⇒ 00:21:16.600 Robert Tseng: like recommendation at the end. Which, yeah, I I just need to set aside an hour to to do it
196 00:21:21.530 ⇒ 00:21:23.460 Aakash Tandel: Endings. Okay, cool.
197 00:21:23.750 ⇒ 00:21:26.530 Aakash Tandel: I’m gonna put that in testing so that
198 00:21:26.760 ⇒ 00:21:29.020 Aakash Tandel: you know, it was weighing on his review. But
199 00:21:29.580 ⇒ 00:21:33.510 Aakash Tandel: okay, that sounds good. And then we’re still blocked on this guy
200 00:21:33.930 ⇒ 00:21:48.720 Robert Tseng: Yeah, that’s really just well, we don’t have subscribe and save. And then, in order to bring the margin reports over from shopify, basically make an Amazon version of that dashboard. We need to have the cogs sheet ready, which I think Kyle is kind of
201 00:21:48.720 ⇒ 00:21:49.150 Aakash Tandel: Yep.
202 00:21:49.150 ⇒ 00:21:50.770 Robert Tseng: Leading, that now which
203 00:21:50.770 ⇒ 00:21:54.959 Aakash Tandel: Yeah, we already went through Kyle, Santa, but yeah, he had his tickets on that one, so that
204 00:21:54.960 ⇒ 00:21:55.490 Robert Tseng: Yeah.
205 00:21:55.490 ⇒ 00:22:02.730 Aakash Tandel: That sounds good. And then, okay, and I’ll just add waiting on cogs.
206 00:22:03.370 ⇒ 00:22:08.670 Aakash Tandel: Sheet work for client and Kyle.
207 00:22:09.865 ⇒ 00:22:26.960 Aakash Tandel: And don’t have Amazon subscribe subscribe and save the through any yeah provider.
208 00:22:27.590 ⇒ 00:22:35.769 Aakash Tandel: Yes, and that will. The second part’s kind of still a big blocker. Because we don’t
209 00:22:35.770 ⇒ 00:22:36.960 Robert Tseng: Meeting Aman today
210 00:22:36.960 ⇒ 00:22:38.609 Aakash Tandel: I am meeting with them on today. Yep.
211 00:22:39.230 ⇒ 00:22:40.420 Robert Tseng: Oh, when.
212 00:22:40.420 ⇒ 00:22:43.389 Aakash Tandel: 1230. You’re on the invite. If you want to join
213 00:22:43.730 ⇒ 00:22:44.430 Robert Tseng: Okay.
214 00:22:44.860 ⇒ 00:22:47.930 Robert Tseng: Alright, yeah. I want to try to get the north beam kind of
215 00:22:49.380 ⇒ 00:22:53.080 Robert Tseng: stuff figured out before we talk to him. Then, because I’m sure he’ll ask about it.
216 00:22:53.080 ⇒ 00:22:54.270 Aakash Tandel: Yeah, that sounds good.
217 00:22:54.450 ⇒ 00:22:54.800 Robert Tseng: Yeah.
218 00:22:56.121 ⇒ 00:23:00.140 Aakash Tandel: Anything else for standup kind of any
219 00:23:00.400 ⇒ 00:23:01.970 Caio Velasco: No nothing listed
220 00:23:02.250 ⇒ 00:23:02.790 Aakash Tandel: Okay.
221 00:23:03.490 ⇒ 00:23:04.370 Aakash Tandel: Awesome.
222 00:23:05.130 ⇒ 00:23:09.960 Aakash Tandel: Thanks. Y’all, Robert, do you want to stick on for a minute? We can talk through Northum stuff
223 00:23:10.200 ⇒ 00:23:11.670 Robert Tseng: Yeah, let’s do that.
224 00:23:11.670 ⇒ 00:23:14.829 Aakash Tandel: Okay, feel free to drop
225 00:23:14.830 ⇒ 00:23:15.500 Robert Tseng: Alright! See you next one
226 00:23:20.410 ⇒ 00:23:21.310 Aakash Tandel: All right.
227 00:23:22.940 ⇒ 00:23:24.339 Aakash Tandel: No.
228 00:23:24.340 ⇒ 00:23:28.270 Robert Tseng: Yeah, I’m just opening up the amplitude dash and trying to review this
229 00:23:28.790 ⇒ 00:23:32.020 Aakash Tandel: I can stop my screen share. Then if you wanna hold up
230 00:23:46.850 ⇒ 00:23:47.230 Robert Tseng: But
231 00:23:47.230 ⇒ 00:23:49.990 Aakash Tandel: Like, not happy with amplitude. Is that the idea or
232 00:23:50.850 ⇒ 00:23:54.210 Robert Tseng: Well, I think Aman has this perception now that like.
233 00:23:54.460 ⇒ 00:23:58.459 Robert Tseng: okay, now that we have all this data
234 00:23:58.610 ⇒ 00:24:08.313 Robert Tseng: model than our down data warehouse. The numbers are all gonna be different. Like we’re, it’s it’s more accurate kind of going from there. We prove that out
235 00:24:09.250 ⇒ 00:24:14.300 Robert Tseng: before, like, I I think I could kind of be here. Something.
236 00:24:19.520 ⇒ 00:24:20.240 Robert Tseng: hey?
237 00:24:29.100 ⇒ 00:24:44.830 Robert Tseng: Yeah. So this was like a slide that we shared with him a while ago. That was basically showing, hey? Like you were off by, you know, 20% in that sales 40% in shipping by just relying on amplitude, reporting
238 00:24:44.830 ⇒ 00:24:45.280 Aakash Tandel: Yeah, I’m good.
239 00:24:45.622 ⇒ 00:24:52.470 Robert Tseng: So obviously, this pretty big spread, it’s like 24% depending on the on the cost category there.
240 00:24:52.780 ⇒ 00:25:12.530 Robert Tseng: or depending on the on the balance sheet line. Item, and so now I think he, he believes that, like anything that’s tied to revenue any revenue related reporting that exists in amplitude should be brought into like this workflow, so that we can have more accurate reporting
241 00:25:13.690 ⇒ 00:25:14.290 Aakash Tandel: That makes sense.
242 00:25:15.400 ⇒ 00:25:17.929 Aakash Tandel: Yeah, amplitude is not great at this type of data.
243 00:25:18.620 ⇒ 00:25:30.469 Robert Tseng: Yeah. Well, I mean to be honest, it’s really just like the assumptions that they made an amplitude are not the same that the ones that we made they. So that’s why it’s different. And they’re obviously missing.
244 00:25:30.810 ⇒ 00:25:41.139 Robert Tseng: You know, there’s always some leakage like, not every order. Yeah. Amplitude is really not meant for order tracking and stuff. So, yeah, so they were just yeah. There was some mess ups there.
245 00:25:43.830 ⇒ 00:26:08.609 Robert Tseng: yeah, before we came in. There was no way to like update data. So whenever there was an outage and they need to backfill data he would send like he would send post requests into amplitude to backfill data, or if, like a bunch of orders got canceled and they woke up, the statuses were up, reflected in amplitude. He would have to. So I feel like that’s how he spent all of his time before we were here, just doing like data recon work.
246 00:26:09.009 ⇒ 00:26:16.989 Robert Tseng: So I think he obviously he’s bought into what we’re doing now. And he saves him from having to do any bad stuff anymore.
247 00:26:17.943 ⇒ 00:26:26.160 Robert Tseng: That said as I’m like looking through this Northeas metrics dashboard like to me. Obviously, Cac Cac, by product is like the main thing that I see here.
248 00:26:27.038 ⇒ 00:26:31.200 Robert Tseng: That is probably a number that’s pulled straight from north beam.
249 00:26:34.360 ⇒ 00:26:41.709 Robert Tseng: Yeah. Internal north. Theme import like spend is probably coming from north theme. Yeah, it’s just like some
250 00:26:42.800 ⇒ 00:26:46.099 Robert Tseng: property that they’re pulling from north beam hacked.
251 00:26:52.020 ⇒ 00:26:59.300 Robert Tseng: well, I suppose it’s just spend over revenue, so the revenue number, their denominator is probably off
252 00:27:02.040 ⇒ 00:27:04.789 Robert Tseng: so their cap is probably
253 00:27:07.680 ⇒ 00:27:11.600 Robert Tseng: lower than it actually is. As my guess.
254 00:27:12.466 ⇒ 00:27:21.020 Robert Tseng: Yeah. So they have weekly. They have daily by product, just concentrate versus protein. So yeah, I mean, we have the underlying model. So like, kind of
255 00:27:21.230 ⇒ 00:27:29.350 Robert Tseng: bought all this like, yeah, we we’re just we’re missing spend by product. So we do need, like a marketing, spend kind of like
256 00:27:30.140 ⇒ 00:27:32.442 Robert Tseng: model in our
257 00:27:35.440 ⇒ 00:27:41.109 Robert Tseng: in the data warehouse, which is okay. If we pull that straight from north Beam, and we just call North Beam the source of truth.
258 00:27:43.480 ⇒ 00:27:45.100 Robert Tseng: I’m okay with doing that
259 00:27:45.200 ⇒ 00:27:47.740 Aakash Tandel: Okay. I don’t think that’s hard to do
260 00:27:48.900 ⇒ 00:27:52.580 Robert Tseng: But I guess what I was saying on the stand up was like, so
261 00:27:52.830 ⇒ 00:27:55.499 Robert Tseng: Northeas just kind of like a
262 00:27:57.190 ⇒ 00:28:15.580 Robert Tseng: it’s like an intermediary tool here, like they’d be. They’re doing the same thing as we are. They’re doing direct connections with all of these like ad spend platforms, and they aggregate it into their own platform. And then we’re getting like, you know, we’re getting the data from them. Whereas, like Eden is like, we don’t want to go north team anymore. We want you to go direct.
263 00:28:15.800 ⇒ 00:28:21.240 Robert Tseng: And I’m like, I don’t wanna have our team do like.
264 00:28:21.380 ⇒ 00:28:26.849 Robert Tseng: yeah, yeah, we we don’t. We don’t need to do that like I think we should. Just
265 00:28:27.965 ⇒ 00:28:43.700 Robert Tseng: we, we should consider other partners or tools that do specifically marketing connectors with some like basic reporting from the marketing side. So that’s that’s like, that’s why I would. I don’t. I don’t want our team to be doing
266 00:28:43.910 ⇒ 00:28:50.119 Robert Tseng: this kind of like attribution, modeling and re- reporting like, yeah, it’s kind of what I was telling you
267 00:28:50.660 ⇒ 00:28:58.060 Aakash Tandel: Yeah, I think leveraging a tool that does. That is definitely the the way to go, because we don’t want to be in the place where we’re like, also maintaining something like that. That’s just gonna be
268 00:28:58.060 ⇒ 00:28:58.920 Robert Tseng: Exactly.
269 00:28:59.623 ⇒ 00:29:05.090 Robert Tseng: And like, you know, you, you never succeed doing this like, we’re always gonna it’s a moving target
270 00:29:06.300 ⇒ 00:29:21.529 Robert Tseng: campaign, naming conventions like always change and things. Things will break in marketing. And so we’re we’re just like, it’s like a highly stressful like scope of work to to maintain. And there’s no real success for us, because we’re always gonna be
271 00:29:21.940 ⇒ 00:29:27.410 Robert Tseng: not to say we shouldn’t do it. I just think that there are easy ways to do it without us having to be that involved.
272 00:29:27.920 ⇒ 00:29:42.209 Robert Tseng: So like this is one of our partners. I know this is like tangent, but like they do, they they have a lot of like, it’s basically Etl out of a box. And so you connect to, you know all, all of the all the outsources that you would want here. So
273 00:29:42.930 ⇒ 00:29:43.670 Robert Tseng: and then
274 00:29:44.970 ⇒ 00:30:05.019 Robert Tseng: it they automatically ha! They already have, like all the modeling for these different sources, productized. And we just it has a simple ui where they can ask questions, and they build like a dashboard for themselves. So I’m thinking of like hooking up something like this. If we ever are being asked to go direct with the with the marketing data sources.
275 00:30:05.782 ⇒ 00:30:13.769 Robert Tseng: If it’s just north theme, I’m okay with, you know, building a single connector. But if we’re having to build like 5 to 10, like I don’t wanna do that.
276 00:30:13.770 ⇒ 00:30:14.360 Aakash Tandel: Yep.
277 00:30:14.600 ⇒ 00:30:15.539 Aakash Tandel: Yeah. Yeah. Anyway.
278 00:30:15.540 ⇒ 00:30:19.620 Robert Tseng: So that’s of a thing I was thinking about over the weekend
279 00:30:20.260 ⇒ 00:30:29.240 Aakash Tandel: Yeah, no, I I definitely agree with all the things you said. There, I think, going direct with a tool like that again. It’s almost just another etl partner.
280 00:30:29.520 ⇒ 00:30:38.680 Robert Tseng: It is. Yeah, they just take it one step further. And like, they have, like some basic reporting on it already. And it’s like, good enough.
281 00:30:40.130 ⇒ 00:30:41.250 Robert Tseng: yeah, cool.
282 00:30:42.090 ⇒ 00:30:43.069 Robert Tseng: That sounds good.
283 00:30:43.070 ⇒ 00:30:49.190 Robert Tseng: Pricing is pricing is cheap, too, like it’s like a hundred dollars per connector per month. And like, it seems pretty simple. So
284 00:30:50.830 ⇒ 00:30:52.140 Aakash Tandel: Yeah, that sounds good.
285 00:30:52.613 ⇒ 00:31:08.759 Aakash Tandel: Okay, cool. So that this is this seems pretty good. We we definitely have to, I guess, get that data into. Or I guess we had to get the data model in for those kind of couple of items in the warehouse. But otherwise we should be.
286 00:31:09.290 ⇒ 00:31:11.640 Aakash Tandel: Yeah, you can set.
287 00:31:22.740 ⇒ 00:31:25.080 Aakash Tandel: And it’s a lot of utm parameters. Looks like
288 00:31:25.380 ⇒ 00:31:33.520 Robert Tseng: Yeah, I feel like, that’s what they’re doing on the source source comparisons like, I don’t think this is coming from Northeas. This is just their utms.
289 00:31:33.520 ⇒ 00:31:40.259 Aakash Tandel: Yeah, okay, yeah, not super crazy stuff. There.
290 00:31:40.750 ⇒ 00:31:58.557 Aakash Tandel: Yeah. So yeah, okay, so they basically just want the north beam data ported to our warehouse to marry with some of the product level information because they can’t. Currently because north, me doesn’t have that product. Okay? I mean, that’s fairly straightforward. Ask just gotta make sure that that’s straightforward from a engineering standpoint.
291 00:31:59.440 ⇒ 00:32:00.090 Robert Tseng: Yeah.
292 00:32:00.620 ⇒ 00:32:05.448 Aakash Tandel: Okay, cool. And yeah, I wish he’s looking into this already. Or I think, yeah, he’s looking into this data
293 00:32:06.290 ⇒ 00:32:06.930 Robert Tseng: Okay.
294 00:32:07.420 ⇒ 00:32:17.859 Robert Tseng: yeah, it’s really just product level ad, spend and marketing metrics, which yeah, that’s what North doesn’t allow them to do. So I think this is a request that every Ecom Company will have
295 00:32:17.860 ⇒ 00:32:18.410 Aakash Tandel: Yep.
296 00:32:18.880 ⇒ 00:32:23.040 Aakash Tandel: Cool. Okay, that makes sense. Sweet
297 00:32:23.948 ⇒ 00:32:29.530 Aakash Tandel: it washed out today. So no boots for him. He’s a sick today. So that’s fine.
298 00:32:29.640 ⇒ 00:32:31.910 Aakash Tandel: I’m is there anything else you
299 00:32:32.310 ⇒ 00:32:35.435 Aakash Tandel: you want to talk about with the
300 00:32:37.310 ⇒ 00:32:39.791 Aakash Tandel: the roadmap stuff that I’m gonna share with him at
301 00:32:40.450 ⇒ 00:32:42.509 Aakash Tandel: whatever time 1230, I think
302 00:32:42.910 ⇒ 00:32:43.995 Robert Tseng: Yeah.
303 00:32:46.720 ⇒ 00:32:49.440 Aakash Tandel: Like, I close this one out because you just you finish that kind of work
304 00:32:49.440 ⇒ 00:32:50.190 Robert Tseng: Yep.
305 00:32:50.697 ⇒ 00:32:57.249 Aakash Tandel: Subscribe and save this one’s still. I mean, we’re kind of blocked on this because we don’t have that data.
306 00:32:57.820 ⇒ 00:33:11.590 Aakash Tandel: So until we kind of make a move here. It’s not. And that’s a lot of that. Data is just gonna be that setting up the Etl pipeline. It will be fairly straightforward. But then we gotta figure out if it needs to be modeled in a specific way. So it’s kind of not
307 00:33:11.870 ⇒ 00:33:14.579 Aakash Tandel: like these estimates vary estimatey.
308 00:33:14.800 ⇒ 00:33:20.120 Aakash Tandel: Yeah, cancellation. This stuff, this stuff is almost done right
309 00:33:22.720 ⇒ 00:33:24.470 Robert Tseng: Yeah, it’s almost done
310 00:33:24.650 ⇒ 00:33:28.029 Aakash Tandel: Okay. So I’ll say, like, drop it down to like 1 h, 2 h
311 00:33:28.410 ⇒ 00:33:29.430 Robert Tseng: Yeah, jars.
312 00:33:36.380 ⇒ 00:33:38.380 Aakash Tandel: Just gonna call that yield
313 00:33:38.780 ⇒ 00:33:44.710 Robert Tseng: Yeah, I feel like Aman is just like he’s gonna look at like, okay, your contract is up for renewal in 3 weeks.
314 00:33:45.570 ⇒ 00:33:52.850 Robert Tseng: where I don’t know. I guess he buckets 20 HA week for us right now. So he’s like, I want to see 60 h of work. That’s what he wants to see
315 00:33:52.850 ⇒ 00:33:56.169 Aakash Tandel: Of course. Yeah, that’s just how that’s yeah. It makes total sense.
316 00:33:56.400 ⇒ 00:34:01.853 Aakash Tandel: Yeah, they want to get more than they pay for. That’s just how it’s always gonna work with clients.
317 00:34:03.110 ⇒ 00:34:06.434 Aakash Tandel: cool. So, Klavian, attentive.
318 00:34:08.270 ⇒ 00:34:15.730 Aakash Tandel: this, this connections already built. But we have to model the data and build reporting. So that’s not something small
319 00:34:15.960 ⇒ 00:34:17.400 Robert Tseng: Yeah, it’s not trivial. Yeah.
320 00:34:17.693 ⇒ 00:34:21.379 Aakash Tandel: So that’s I’m gonna say, I’m gonna stick with 10 h there, this
321 00:34:22.699 ⇒ 00:34:31.960 Aakash Tandel: we don’t have it possible. This is again a very estimated estimate, because it’s kind of blocked at this point. Until we find a an Etl partner that can do that.
322 00:34:32.600 ⇒ 00:34:42.709 Robert Tseng: Yeah. So that’s another thing where I’m like, Dude Klavio and attentive are also on. We don’t even need to build custom reports like, I kind of want to move away from like doing any sort of custom modeling
323 00:34:42.949 ⇒ 00:34:43.599 Aakash Tandel: Yeah.
324 00:34:44.546 ⇒ 00:34:56.789 Robert Tseng: That’s not like core to their business, like orders, transactions, and the nuances around that like, I want to own that because there’s nothing out of the box that will really really do that. Well. But all of these like
325 00:34:56.969 ⇒ 00:35:15.729 Robert Tseng: kind of other data sources like, I don’t want us to do modeling for them frankly. So like, I wonder if we should even change our approach here? It’s not just the marketing. Yeah, I mean, I guess I haven’t written out like, kind of an sop for this yet, like, this is really just like me just thinking about it over
326 00:35:15.829 ⇒ 00:35:29.349 Robert Tseng: backlog for the weekend. And like, we’re spending. Yeah, we’re queuing up time to like, do all these like other data sources that are not that core to the business, like, I feel like we should just not do them
327 00:35:29.510 ⇒ 00:35:31.659 Aakash Tandel: Yeah, I I agree, sir.
328 00:35:31.960 ⇒ 00:35:39.400 Aakash Tandel: the one thing I like to talk about when we’re working with clients is like, we want to be as close to the revenue driver. Data
329 00:35:40.015 ⇒ 00:35:40.630 Robert Tseng: Yeah.
330 00:35:40.630 ⇒ 00:35:48.810 Aakash Tandel: Because, like honestly like sometimes at willtree, we end up just doing marketing work, and that’s like the 1st thing to get cut. Once budgets get tight. Anything like that
331 00:35:49.410 ⇒ 00:35:50.010 Robert Tseng: Yeah.
332 00:35:50.010 ⇒ 00:35:55.950 Aakash Tandel: Use a shit about marketing data. If we’re not touching like their actual like product orders and stuff like that. So yeah.
333 00:35:56.950 ⇒ 00:36:22.140 Aakash Tandel: like, this type of work is fine to set up the connections for, like the the engineering. But then just take honestly whatever klaviyo and attentive gives, you don’t do some custom work on that. And then if there’s, you know, if things change down the line and they have better offerings, you can you bring that in. But yeah, I agree with you that totally, this is the type of work that our team is going to spend a lot of time on. And then it’s gonna change, too, because these products always change
334 00:36:22.490 ⇒ 00:36:29.180 Robert Tseng: Yeah, they all. They always go to a different vendor, and like we then waste all our time kind of like, oh, it’s not waste what’s not necessarily waste. But, like.
335 00:36:29.980 ⇒ 00:36:36.489 Robert Tseng: you know, we’re already having a hard time enough knowledge, tramp knowledge sharing on like a couple 4 sources like.
336 00:36:36.970 ⇒ 00:36:44.559 Robert Tseng: yeah to. Not yeah like it. It just. It’s just too much for where? For? For? How? For? Who we have right now?
337 00:36:44.560 ⇒ 00:36:45.130 Aakash Tandel: Yep.
338 00:36:45.570 ⇒ 00:36:53.409 Aakash Tandel: okay, cool. So that works Amazon script. So this is completed. But then there’s that secondary request.
339 00:36:53.540 ⇒ 00:37:06.540 Aakash Tandel: I’m just gonna be very firm with them like, Hey, look! This is a secondary request. It’s gonna take us time. It’s the we can’t just like use this existing script and make 2 lines of code changes and then have that work. So that is what it is.
340 00:37:07.518 ⇒ 00:37:12.400 Aakash Tandel: Tiktok also blocked. And you know.
341 00:37:12.660 ⇒ 00:37:20.099 Aakash Tandel: we’re gonna try to figure that out. But right now there’s no, there’s no nothing that can connect this information. So we’re still kind of
342 00:37:20.450 ⇒ 00:37:24.409 Aakash Tandel: this is interesting data, but not the most helpful at the moment
343 00:37:25.450 ⇒ 00:37:28.309 Robert Tseng: Yeah, portable
344 00:37:28.310 ⇒ 00:37:39.560 Robert Tseng: like that. One to me is interesting. There are no Etl providers out there that do tiktok shop. And it’s like, okay, if we really want our engineers to crack like a hard connectors problem like, it would be building something out like that, like.
345 00:37:39.890 ⇒ 00:37:41.320 Robert Tseng: yeah. But
346 00:37:41.825 ⇒ 00:37:45.539 Aakash Tandel: And it’s like they actually generate revenue from Tiktok shop, which is cool
347 00:37:45.540 ⇒ 00:37:46.849 Robert Tseng: Totally. Yeah.
348 00:37:46.850 ⇒ 00:37:47.740 Aakash Tandel: That makes sense.
349 00:37:48.200 ⇒ 00:38:14.109 Robert Tseng: The idea that, like they need to blend Tiktok with Amazon with with shopify like, that’s that’s a that’s a really like there’s no, there’s no tool out there right now that allows you to blend all those. And like they have standardized modeling, we’re able to put pull them all to a single like orders of products and transactions. Model like that would be a really valuable problem to solve. And we can go and take it to any company and get really close to their business
350 00:38:14.110 ⇒ 00:38:14.660 Aakash Tandel: Yeah.
351 00:38:14.910 ⇒ 00:38:24.660 Aakash Tandel: yeah, totally. Yeah. This is really interesting. Are we? I know. Aman with them talked with polytomic. They said a month away, is that
352 00:38:24.840 ⇒ 00:38:26.270 Robert Tseng: Yeah. It’s a month away
353 00:38:26.430 ⇒ 00:38:29.389 Aakash Tandel: That’s fine. We’ll stick with that. That sounds good.
354 00:38:30.353 ⇒ 00:38:32.760 Aakash Tandel: Portable. So this is like a like
355 00:38:33.030 ⇒ 00:38:36.909 Aakash Tandel: reducing the number of connectors like, do we want.
356 00:38:37.170 ⇒ 00:38:42.959 Aakash Tandel: Like, I, I feel like that’s not a thing that we need to be spending time on. It’s like.
357 00:38:43.070 ⇒ 00:38:54.450 Aakash Tandel: if you are. If you need to shop around for different Etl partners. We can help you do that. But again, it’s using time that I think we can be doing a little bit more complicated things for you, but
358 00:38:58.250 ⇒ 00:39:23.106 Robert Tseng: Yeah, no, I definitely want him to. So yeah, I want to solve this connectors problem where we’re just like always having connect connecting. He’s like debating us budget on connectors, and then having to do these ancillary sources. I I don’t know if we feel ready to make that push, but, like, I, you know, Corral is already one of our partners. Share with you like I wanna I wanna really just tell him on like, Hey, we’re gonna we should.
359 00:39:23.420 ⇒ 00:39:35.610 Robert Tseng: you know, think, think of Etl providers as like as a commodity like you don’t need to have. It’s not. You don’t have to be loyal to one or whatever like we just pick whoever has a connection set up. And we were like trying to optimize for speed here. It’s like.
360 00:39:35.640 ⇒ 00:39:47.149 Robert Tseng: Okay, well, if you want us to reduce connectors like are the portable cost. That’s fine, like we’ll just plug in like your marketing data sources, play video attentive. We’ll plug them all into this other kind of
361 00:39:48.300 ⇒ 00:40:04.720 Robert Tseng: a platform. Where you can go, and you can get reporting on it super quick. And we’re not gonna have to build custom reporting around that we only want to build custom reporting when there’s a very clear like loop back into like the core revenue and products that we’re talking about like
362 00:40:04.720 ⇒ 00:40:05.230 Aakash Tandel: Yep.
363 00:40:05.230 ⇒ 00:40:09.030 Robert Tseng: I. That’s kind of how I’m thinking about it, but
364 00:40:09.300 ⇒ 00:40:17.059 Robert Tseng: I don’t know if I feel ready to make that, you know. Push to him today, like I, I wanted a couple of days to kind of
365 00:40:17.590 ⇒ 00:40:26.959 Robert Tseng: the the verify. This is like a good approach for us. But anyway, like I’m I’m not. That’s kind of where. How I see this right now.
366 00:40:27.200 ⇒ 00:40:27.740 Aakash Tandel: Yeah.
367 00:40:28.000 ⇒ 00:40:28.820 Aakash Tandel: Okay.
368 00:40:29.477 ⇒ 00:40:46.090 Aakash Tandel: Yeah. I mean the general idea of like, Hey, look your Etl part. We’re like, we’re not loyal to any of your Etl providers like, if they have a better connection, or cheaper rates, or whatever we can do that. But, like, you know, doing that it is an engineering cost them in moving stuff. So it’s not something that we just
369 00:40:46.310 ⇒ 00:40:50.399 Aakash Tandel: plug and play. But if you do move to something like a corral that could be a thing. But, okay.
370 00:40:50.950 ⇒ 00:40:51.560 Robert Tseng: Yeah.
371 00:40:51.560 ⇒ 00:40:57.730 Aakash Tandel: That sounds good. This is kind of tied to it like I don’t. I don’t know that these are specifically
372 00:40:58.160 ⇒ 00:41:01.000 Aakash Tandel: decide how much to increase data ingestion like.
373 00:41:02.540 ⇒ 00:41:04.559 Aakash Tandel: I feel like, that’s not a
374 00:41:04.930 ⇒ 00:41:18.199 Aakash Tandel: usually a cost driven thing and more of a needs driven thing like, are we? Is there a need to increase data just like a delay in reporting from that, like, that type of question should be asked first.st But I’m not sure if we have
375 00:41:19.310 ⇒ 00:41:22.470 Aakash Tandel: better, better information here.
376 00:41:22.870 ⇒ 00:41:30.129 Aakash Tandel: Yeah. Well, I mean, at least, we have, like the okay. This is how much it costs per connector. And we know the snowflake costs are not gonna go up much like
377 00:41:30.330 ⇒ 00:41:33.299 Robert Tseng: Yeah, we’re we’re not. We’re not the ones to tell him like
378 00:41:33.440 ⇒ 00:41:39.059 Robert Tseng: how how he wants to increase the frequency of his data ingestion. Like
379 00:41:39.390 ⇒ 00:41:43.509 Robert Tseng: he. He kind of needs to tell us like, where, where do we? Where does he think like
380 00:41:43.650 ⇒ 00:41:44.620 Robert Tseng: we’re not?
381 00:41:45.178 ⇒ 00:41:56.459 Robert Tseng: Getting data frequently enough, and like what what you know, and we can talk through the cost of that. But, like I think everything we do is at least weekly, right now and then daily for some some of the core stuff
382 00:41:56.840 ⇒ 00:41:58.660 Aakash Tandel: Okay, yeah.
383 00:41:59.410 ⇒ 00:42:02.870 Aakash Tandel: That sounds good. North, being
384 00:42:02.870 ⇒ 00:42:05.141 Robert Tseng: He’s like, Hey, like we need
385 00:42:05.680 ⇒ 00:42:17.656 Robert Tseng: something in in real on an hourly cadence, or like he. He kind of tells us there. There are certain sources that need more be more frequent. Then I think then we can go back and actually,
386 00:42:19.680 ⇒ 00:42:20.500 Robert Tseng: better
387 00:42:22.915 ⇒ 00:42:26.930 Aakash Tandel: Okay, that sounds good. I’ll kind
388 00:42:27.060 ⇒ 00:42:35.891 Aakash Tandel: Diggle into this one to see if we can get more information north beam dashboard. Okay. So we just talked about this
389 00:42:37.730 ⇒ 00:42:51.199 Aakash Tandel: again, we’re a little blocked on this to determine what the data is gonna be looked like. But there might be a world where it’s, you know, we just port the data over and we marry it with some product level information. And you get what you get type of thing. But
390 00:42:52.300 ⇒ 00:42:53.739 Aakash Tandel: we can talk through that, too.
391 00:42:55.806 ⇒ 00:43:02.153 Aakash Tandel: The data analyst training. This one sounds straightforward. I was just gonna make sure that
392 00:43:02.710 ⇒ 00:43:13.483 Aakash Tandel: This is this still work he wants to do like an hour with 2 days a week, like Tuesday, Thursdays, with Annie. And he, I think, is Yvonne. I know. Vlad vlad
393 00:43:14.154 ⇒ 00:43:14.500 Robert Tseng: Yeah.
394 00:43:15.316 ⇒ 00:43:20.439 Aakash Tandel: So we can. We, we can set that up. That one’s fairly straightforward.
395 00:43:22.700 ⇒ 00:43:29.905 Aakash Tandel: The create dash. Okay? So that’s fine migration from Meta base to light. Dash
396 00:43:31.810 ⇒ 00:43:32.900 Aakash Tandel: That
397 00:43:33.331 ⇒ 00:43:55.049 Aakash Tandel: idea was kicked off with the light dash team in whatever channel that was. So if that is a thing we want to do like we we’ll need to estimate it. But yeah, I don’t know how long that’ll take. I’m assuming it’s not. It’s not a trivial thing to move all of our dashboards over from one thing to another, so we’ll have to make that determination
398 00:43:55.690 ⇒ 00:43:56.340 Robert Tseng: Yeah.
399 00:43:56.870 ⇒ 00:44:01.149 Aakash Tandel: Okay, sweet? Yeah. Anything else you want me to bring up with him
400 00:44:02.270 ⇒ 00:44:24.340 Robert Tseng: Yeah, I mean, I’ll probably be on that call. But like, I don’t know. So I mean, I’m looking at the estimates right now. So we’re about like 60. Honestly, if we were to switch if we were to be like, you know, I’m on, we’re gonna only we’re gonna for Klavio for attentive for anything that we don’t really see a clear use case where it’s really tying back to like orders and like kind of the core rep like core revenue. We’re just gonna we’re gonna
401 00:44:24.680 ⇒ 00:44:26.531 Robert Tseng: we’re gonna try. We’re gonna use
402 00:44:27.570 ⇒ 00:44:28.050 Aakash Tandel: No.
403 00:44:28.050 ⇒ 00:44:35.200 Robert Tseng: We’re gonna use corral that that will drop the estimates significantly. I think it would. It would reduce it to like 5 h each, probably.
404 00:44:37.110 ⇒ 00:44:39.140 Robert Tseng: Yeah. And then.
405 00:44:39.250 ⇒ 00:44:50.999 Robert Tseng: yeah, I think, like the big things that I think we can talk about are like Tiktok shop like, if this is really important, like we can go. And we can try to build. We could build that out. But it needs more time. We can’t. It’s like a
406 00:44:51.410 ⇒ 00:44:56.110 Robert Tseng: yeah, it’s it’s not something that’s entirely trivial for us to to do.
407 00:44:56.890 ⇒ 00:45:00.459 Robert Tseng: And then, yeah, like, I just
408 00:45:04.380 ⇒ 00:45:15.669 Robert Tseng: well, yeah, I mean, I’m I’m not trying to like, give you a directive or anything like I’m trying to like. Discuss this with you like, do you like? How do you? How do you feel about that? Taking the conversation in that direction.
409 00:45:15.860 ⇒ 00:45:20.440 Robert Tseng: like so setting setting those guard rails like even even now. And it’s like
410 00:45:20.760 ⇒ 00:45:34.613 Robert Tseng: well, whatever we did with gorgeous and with the gorgeous dashboard and the Okendo dashboard like, yeah, we did it. But I also think like that was not really that important like, I don’t think we need to do that. I like, I don’t want to be doing stuff like that anymore.
411 00:45:36.230 ⇒ 00:45:40.569 Aakash Tandel: Yeah, I I agree. I think we don’t want to be in the long term
412 00:45:41.456 ⇒ 00:46:00.139 Aakash Tandel: world of ingesting data, modeling it kind of uniquely and then putting out it in the dashboard. Because then we had to maintain those things. If we want to do light versions of that, we can do that. But we we just gotta be clear to be with the with the, you know client, that’s saying, hey, if these
413 00:46:00.150 ⇒ 00:46:11.839 Aakash Tandel: connectors change and they send us different data. This breaks your model, if the you know, you know, just very straightforward things like that. Because, yeah, we I don’t like the idea of
414 00:46:12.186 ⇒ 00:46:32.683 Aakash Tandel: modifying everything on the fly like you get what you get from klaviyo and attentive. And if you want other things, or you need the data like super different. Then, instead of modeling, we need to go to those providers and say, Hey, we’re not getting the data we want from everything. So is there a way that we can get that like. Can you add that to your, you know, Api, or whatever
415 00:46:33.740 ⇒ 00:46:38.640 Aakash Tandel: usually the better way cause? Yeah, work is building things
416 00:46:38.850 ⇒ 00:46:44.619 Aakash Tandel: midstream just means that we have to maintain that, and it will likely break
417 00:46:45.280 ⇒ 00:46:45.940 Robert Tseng: Yeah.
418 00:46:47.360 ⇒ 00:47:10.940 Robert Tseng: yeah, I mean, I don’t see enough in this backlog on us. For, like us doing more strategy investigation stuff like the Amazon cancellation analysis. Whether or not I actually give him a good answer on this right now, because unlimited like, I want more stuff like that where it’s like, hey? Like Justin is concerned about retaining customers for this particular product? Like, you guys go figure out like, what? What does that actually look like? Like, what?
419 00:47:11.260 ⇒ 00:47:31.079 Robert Tseng: What’s what’s what is retention? Yeah? Like doing like a full retention analysis across the product portfolio, figuring out what’s like a good benchmark and then, like giving some recommendations on how to retain customers better on particular products. Or, you know, just so, I think like that. That’s the kind of like work that I want.
420 00:47:31.080 ⇒ 00:47:43.379 Robert Tseng: like our analysts to be doing more of not just like, yeah. And he’s just gonna copy paste like amplitude dashboard over to understand, like the app, the data accuracy piece to it like we we can
421 00:47:43.640 ⇒ 00:47:56.510 Robert Tseng: the the importance of that. But we we, you know, that’s that’s where I I want to to push them on. Put push back to them on like I don’t. I don’t even want this to be the only stuff we work on for the next few weeks.
422 00:47:56.660 ⇒ 00:47:57.310 Aakash Tandel: Yeah.
423 00:47:57.590 ⇒ 00:48:07.650 Aakash Tandel: Yeah. And I agree, I think the data analysis title to our type here is not really accurate. I mean, even Annie. Again, Annie’s work has not really been any analysis
424 00:48:07.810 ⇒ 00:48:14.689 Aakash Tandel: things over. So yeah, to get to get it to a state where we’re actually solving problems like we need to.
425 00:48:15.410 ⇒ 00:48:16.010 Aakash Tandel: You’re actually
426 00:48:16.010 ⇒ 00:48:27.090 Aakash Tandel: right now, it’s been like engineering. And then like visualization work, which is fine. But again, yeah, you’re right, like, we’re not actually solving anything with this data analysis here
427 00:48:28.170 ⇒ 00:48:43.599 Robert Tseng: Yeah. So I wanna put the onus on him. Be like dude. Give us harder problems to solve, like all these things. If you, if you if you we wanna like, stop doing one off modeling for it, we’re gonna here’s a recommend. Here’s a partner like I can. I can be there. And I can. I can kind of push- push that on him.
428 00:48:44.900 ⇒ 00:48:53.470 Robert Tseng: Yeah, like, I kinda wanna make this conversation about this paradigm shift that to like, not like, I, yeah, I’m I’m not a
429 00:48:53.650 ⇒ 00:48:57.040 Robert Tseng: anyway. Yeah. So I think I don’t rehash that. But that’s that’s what I’m thinking
430 00:48:57.500 ⇒ 00:48:58.819 Aakash Tandel: Yeah, yeah.
431 00:48:58.820 ⇒ 00:49:15.030 Robert Tseng: Take like another session after this, because you have to go back and like think through it, or whatever we’ve teed up some ideas before, and I might even go back into this roadmap and add a few more things that’s like, Hey, these are some recommendations that I gave you on my things we could work on before. It’s just like references to our old notion, or whatever
432 00:49:15.910 ⇒ 00:49:22.619 Robert Tseng: that ended up like he never really, you know, he didn’t sign off on, because he was just like caught up in getting all of this work done.
433 00:49:23.600 ⇒ 00:49:25.989 Robert Tseng: But anyway, that’s that’s what I’m thinking
434 00:49:26.310 ⇒ 00:49:41.900 Aakash Tandel: Yeah, no, that sounds good. And I think my my frame with all this is like, Hey, we want to provide significantly more value to where we’re not being asked of them like, Hey, is this? Gonna take? Is this attentive thing? Gonna take 2 HI thought it was only gonna take 30 min like, if that’s
435 00:49:41.900 ⇒ 00:49:42.440 Robert Tseng: Yeah.
436 00:49:42.440 ⇒ 00:49:59.420 Aakash Tandel: We’re having. Then we’re clearly not like you’re you don’t see enough value in what we’re doing. So let’s work on stuff that you’re like, Oh, yeah, that’s like super helpful and super, you know. That. That means that like brain forge is definitely worth the money we’re spending on them. And you’re not kind of like.
437 00:50:00.270 ⇒ 00:50:03.809 Aakash Tandel: worried about like kind of small discrepancies between, like that type of work
438 00:50:04.460 ⇒ 00:50:05.020 Robert Tseng: Yeah.
439 00:50:06.560 ⇒ 00:50:12.759 Aakash Tandel: Cool. Okay, that sounds good. Yeah. I’m excited to meet him on the call. I think that’d be good.
440 00:50:13.085 ⇒ 00:50:14.060 Aakash Tandel: And then I can also
441 00:50:14.060 ⇒ 00:50:20.910 Robert Tseng: I’ll join that call. I know this will kind of go. This will probably not go the way that he expected so might get like some back and forth. There.
442 00:50:21.100 ⇒ 00:50:28.290 Aakash Tandel: Okay, that sounds good sweet. Alright, I know we have 10 min till even anything else on this guy
443 00:50:29.250 ⇒ 00:50:32.340 Robert Tseng: No, yeah. I think this is, this is a good
444 00:50:33.050 ⇒ 00:50:35.240 Robert Tseng: Yeah, yeah, this is good.
445 00:50:35.410 ⇒ 00:50:40.720 Aakash Tandel: Okay, cool, sweet. Yeah. I’m gonna shift gears to Eden now. Just so get that
446 00:50:40.720 ⇒ 00:50:45.430 Robert Tseng: Yeah, I gotta gotta go and look at that to see if we’re in a proper meeting for that
447 00:50:45.880 ⇒ 00:50:47.609 Aakash Tandel: Okay, cool. I’ll see.
448 00:50:48.010 ⇒ 00:50:48.609 Robert Tseng: See you later