Meeting Title: [Eden] Standup and Weekly Sprint Retro／Planning Date: 2025-04-04 Meeting participants: Annie Yu, Aakash Tandel, Demilade Agboola, Robert Tseng, Josh, Awaish Kumar
WEBVTT
1 00:03:53.080 ⇒ 00:03:54.000 Robert Tseng: Hey! Kosh
2 00:03:55.110 ⇒ 00:03:56.060 Aakash Tandel: Hey? How’s it going
3 00:03:56.640 ⇒ 00:03:57.310 Robert Tseng: Good.
4 00:03:58.290 ⇒ 00:04:01.845 Robert Tseng: I’m so glad you’re running. Stand up today
5 00:04:02.830 ⇒ 00:04:09.059 Aakash Tandel: Yeah, I have bandwidth today. I don’t know why, but I’ve just been
6 00:04:09.480 ⇒ 00:04:13.650 Aakash Tandel: more booked than normal in the mornings, for Wiltree also
7 00:04:13.650 ⇒ 00:04:16.990 Robert Tseng: We are well, appreciate it.
8 00:04:19.140 ⇒ 00:04:19.679 Aakash Tandel: All right.
9 00:04:22.019 ⇒ 00:04:23.400 Aakash Tandel: Yes.
10 00:04:25.580 ⇒ 00:04:32.260 Aakash Tandel: my chrome Browser was not allowing me to share earlier this morning. I don’t know what was up with it, so I’m glad we’re using zoom instead of
11 00:04:32.910 ⇒ 00:04:34.700 Aakash Tandel: Google meet
12 00:04:34.700 ⇒ 00:04:43.490 Robert Tseng: Yeah, no Google meet sucks. It has like these weird things where they hide tabs. And whatever you think, you’re like sharing it because it gives you the like, the
13 00:04:43.810 ⇒ 00:04:44.490 Aakash Tandel: The bar around
14 00:04:44.490 ⇒ 00:04:47.849 Robert Tseng: The bar around it, but it’s no one’s actually seeing anything. Yeah.
15 00:04:48.320 ⇒ 00:04:52.339 Aakash Tandel: Yeah, yeah, I was on a call with Rebecca.
16 00:04:53.485 ⇒ 00:04:59.620 Aakash Tandel: And oh, I can’t remember. What’s your name, Katie?
17 00:04:59.770 ⇒ 00:05:00.690 Robert Tseng: Katie. Yeah.
18 00:05:00.690 ⇒ 00:05:10.009 Aakash Tandel: Yeah, Katie, and yeah, it was like, Google meet was like, Nope, you’re not sharing your screen today. I was like, All right. Well, I guess I will talk through this instead. So
19 00:05:10.130 ⇒ 00:05:10.800 Robert Tseng: Yeah.
20 00:05:11.980 ⇒ 00:05:12.900 Aakash Tandel: Cool.
21 00:05:14.990 ⇒ 00:05:23.629 Aakash Tandel: Let’s see where you’re gonna log in me check, see? Make sure.
22 00:05:24.260 ⇒ 00:05:27.779 Aakash Tandel: Maybe they I know, Sahana said. No, she can’t make it
23 00:05:29.490 ⇒ 00:05:30.180 Robert Tseng: No worries.
24 00:05:31.400 ⇒ 00:05:40.270 Aakash Tandel: Okay, actually, it looks like this might be it, I think, Demi, and the way you’re busy. So let me just give a quick look at what
25 00:05:41.520 ⇒ 00:05:49.469 Aakash Tandel: they’re working on. Okay, so this is product mapping maintenance.
26 00:05:50.140 ⇒ 00:05:55.199 Aakash Tandel: I guess I can do this. I I don’t need to do this with you guys on the thing I. Oh, no, there’s me cool.
27 00:05:56.750 ⇒ 00:05:57.910 Aakash Tandel: hey, Damari!
28 00:05:59.580 ⇒ 00:06:02.559 Demilade Agboola: Hi! Everyone sorry had a meeting that ran a little over
29 00:06:02.560 ⇒ 00:06:03.290 Robert Tseng: No worries.
30 00:06:04.416 ⇒ 00:06:12.110 Aakash Tandel: All right, let’s start I’ll give you a second to breathe anyone. Let me start with you. Let’s go ahead
31 00:06:12.749 ⇒ 00:06:19.929 Annie Yu: I wanna say I only have that one. And I think actually, Robert already kind of wrap things up
32 00:06:19.930 ⇒ 00:06:20.640 Aakash Tandel: Okay.
33 00:06:21.020 ⇒ 00:06:21.820 Annie Yu: For me.
34 00:06:22.220 ⇒ 00:06:35.540 Aakash Tandel: So just so you’re aware. So Sahana and I met with Rebecca and Katie this morning, and then Robert and I also met with Danny on Wednesday afternoon. So we have a bunch of
35 00:06:36.080 ⇒ 00:06:54.365 Aakash Tandel: tickets that are going to come out of kind of those meetings and and modifications of the dashboard. Some of it will be on the analytics engineering side, but a lot of it will be on the tableau side, so I’ll split those tickets between you and Sahana so that we can get those out faster. So move so next week
36 00:06:55.084 ⇒ 00:07:09.380 Aakash Tandel: I’ll I’ll assign those to you, and then if you have questions on them, definitely ping Sahana and cause she’s the original owner and creator of a lot of those tableau dashboards, so they’ll kind of be helpful for you. There
37 00:07:10.020 ⇒ 00:07:13.559 Annie Yu: Okay. And do you have a place where I can?
38 00:07:13.730 ⇒ 00:07:27.339 Annie Yu: I guess right now my questions. I know there’s a lot of tables. And I did get with some of the ones that James used for the retention dashboards. But from here I don’t really know which ones to focus on 1st
39 00:07:27.610 ⇒ 00:07:37.489 Aakash Tandel: Yeah, I’ll I’ll outline that specifically in the ticket. So I think the 1st ones that we’re gonna work on are the at least for
40 00:07:38.400 ⇒ 00:07:54.929 Aakash Tandel: for Rebecca and Josh. It’s gonna be a couple of modifications to this one. So this might be, it’s the agent performance dashboard that’s mostly reliant on Zendesk data. This will probably be the 1st one that I send your way. So if you wanted to review this one. You’re welcome to
41 00:07:55.320 ⇒ 00:07:57.300 Annie Yu: Okay, cool. That’s helpful.
42 00:07:57.690 ⇒ 00:07:58.280 Aakash Tandel: Possible.
43 00:07:58.440 ⇒ 00:08:13.050 Aakash Tandel: Okay, cool. So that’s what’s coming up for you? Oh, yes, I’ll I’ll just check on Sahana’s. Sahana probably has. Okay. Are we still waiting on feedback from Mattesh on this Robert
44 00:08:14.545 ⇒ 00:08:16.524 Robert Tseng: No, I mean, I think,
45 00:08:17.320 ⇒ 00:08:22.630 Robert Tseng: I thought we were just got we just got stuck on the bi-channel thing. So I just told. I just had Sahana like.
46 00:08:22.800 ⇒ 00:08:29.239 Robert Tseng: just check them herself against these things. But yeah, we’re not waiting on Mattesh.
47 00:08:29.430 ⇒ 00:08:35.408 Aakash Tandel: Okay, cool. So I’m gonna pull this. So okay, so she’s still working on that. That’s fine.
48 00:08:36.200 ⇒ 00:08:37.879 Aakash Tandel: this is in client.
49 00:08:41.070 ⇒ 00:08:54.850 Aakash Tandel: Okay, this is. I guess we did did this this morning. So I’ll I’ll figure out what that looks like. And then, yeah, again, like I said, I’ll have a lot of tickets for Sahana and Annie on those things. And a lot of some of the
50 00:08:54.850 ⇒ 00:08:57.740 Robert Tseng: Pull the Smorelin thing into escalated
51 00:08:58.093 ⇒ 00:09:02.519 Robert Tseng: I mean, I’ve already kind of more or less finished my investigation. I just need to write the message.
52 00:09:02.730 ⇒ 00:09:06.429 Robert Tseng: It’s just that the whatever it’s like. 2,
53 00:09:07.480 ⇒ 00:09:16.869 Robert Tseng: the there’s questions around. Looker versus tableau. Again. They’re pointing at 2 different models. So we just need to send another message saying, Why, we’re using our model versus the old model
54 00:09:17.240 ⇒ 00:09:17.960 Aakash Tandel: Okay.
55 00:09:17.960 ⇒ 00:09:18.510 Robert Tseng: Yeah.
56 00:09:18.700 ⇒ 00:09:27.879 Josh : This is a huge disparity, I mean, that’s the only thing like in the new model. It’s showing 800 bucks, and the other one shows 1,400. Which is why it’s freaking people out.
57 00:09:28.090 ⇒ 00:09:31.709 Josh : Yeah, I get it. I get it. I get it
58 00:09:35.322 ⇒ 00:09:39.349 Aakash Tandel: That sounds good. I think. If we have.
59 00:09:39.570 ⇒ 00:09:52.240 Aakash Tandel: I don’t know how like many steps there are to the model. But maybe if there’s like a hey, this is what it does. First, st this is what it is, second type of thing that might be helpful to share with the team just so that they the business logic, makes sense
60 00:09:52.240 ⇒ 00:09:53.080 Robert Tseng: Yeah.
61 00:09:53.080 ⇒ 00:09:53.410 Aakash Tandel: Is, that
62 00:09:53.410 ⇒ 00:10:12.740 Robert Tseng: I mean, we did that for cause, it’s the same model. But obviously, when we switched over for a different product, we kind of did that step by step, breakdown. The discrepancy wasn’t by like a hundred percent. So I understand, this product is a has more shock factor because it’s off by a hundred percent. So I mean, we basically just have to.
63 00:10:13.010 ⇒ 00:10:29.430 Robert Tseng: That’s why I think it’s worth a double a double look into it as well. I mean, the team seems to have accepted it for the Sema product, but for some more. And it I understand that it is pretty big. So we’re we’re just having to write another explanation about this
64 00:10:30.280 ⇒ 00:10:39.139 Aakash Tandel: Okay, gotcha. And then, yeah, that sounds good. And then maybe for the looker dashboard. Who was the original creator of that one
65 00:10:39.140 ⇒ 00:10:40.250 Robert Tseng: He’s not here anymore
66 00:10:40.500 ⇒ 00:10:45.179 Aakash Tandel: Oh, okay, alright so that’ll be hard to ask him. Okay,
67 00:10:45.830 ⇒ 00:10:50.809 Robert Tseng: I’ve looked into it. I I know what model it’s using. I mean, that was, yeah, yeah.
68 00:10:51.330 ⇒ 00:10:55.910 Robert Tseng: yeah, okay, we should know. He built. He built the new model.
69 00:10:56.443 ⇒ 00:11:00.399 Robert Tseng: I walked him through the old model before, so he he should be able to.
70 00:11:00.590 ⇒ 00:11:03.570 Robert Tseng: Yeah, we’re going to send a message out soon.
71 00:11:03.570 ⇒ 00:11:07.300 Aakash Tandel: Okay, I’m gonna change this to you. Then.
72 00:11:08.223 ⇒ 00:11:12.206 Aakash Tandel: Just because you’re working on this.
73 00:11:28.680 ⇒ 00:11:29.930 Aakash Tandel: okay, cool.
74 00:11:30.080 ⇒ 00:11:35.569 Aakash Tandel: Alright let me go to demolade. Let’s talk through your stuff.
75 00:11:41.390 ⇒ 00:11:48.360 Demilade Agboola: Gotcha. So let’s start with the product data maintenance. So for that, that is
76 00:11:48.590 ⇒ 00:12:00.629 Demilade Agboola: basically done. I did have a question, and I asked it, but no one responded. But basically the new sheet that Josh sent has 739 records
77 00:12:01.050 ⇒ 00:12:09.970 Demilade Agboola: The 1st he sent had 763 records. So there are some records that are not there. But there are 6 new records
78 00:12:09.970 ⇒ 00:12:15.930 Robert Tseng: Active products. So maybe there’s there are some that just didn’t. There were. No.
79 00:12:16.090 ⇒ 00:12:21.430 Robert Tseng: you know, there was no orders placed for those this past week, so I would expect it to be different every week.
80 00:12:21.770 ⇒ 00:12:27.729 Demilade Agboola: Yeah, so that’s fine. So what I just did was I appended? And so now we have the unique
81 00:12:27.840 ⇒ 00:12:34.409 Demilade Agboola: pros of all active products last week. And this week. So instead of like, I’m not.
82 00:12:34.660 ⇒ 00:12:45.110 Demilade Agboola: I’m appending. So basically, I have a script that scans through the sheets finds the new variant ids the new ones that don’t appear in the in the current version
83 00:12:45.920 ⇒ 00:12:49.229 Robert Tseng: Yeah, that’s good. I don’t think we need to overwrite. So that makes sense to me.
84 00:12:49.480 ⇒ 00:13:08.710 Demilade Agboola: Alright. So that’s just why I wanted to confirm so yeah, in that case, this is done. We have it in Dbt, the the model runs. I will see, though, that we might create another ticket like just technical debt, and just ensuring that we clean up some things. But in terms of the actual availability of data. It’s right there
85 00:13:08.710 ⇒ 00:13:12.620 Robert Tseng: Can you slack me which model it is so I can just go in and look at it myself, too.
86 00:13:12.870 ⇒ 00:13:13.265 Demilade Agboola: Gotcha
87 00:13:13.660 ⇒ 00:13:15.780 Robert Tseng: We renamed it or something. Yeah, thanks.
88 00:13:17.980 ⇒ 00:13:20.240 Demilade Agboola: Got now, and
89 00:13:24.960 ⇒ 00:13:44.650 Robert Tseng: Just a couple of checks that I’m gonna do. I’m gonna look for. I’m gonna look at duplicates. And then I’m gonna just do like some basic quick filters on, on, like on. I’m just gonna look at the the distribution for the for the cogs. So if I were to just run like a cogs by product calculation like, just make sure that nothing looks like too crazy
90 00:13:45.130 ⇒ 00:13:52.090 Demilade Agboola: Yeah. So the are you asking for the final table in terms of like being product? Or are you asking for like the raw table
91 00:13:53.660 ⇒ 00:14:00.290 Robert Tseng: I think dim products is I? Yeah, I’m asking about the raw. I think dim products should. Probably I know what that looks like.
92 00:14:00.640 ⇒ 00:14:02.239 Demilade Agboola: Okay, all good.
93 00:14:02.970 ⇒ 00:14:05.260 Demilade Agboola: Yeah. I just slapped you up
94 00:14:05.470 ⇒ 00:14:06.660 Robert Tseng: Okay. Thanks.
95 00:14:06.980 ⇒ 00:14:07.610 Aakash Tandel: Awesome.
96 00:14:07.730 ⇒ 00:14:20.499 Aakash Tandel: Yeah. And Demo, if you want to write up that ticket for tech debt, feel free to either. If it’s like ready, it’s, you know, fully defined. You can put it for ready for development. And I can take a look at that and pull it into the cycle, depending on
97 00:14:20.610 ⇒ 00:14:22.099 Aakash Tandel: what workload looks like.
98 00:14:22.640 ⇒ 00:14:26.340 Demilade Agboola: Gotcha so that’s
99 00:14:26.500 ⇒ 00:14:35.829 Demilade Agboola: that’s been handled. And then in terms of ship or data. So I have been able to get the payload. So I’ve sent the post request got in the payload the
100 00:14:36.570 ⇒ 00:14:40.519 Demilade Agboola: payload because I use postman to do it. The payload is in a Csv file.
101 00:14:40.800 ⇒ 00:15:03.940 Demilade Agboola: So now I need to use the because in segment they automatically is a function that strips the payload to different columns and then appends it to the table that exists. So I’m trying to upload. That’s like what I’m trying to do today. Upload that like Csv file, strip it out of the payload into different columns and then append it to our currently existing table. So that’s like, basically the final step
102 00:15:04.060 ⇒ 00:15:07.670 Robert Tseng: And that’ll backfill for us in the in our, in our model
103 00:15:08.100 ⇒ 00:15:14.270 Demilade Agboola: Yeah, like again. So I already have the payload. So I have the records of every like missing record.
104 00:15:14.450 ⇒ 00:15:18.029 Demilade Agboola: It’s just to append it to the table, or whatever they want.
105 00:15:18.940 ⇒ 00:15:19.640 Robert Tseng: Cool.
106 00:15:20.980 ⇒ 00:15:21.560 Aakash Tandel: Yes.
107 00:15:23.760 ⇒ 00:15:24.649 Robert Tseng: Got it.
108 00:15:24.650 ⇒ 00:15:25.330 Robert Tseng: Be good
109 00:15:26.770 ⇒ 00:15:29.659 Aakash Tandel: Sorry. Yeah, Demo, you’ve got
110 00:15:30.750 ⇒ 00:15:37.109 Demilade Agboola: Yeah, so that’s basically the obvious on that so I just want to append the payload to that.
111 00:15:43.020 ⇒ 00:15:44.610 Demilade Agboola: what other
112 00:15:45.600 ⇒ 00:15:51.480 Aakash Tandel: Order status, I guess. Ship oh, and bad! This isn’t. This is kind of waiting on this ticket.
113 00:15:51.930 ⇒ 00:15:59.599 Robert Tseng: No, I mean, that’s yeah. I think they can happen in parallel. We don’t have to update everything. This was kind of looking at the new web hook.
114 00:16:00.740 ⇒ 00:16:01.290 Robert Tseng: Yeah.
115 00:16:01.796 ⇒ 00:16:07.629 Demilade Agboola: Have a call on that, Robert. Yeah. So I’ll schedule a call
116 00:16:08.400 ⇒ 00:16:11.859 Demilade Agboola: my calendar right now, so I don’t like that doesn’t sleep by.
117 00:16:13.610 ⇒ 00:16:14.970 Demilade Agboola: Let’s let’s meet on that today.
118 00:16:15.120 ⇒ 00:16:17.599 Aakash Tandel: Is this, are you guys gonna also talk about this guy
119 00:16:18.870 ⇒ 00:16:20.169 Robert Tseng: Yeah, we should
120 00:16:21.000 ⇒ 00:16:22.899 Aakash Tandel: Because I think, then what about this guy
121 00:16:23.550 ⇒ 00:16:25.119 Robert Tseng: Yeah, those are the same thing.
122 00:16:25.250 ⇒ 00:16:29.529 Aakash Tandel: Okay, cool. Yeah. Okay. Sounds like you guys are gonna be on that. And then this one
123 00:16:30.380 ⇒ 00:16:37.309 Demilade Agboola: So just say, like, Robert, can you send an invite because your kind of looks pretty full? I’m not sure what you can move and what you can’t move
124 00:16:37.310 ⇒ 00:16:39.830 Robert Tseng: Oh, right? Okay, yeah. I’ll I’ll add you
125 00:16:42.910 ⇒ 00:16:46.297 Aakash Tandel: Awesome. And then this is the only one that’s ready for development.
126 00:16:47.280 ⇒ 00:16:50.720 Aakash Tandel: Robert, you create this 2 days ago
127 00:16:52.241 ⇒ 00:16:56.460 Robert Tseng: Yeah, this is the same thing that I’m gonna be talking to about.
128 00:16:56.700 ⇒ 00:17:00.830 Aakash Tandel: Okay, sweet? Well, that sounds good. Cool.
129 00:17:02.124 ⇒ 00:17:26.870 Aakash Tandel: okay. And then let’s look at Robert. Your board demi la Day. There will be probably some tickets that come out of the meetings that Sana I had this morning with Rebecca based off of like some modeling needs. But that’ll be next week, because I have to like, organize all those like things and figure out what’s what needs a model change? And what needs a just a visualization. So I’ll that’ll be coming down the pipe for you.
130 00:17:27.310 ⇒ 00:17:34.539 Demilade Agboola: Yeah, that’s fine. Just in all things. Can we just remember, like to like how we want to prioritize? So I know which ones to knock off 1st before
131 00:17:35.190 ⇒ 00:17:39.529 Aakash Tandel: Sure. Yeah. I think. Let me sign
132 00:17:41.304 ⇒ 00:17:51.359 Aakash Tandel: so I think I’ll leave it to. So I think this is Robert. I think this is probably the 1st thing to get out the door today, if this is possible. Yeah.
133 00:17:51.360 ⇒ 00:17:53.940 Robert Tseng: Let’s just meet right after this call to finish it. Yeah.
134 00:17:54.270 ⇒ 00:17:55.300 Demilade Agboola: Okay.
135 00:17:55.300 ⇒ 00:18:10.159 Aakash Tandel: And then I’ll leave it to you guys to put the order of priority on these. Maybe just use the due dates. Just, you know, the 1st one is like, you know, due on like what Monday or Tuesday, then the second one Wednesday type of thing. But so just I’ll leave it to you guys to do that
136 00:18:10.360 ⇒ 00:18:10.930 Robert Tseng: Yeah.
137 00:18:11.750 ⇒ 00:18:12.280 Aakash Tandel: Cool.
138 00:18:12.660 ⇒ 00:18:20.280 Aakash Tandel: Okay, Robert, let’s look at your stuff
139 00:18:21.421 ⇒ 00:18:24.320 Aakash Tandel: anything you need to talk about. It sounds like you’re sending that
140 00:18:24.320 ⇒ 00:18:42.740 Robert Tseng: Yeah, that’s the main thing. And then, yeah, I mean already, clear something. Some things here, this, that block is still blocked. And then the customer journey. Dash, that’s we update. We’re updating that post our call with Daddy. So yeah, that’s just like we have to take. We, he, the the new
141 00:18:43.520 ⇒ 00:18:50.103 Robert Tseng: where we sent him the figmas area, so I mean that he’s he already approved it. So we would just have to rebuild
142 00:18:50.510 ⇒ 00:18:53.019 Robert Tseng: with with the with the thing that we sent him
143 00:18:53.250 ⇒ 00:18:55.830 Aakash Tandel: Yup, yeah, that sounds good.
144 00:18:57.120 ⇒ 00:19:01.860 Robert Tseng: So I I would. I would honestly just take that out of cycle like there’s not. We should just close that ticket.
145 00:19:02.210 ⇒ 00:19:05.560 Robert Tseng: It’s changed it. It’s just it’s changed at this point
146 00:19:06.370 ⇒ 00:19:08.240 Aakash Tandel: So like done, or canceled.
147 00:19:08.240 ⇒ 00:19:09.490 Robert Tseng: Yeah, let’s just cancel it.
148 00:19:13.200 ⇒ 00:19:15.615 Aakash Tandel: Okay, yeah, that sounds good. I will.
149 00:19:16.380 ⇒ 00:19:26.763 Aakash Tandel: I’ll compile all the stuff with, because Rebecca’s like feedback, also kind of overlap with some of the stuff from Danny, so I’ll try to organize that in one thing so that we can.
150 00:19:27.180 ⇒ 00:19:34.030 Aakash Tandel: you know, juggle the items around and make sure that the day is all in the right place. Yeah, okay.
151 00:19:34.030 ⇒ 00:19:44.260 Robert Tseng: I wasn’t on that call, but just looking through Rebecca’s feedback in those threads, I mean, she’s clearly like, you know, expanded the scope of that dash. I feel like she was kind of
152 00:19:44.940 ⇒ 00:19:55.160 Robert Tseng: comp consolidating like feedback on 2 separate things, and like 2 tabs, 2 separate reporting streams into one, which is fine, like I if we just kind of
153 00:19:55.380 ⇒ 00:20:06.179 Robert Tseng: change it from 2 dashboards to just one like I’m fine with that. But I do. Wanna I just wanna make sure that we we I wasn’t there on the call set. I don’t know how you handled that
154 00:20:06.450 ⇒ 00:20:30.859 Aakash Tandel: Yeah, I, I basically said, like, we’ll do it iteratively. So we’ll start off with the kind of like the Zendesk stuff. And then the data that’s gonna be merged with bask information. We’ll we’ll either build that separately and then pull it in or build it on an existing dash. But yeah, I think we’ll this will be a piecemeal thing. We’re not going to be able to deploy all of this at one time, because there’s a fair amount of differences in what we’re looking at.
155 00:20:30.860 ⇒ 00:20:31.530 Robert Tseng: Okay.
156 00:20:32.577 ⇒ 00:20:35.512 Aakash Tandel: And Sahana had a
157 00:20:37.259 ⇒ 00:21:01.739 Aakash Tandel: like a or by order Zendesk information, table that she was working on, or dash that she was working on. And Rebecca’s not interested in that. So we’re gonna that’s obviously not the top priority, because there’s there’s other things ahead of that, but that’s something that I’ll probably run by you to see how we can modify that existing one that Sahana would have like, maybe partially done to get that to the finish line
158 00:21:02.090 ⇒ 00:21:02.810 Robert Tseng: Okay.
159 00:21:04.570 ⇒ 00:21:05.170 Aakash Tandel: Cool.
160 00:21:05.570 ⇒ 00:21:07.979 Aakash Tandel: Okay, I will have tickets. Right?
161 00:21:08.850 ⇒ 00:21:11.250 Josh : So my biggest goal today.
162 00:21:12.690 ⇒ 00:21:22.729 Josh : Robert, I know, you know, we have, like our Kpi and Okr review today, does everyone have a baseline of data that they’re gonna be able to present today
163 00:21:25.010 ⇒ 00:21:28.899 Robert Tseng: Yeah, I think so. There’s there’s a there’s a dash for everyone at this point. So
164 00:21:29.470 ⇒ 00:21:32.109 Josh : Perfect. That’s a big win. Thank you. Guys.
165 00:21:33.720 ⇒ 00:21:36.740 Aakash Tandel: Yeah, cool.
166 00:21:37.917 ⇒ 00:21:54.950 Aakash Tandel: Okay, if that’s it, that sounds good. Yeah, we’ll continue to build out the dashboards for well, for everyone. But definitely Rebecca and Danny had some good good stuff they want to see, so we’ll try to get that up and running for them as soon as possible.
167 00:21:56.820 ⇒ 00:22:07.000 Aakash Tandel: Alright! Well, feel free to slack meet up, if you need to let me know if you guys have any questions, but have a good weekend if I don’t hear from you. And yeah.
168 00:22:07.000 ⇒ 00:22:27.789 Josh : Yeah, I I only have one other one other, like a couple other couple of other quick things. So this incremental thing I have a couple of questions about, and then also, is there anything that we can do to again like for maybe, like I don’t know if there’s like some looms, or like some like something we can record to give to the team to help them understand, like the
169 00:22:28.100 ⇒ 00:22:32.719 Josh : the differentiation between like what you guys have done with our reporting
170 00:22:33.010 ⇒ 00:22:42.369 Josh : in tableau versus like what they’re used to seeing in Looker, because, like these people are trying to. And I and I get it right? Like I’m I’m also like, I’m
171 00:22:42.750 ⇒ 00:22:47.789 Josh : I’m being very. I run people pretty rough, like pretty tough in our org
172 00:22:48.335 ⇒ 00:22:50.960 Josh : to hold them really accountable on things.
173 00:22:51.140 ⇒ 00:23:11.479 Josh : and it’s really tough for them to go from, hey? I had like a $1,400 Ltv. On this other report to 800 now, and I’m holding them accountable to Ltv and Cac ratios. And so like, they’re really struggling to like, just like, justify it. And like, I believe, what you guys have put together.
174 00:23:11.480 ⇒ 00:23:27.399 Josh : But I just wanna like, have like a loom or something, so that people like feel really good. I mean, this week was good. This is probably the best week I think we’ve had, you know, like, in terms of, like everyone trusting the data, the quality of stuff like, I think we are kind of crossing that chasm right now.
175 00:23:27.650 ⇒ 00:23:39.730 Josh : but I do still think that, like the team doesn’t really get it yet, because they haven’t been in all these meetings, you know, they haven’t had all the discussions, so a loom or something would be really helpful. And then I just really wanted to understand like this incremental thing.
176 00:23:40.110 ⇒ 00:23:42.030 Josh : or like what we’re doing against them.
177 00:23:42.650 ⇒ 00:23:43.320 Robert Tseng: Okay?
178 00:23:47.510 ⇒ 00:23:49.758 Robert Tseng: Yeah, no. I I hear that, I think,
179 00:23:50.840 ⇒ 00:24:13.639 Robert Tseng: I mean before the when we were doing releases, they would just drop links. And I mean, I pretty much have stopped doing that because I feel like it was just so distracting. But okay, yeah, no, we we should. We should recruit record looms. I mean, I try to anticipate when the differences are pretty off, and then, like, I’m calling out like, Hey, like this metric is dropped, or whatever. But okay, I think we need to just
180 00:24:13.640 ⇒ 00:24:19.274 Josh : Yeah, I don’t know if it’s always making blooms. I think it might just be like a do like even like a once
181 00:24:19.630 ⇒ 00:24:22.169 Josh : like a month, or something, or like once a
182 00:24:22.300 ⇒ 00:24:30.149 Robert Tseng: Yeah, like a release like an internal product release thing where we go through updates and stuff. Yeah, like, I’m I’m down to record that that makes sense to us. I mean, we, yeah.
183 00:24:30.370 ⇒ 00:24:46.380 Josh : Cause. I feel like it’ll like, reduce a lot of the sidebar questions you guys are getting and like trying to make it. So you have to explain things 10 times, that’s all I’m trying to do. I’m just trying to like cause I think that like, basically just like one loom, as of like, hey, here’s all the shit that’s gotten us to where we are today.
184 00:24:46.790 ⇒ 00:24:48.829 Josh : Like, why you should trust the data
185 00:24:48.990 ⇒ 00:24:57.059 Josh : like is like a loom. You know what I mean? And then like, for each one of these dashboards like this is what is happening, and boom like a 15 min loom.
186 00:24:57.170 ⇒ 00:25:09.230 Josh : You know nothing crazy, just like explaining at a high level. These are the technologies we’re using. This is the changes that we’ve made. This is the advancements that we’ve done. This is what we’re going to be doing, moving forward. Yeah.
187 00:25:10.620 ⇒ 00:25:40.169 Robert Tseng: Okay? Well, yeah, I mean Akash, like, just for your context, like, I don’t know, we we stopped doing. I haven’t been as kind of on top of the past couple of weeks, but before, whenever somebody was building visualization and and sharing it, I would require them to share a loom with me and walk me through it as if I’ve never seen it before, so I kinda feel like we should be doing that again, and especially for the final state we need to. We need to be doing doing that. So I I think that’s that to me is something we need to bring back
188 00:25:40.170 ⇒ 00:26:09.469 Aakash Tandel: Yeah, no, that makes sense. I think, like getting the I I feel like from their perspective. It’s probably the business logic is changing, or, you know, change from looker to tableau. There was reasons for those changes that you know we made, or the business made and those just had to be documented. So they’re aware of that. That makes all sense to me. Yeah, we can. We can figure out if we want to do. Yeah looms. Or if there’s another way, we can service the information. But yeah, loom sounds
189 00:26:09.470 ⇒ 00:26:19.310 Josh : Yeah, loom loom is, I mean, don’t. I’m not saying you have to do Loom you do. If it’s like a paragraph, write up, or whatever is, whatever is the easiest stuff. I just wanna make sure that like.
190 00:26:19.460 ⇒ 00:26:41.859 Josh : Hey, you guys put this in the frigging right in the general chat, or whatever. It doesn’t even have to be like sequestered in analytics like, just just like, make it like, it’s something you guys are proud of like, I’m proud proud of the work that you guys have done. I think they were making progress. And like, I just want people to feel comfortable and confident, and also know that you’re probably gonna get most of the questions drawn out from people by doing that
191 00:26:42.198 ⇒ 00:26:46.570 Josh : cause. The other side is a lot of the times. People won’t ask questions because they’re not even knowing what’s going on.
192 00:26:46.920 ⇒ 00:26:47.530 Robert Tseng: Yeah.
193 00:26:47.870 ⇒ 00:26:48.560 Aakash Tandel: That’s fair.
194 00:26:49.270 ⇒ 00:26:53.960 Aakash Tandel: Yeah, that sounds good. Robert, do you want to talk about the incremental stuff?
195 00:26:56.040 ⇒ 00:27:14.559 Robert Tseng: Yeah, well, I mean, basically, they’re moving off of north beam and going to incremental instead. Which yeah, I think in parallel, what we’re doing is we’re we’re gonna be, do. We’re we’re we’re doing the direct connectors to these ad platforms and then show showing like.
196 00:27:14.950 ⇒ 00:27:32.370 Robert Tseng: basically replicating what North beam is doing on the attribution reporting side right now. And I think within next week we’re gonna make, we’re gonna we’re gonna demo that. And that that could end up being the the catalyst to move, move off of North Beam
197 00:27:33.270 ⇒ 00:27:34.330 Josh : Hooray.
198 00:27:36.150 ⇒ 00:27:43.429 Josh : yeah, that’d be great to get off there. But yeah, I also I understand why they they push back a lot on incremental because they’re like, Hey, look.
199 00:27:43.740 ⇒ 00:27:59.977 Josh : we want really wanna start getting data right now, even though it’s not perfect. Right now, we know it’s gonna take a while just to get the incrementality data stood up. And it’s gonna take a couple of weeks for us to get our own. They’re okay. They’d rather have it internally, too, but like they basically already signed a deal, I didn’t know about it. So
200 00:28:00.410 ⇒ 00:28:02.000 Josh : yeah, it’s
201 00:28:02.170 ⇒ 00:28:09.559 Robert Tseng: Bias, you know. That’s kind of the classic. The marketing teams move faster than we we do. It’s just kind of how it is
202 00:28:09.860 ⇒ 00:28:10.520 Josh : Yeah.
203 00:28:10.520 ⇒ 00:28:11.080 Robert Tseng: Yeah.
204 00:28:13.040 ⇒ 00:28:23.269 Aakash Tandel: We can. We can also figure out all those direct connections. And we can scope those for how we want to ingest that data into the instance and stuff, but that makes sense to me.
205 00:28:23.990 ⇒ 00:28:29.880 Robert Tseng: Yeah, I mean, Akash for this demo. I was just gonna plug it into corral corral already. Has all those connect connections. Yeah.
206 00:28:30.120 ⇒ 00:28:33.140 Aakash Tandel: Okay, that simplifies things. A lot. Okay, cool.
207 00:28:34.180 ⇒ 00:28:34.900 Aakash Tandel: I like that idea
208 00:28:34.900 ⇒ 00:28:38.360 Josh : Cool, and then what else is on the roadmap for next week?
209 00:28:40.120 ⇒ 00:28:42.059 Aakash Tandel: Yes, let me pull up
210 00:28:45.370 ⇒ 00:28:55.139 Aakash Tandel: stuff. So one of the things that I’m still on my to do is to make sure that the roadmap matches with your massive spreadsheet. And that’s the main
211 00:28:55.140 ⇒ 00:29:14.910 Josh : And yeah, don’t. Don’t think you have to do everything exactly like that was just I was giving you guys a detail like, that’s how I I built the whole organization before, and that’s like how we structured it like you don’t have to do it the exact same way, like it’s just like kind of like a useful tool. I will use a lot of that same, you know, the same kind of reporting, because it’s like, literally the same business model
212 00:29:15.210 ⇒ 00:29:15.860 Aakash Tandel: Yeah.
213 00:29:15.980 ⇒ 00:29:34.050 Aakash Tandel: that makes sense. Yeah, I think for us next week there’s gonna be some of that order sla performance reporting from the engineering standpoint. So getting some of that business logic into data models, basically and then from
214 00:29:35.050 ⇒ 00:29:54.520 Aakash Tandel: it’s not quite. I guess I don’t know what it would be under a lot of the reporting changes would be on the existing dashboards that we have for Rebecca and Danny. So those are going to be more on the analyst side of things. So those are the 2 things I can think of that are coming up.
215 00:29:55.023 ⇒ 00:30:22.510 Aakash Tandel: Does this cycle. This goes to April 6, th so I’ll also be starting a new cycle, so I’ll pull the top, and I’ll run these by you, too, Josh. I’ll pull the top things on this spreadsheet that makes sense with kind of what we have currently in backlog. And then, if we need a scope or like, get more definition on stuff. You, me and Robert can sync up to get clarity on those items
216 00:30:23.370 ⇒ 00:30:25.565 Josh : Perfect, perfect
217 00:30:27.370 ⇒ 00:30:35.379 Josh : cool, and then the only other one that I had a very specific question on was on that retention. Dashboard
218 00:30:35.812 ⇒ 00:30:38.839 Josh : with, like the bar chart and all that other stuff
219 00:30:41.180 ⇒ 00:30:45.330 Robert Tseng: We haven’t added the bar chart to it yet. Yeah.
220 00:30:45.990 ⇒ 00:30:49.720 Josh : Is there like just like a timing? Or what? What’s the what’s the thought
221 00:30:50.104 ⇒ 00:31:03.529 Robert Tseng: Well, yeah, I mean, I’ve I was gonna meet with Dave a lot after this. But there is a model change that needs to happen before we can do the stack bars. And I I mean, given the timing of before the leadership report. I don’t know if we’ll push that change today, but
222 00:31:03.680 ⇒ 00:31:04.530 Josh : No, that’s the only one
223 00:31:04.530 ⇒ 00:31:08.889 Robert Tseng: More urgent thing on the retention dash since last you saw it.
224 00:31:09.330 ⇒ 00:31:19.669 Robert Tseng: I think we just there was an error on kind of how I was reporting. I fixed that yesterday, and so that was the that was the patch that I was working on and didn’t didn’t prioritize the stack bar
225 00:31:20.090 ⇒ 00:31:24.387 Josh : No, that’s okay. I’d rather have the day to be right. Then. Then it looked pretty.
226 00:31:25.930 ⇒ 00:31:28.510 Josh : Okay, cool. That’s it for me. Then, guys.
227 00:31:29.720 ⇒ 00:31:30.240 Robert Tseng: Okay.
228 00:31:31.070 ⇒ 00:31:32.849 Josh : You guys need anything from me?
229 00:31:34.080 ⇒ 00:31:43.319 Robert Tseng: I think we’re just gonna make sure. I just wanna make sure anybody that’s touching reports right now is wraps up their work and don’t touch it before the review
230 00:31:43.320 ⇒ 00:31:43.990 Josh : Yeah, please.
231 00:31:44.110 ⇒ 00:31:47.160 Robert Tseng: This is they have, this is it like? For now
232 00:31:48.010 ⇒ 00:31:51.350 Josh : Yes, I agree. I yeah, please, please. No more breaks
233 00:31:51.350 ⇒ 00:31:51.900 Robert Tseng: Yeah.
234 00:31:52.120 ⇒ 00:32:01.500 Josh : And just make sure that like it works, you know, and that’s all I asked for. Alright, cool, good stuff, guys appreciate it. Have a good weekend
235 00:32:01.880 ⇒ 00:32:04.810 Robert Tseng: Yeah, David, law, do you want to stay on
236 00:32:05.100 ⇒ 00:32:05.839 Demilade Agboola: Yeah, sure.
237 00:32:06.230 ⇒ 00:32:06.790 Robert Tseng: Okay.
238 00:32:07.040 ⇒ 00:32:10.020 Aakash Tandel: I will assign it to Robert
239 00:32:10.020 ⇒ 00:32:24.609 Robert Tseng: Thanks, and I guess, before wish hops off. I know you weren’t here for most of the call. But is there anything that you need kind of that. You’re that you’re blocked on right now, I know, like the user summary versus product. Ltv, thing is one thing we’re waiting on
240 00:32:26.400 ⇒ 00:32:34.320 Awaish Kumar: Yeah, this is like something I have to go in and investigate, because for
241 00:32:34.840 ⇒ 00:32:40.190 Awaish Kumar: for user summary, I can see it is just doing for each
242 00:32:40.390 ⇒ 00:32:47.918 Awaish Kumar: customer. It calculates all the orders and whatever the 1st product was, and there’s no like
243 00:32:49.030 ⇒ 00:32:53.340 Awaish Kumar: there should not be any or discrepancy.
244 00:32:57.910 ⇒ 00:33:01.890 Awaish Kumar: yeah. So I I will have to look like, why is there? Because
245 00:33:03.690 ⇒ 00:33:16.670 Awaish Kumar: it it is coming from order details, table users. Somebody is taking data from order details, and I know that the revenue in order details in in our tables is is bit off
246 00:33:17.150 ⇒ 00:33:18.090 Awaish Kumar: right?
247 00:33:18.740 ⇒ 00:33:21.050 Awaish Kumar: Well, I’m not sure why
248 00:33:23.560 ⇒ 00:33:25.910 Robert Tseng: Our revenue is off like
249 00:33:25.910 ⇒ 00:33:28.629 Awaish Kumar: No like the the. There is difference between
250 00:33:29.280 ⇒ 00:33:37.549 Awaish Kumar: the data from order details and the the models we built right and the the models we built are are have much more accurate revenue.
251 00:33:39.700 ⇒ 00:33:48.359 Awaish Kumar: so I right now, I on the on the top of my mind. I don’t know what exactly is causing this issue. But I have to investigate that part
252 00:33:50.610 ⇒ 00:33:52.639 Awaish Kumar: for the Ltv calculation. Yeah.
253 00:33:54.430 ⇒ 00:34:07.139 Robert Tseng: Yeah, I know we’ve. This is just connecting a lot. We’ve answered a similar question about these model comparisons before, because ultimately everything on the old legacy model stems from order details.
254 00:34:07.330 ⇒ 00:34:18.040 Robert Tseng: So the question is always, what’s the difference between order details and what we built? And it’s like we’re always answering like the same kind of small differences in the question. But
255 00:34:18.446 ⇒ 00:34:39.689 Robert Tseng: yeah, I mean this revenue piece, like we’ve talked about before. We don’t recognize the same order, statuses and everything. So I I think it’s just pulling information that we’ve kind of shared before and kind of applying it to this one use case of some more. And Ltv, because that’s what the team is focused on right now, this is like a product where
256 00:34:39.900 ⇒ 00:34:48.780 Robert Tseng: maybe the difference doesn’t look as drastic for other products. But for this product it looks pretty drastic. So I think the team is just kind of freaking out about that
257 00:34:50.100 ⇒ 00:34:57.360 Awaish Kumar: Okay, I will look into it. And okay, sorry. I
258 00:35:00.540 ⇒ 00:35:18.339 Robert Tseng: Yeah, I mean, I will help, too. I’m gonna go and dig up all the messages we have. I’m gonna try to throw it all into a single place. And we’re just gonna try to build that 1 1 story that. But yeah, I think it’s like anything that we’ve said about order details versus what we do now like is all kind of related to this in some way, so
259 00:35:20.250 ⇒ 00:35:20.900 Robert Tseng: Yeah.
260 00:35:23.070 ⇒ 00:35:23.520 Aakash Tandel: Okay.
261 00:35:23.520 ⇒ 00:35:24.160 Awaish Kumar: Okay.
262 00:35:24.970 ⇒ 00:35:28.160 Aakash Tandel: Awesome anything else away she wanted to touch on. I know we didn’t get you on stage
263 00:35:28.560 ⇒ 00:35:28.960 Awaish Kumar: No!
264 00:35:29.740 ⇒ 00:35:30.310 Aakash Tandel: Okay.
265 00:35:30.870 ⇒ 00:35:33.800 Awaish Kumar: I don’t think there is anything assigned to me
266 00:35:35.180 ⇒ 00:35:36.499 Aakash Tandel: Yeah, no, I think.
267 00:35:39.760 ⇒ 00:35:44.070 Aakash Tandel: yeah, I will. Robert. Do you know anything about these
268 00:35:49.960 ⇒ 00:35:55.840 Robert Tseng: I have not touched this in a while. I don’t remember the context to be honest, so
269 00:35:56.260 ⇒ 00:36:02.389 Aakash Tandel: We’re gonna pull these out of the cycle. I’m gonna pull these back into a deeper development.
270 00:36:03.451 ⇒ 00:36:09.069 Aakash Tandel: Put these 2 next because this is not gonna happen.
271 00:36:10.530 ⇒ 00:36:11.290 Aakash Tandel: Okay.
272 00:36:11.520 ⇒ 00:36:14.599 Awaish Kumar: Yeah, I think this was about Zendesk data, right?
273 00:36:14.920 ⇒ 00:36:16.920 Awaish Kumar: And we haven’t not.
274 00:36:19.130 ⇒ 00:36:23.089 Awaish Kumar: We are not able to get like set up. Say, Zendesk and segmenter
275 00:36:23.900 ⇒ 00:36:25.330 Aakash Tandel: Okay, yeah.
276 00:36:26.002 ⇒ 00:36:28.906 Aakash Tandel: Okay, that’s fine. We’ll worry about that later.
277 00:36:29.630 ⇒ 00:36:32.889 Aakash Tandel: I think everybody you got enough stuff to to work on. So
278 00:36:33.726 ⇒ 00:36:41.749 Demilade Agboola: I just want to quickly flag something. It appears the retention dashboard. At least the one I’m looking at doesn’t seem to have any extract refreshes set up
279 00:36:44.668 ⇒ 00:36:48.951 Robert Tseng: The one I deployed. I thought I did. I like,
280 00:36:49.610 ⇒ 00:36:54.968 Robert Tseng: okay, maybe you’re not looking at the right one. This is the so there’s like a few different ones.
281 00:36:55.890 ⇒ 00:36:58.819 Demilade Agboola: Yeah, so it might be helpful. If
282 00:37:00.320 ⇒ 00:37:02.089 Demilade Agboola: so, this one on the same page.
283 00:37:05.640 ⇒ 00:37:11.790 Demilade Agboola: Yeah, is, cause there’s none in publish dash. But yeah, so there’s no extract refreshes
284 00:37:14.790 ⇒ 00:37:16.219 Aakash Tandel: Oh, yeah, which one is it?
285 00:37:17.000 ⇒ 00:37:24.410 Robert Tseng: It’s it’s just retention dashboard, I think. Yeah, I mean the way that James published this was pretty messy. So anyway, okay.
286 00:37:24.720 ⇒ 00:37:33.410 Demilade Agboola: Alright. So I’ll look into that because you can see that it says that last extract was like it didn’t happen today, so I would have to set that up just so that we have
287 00:37:33.410 ⇒ 00:37:36.420 Robert Tseng: Okay, cause in the data source. If you go to data sources
288 00:37:37.460 ⇒ 00:37:38.070 Demilade Agboola: Yeah.
289 00:37:38.230 ⇒ 00:37:51.049 Robert Tseng: Yeah, if you go to those, if you. These are all, both in published data sources, I’ve set up extracts for those. And then this dashboard is doing the live connection to the extracts, I thought. That’s the way we decided to set it up
290 00:37:53.100 ⇒ 00:37:55.720 Demilade Agboola: Yeah. But I also think there has to be
291 00:37:56.670 ⇒ 00:38:01.320 Demilade Agboola: like, if you go to other dashboards, that if you go to publish dashboards you’ll see that it’s slightly different.
292 00:38:02.110 ⇒ 00:38:03.889 Demilade Agboola: like you will see
293 00:38:04.850 ⇒ 00:38:10.329 Robert Tseng: I tried against the product. Ross, Ltv dashboard and just as like a
294 00:38:11.080 ⇒ 00:38:11.870 Demilade Agboola: Oh, okay.
295 00:38:12.090 ⇒ 00:38:18.590 Robert Tseng: But I mean I could be wrong. I just thought that the whole, the difference between what Sahana was doing, what I was doing was.
296 00:38:18.800 ⇒ 00:38:25.989 Robert Tseng: She published this data sources separately, and then set up live connections to those extracts.
297 00:38:26.430 ⇒ 00:38:32.489 Robert Tseng: So I just, instead of doing embedded in workbook and extracts
298 00:38:33.130 ⇒ 00:38:38.279 Robert Tseng: which I was doing before. That was, that was the main switch I made. But I yeah.
299 00:38:38.480 ⇒ 00:38:43.730 Robert Tseng: please please check me if that’s wrong, because I am, I am still honestly like, don’t
300 00:38:44.050 ⇒ 00:38:51.059 Robert Tseng: just it’s the process like is not super clear to me like I I still don’t understand why
301 00:38:52.170 ⇒ 00:38:52.850 Robert Tseng: mine doesn’t
302 00:38:53.570 ⇒ 00:38:56.509 Demilade Agboola: Yeah, I I think there has to be.
303 00:38:57.230 ⇒ 00:39:03.880 Demilade Agboola: And the reason why I say that if you go to the product so if you go to publish dashboard, and you go to
304 00:39:04.050 ⇒ 00:39:17.399 Demilade Agboola: so publish dashboards right there. Top 1st hyperlink, yeah, and then you go to the product for us. Written on ad spend. If you go there, you can see there’s an extract refresh setup
305 00:39:18.590 ⇒ 00:39:19.580 Demilade Agboola: as well.
306 00:39:20.750 ⇒ 00:39:25.340 Demilade Agboola: So I’m guessing like that refreshes the
307 00:39:26.560 ⇒ 00:39:33.069 Demilade Agboola: the extract that is used for the dashboard every morning. That’s my guess. I’m not just in
308 00:39:33.473 ⇒ 00:39:42.469 Demilade Agboola: so I I will look into that. I will just quickly look at the data and see if it’s gone. Still, that’s the 1st thing. If it’s gone. Still, I will set up the refresh for that
309 00:39:42.470 ⇒ 00:39:43.130 Robert Tseng: Okay.
310 00:39:43.640 ⇒ 00:39:48.669 Demilade Agboola: Yeah, also, can we just move it to the published dashboard? So it’s everything we have is in one place
311 00:39:48.900 ⇒ 00:39:51.220 Demilade Agboola: like the retention dashboard. That is
312 00:39:51.470 ⇒ 00:39:51.900 Aakash Tandel: Oh, yeah.
313 00:40:03.500 ⇒ 00:40:18.210 Robert Tseng: Yeah, I mean, like, in probably like a couple hours, there’s gonna be a quarterly leadership sync. Every every team’s gonna pull up their tableau. Dash, it’s everything we’ve done is gonna be on display. So we’re kinda just to me, like.
314 00:40:18.390 ⇒ 00:40:40.649 Robert Tseng: the only urgent priority is just to make sure everything works, and we don’t run into the oh, this is not refreshed, or this dashboards broken, or whatever like, just as long as it shows data. And we can actually have a productive conversation about it. I think that that’s that’s the main thing I would, and paid attention to before this 2 Pm. Eastern call
315 00:40:41.320 ⇒ 00:40:48.620 Demilade Agboola: Yeah. So so it it will be fine, because it’s monthly data. So even if it’s off by like one day, it wouldn’t, it wouldn’t jump out.
316 00:40:48.620 ⇒ 00:40:49.599 Demilade Agboola: Yeah, yeah.
317 00:40:50.460 ⇒ 00:40:52.529 Demilade Agboola: But it’s just one of those things where
318 00:40:52.650 ⇒ 00:40:57.989 Demilade Agboola: you know, I ideally want us to get ahead of it. So it doesn’t. Things don’t just look weird
319 00:40:59.120 ⇒ 00:41:00.879 Demilade Agboola: over time. Yeah.
320 00:41:01.880 ⇒ 00:41:04.519 Demilade Agboola: So we’re fine for the call. But, like, you know, just
321 00:41:04.650 ⇒ 00:41:06.849 Demilade Agboola: ideally, we are just trying to get ahead of it.
322 00:41:08.590 ⇒ 00:41:15.870 Robert Tseng: I mean, if we have a few minutes, can we just like click into every published dashboard and just look at it and just make sure it looks fine.
323 00:41:18.790 ⇒ 00:41:21.150 Demilade Agboola: Yeah, I mean to be fair. They look fine this morning. So
324 00:41:21.150 ⇒ 00:41:21.840 Robert Tseng: Okay.
325 00:41:22.350 ⇒ 00:41:24.260 Demilade Agboola: That that is.
326 00:41:25.220 ⇒ 00:41:33.600 Robert Tseng: Yeah, so okay, Adam, Danny, Joshua, pull this up. Great. This is their revenue and orders thing.
327 00:41:34.180 ⇒ 00:41:48.320 Robert Tseng: And yeah, they’re gonna filter. But probably ncac products or whatever. So I really just mentioned, this here wish is the Ncac modeling change that you made is that gonna be? Is that would that be reflected here, too, does that impact this dashboard
328 00:41:51.460 ⇒ 00:41:56.160 Awaish Kumar: Yeah, the modeling change I made it is March. It should reflect like
329 00:41:57.220 ⇒ 00:42:02.250 Robert Tseng: Okay. So in that case our Ncac. Should be lower than it was before. So
330 00:42:03.200 ⇒ 00:42:06.630 Robert Tseng: I mean, 800 looks pretty high to me. But okay, sure.
331 00:42:08.569 ⇒ 00:42:18.390 Robert Tseng: I’m just looking at the top 3 revenue products, semaglutide $1,000 and cac in February.
332 00:42:18.790 ⇒ 00:42:19.870 Robert Tseng: Yikes.
333 00:42:20.130 ⇒ 00:42:25.720 Robert Tseng: I I mean, I don’t. I’m not gonna say, that’s wrong. I don’t know for sure. But that seems
334 00:42:26.750 ⇒ 00:42:29.869 Robert Tseng: pretty high. Let me just yeah, okay.
335 00:42:30.140 ⇒ 00:42:32.700 Robert Tseng: I’m while you guys are going through this
336 00:42:37.180 ⇒ 00:42:39.629 Demilade Agboola: Has a can you filter to yesterday
337 00:42:46.480 ⇒ 00:42:49.770 Demilade Agboola: I was so days, and then yesterday.
338 00:42:50.630 ⇒ 00:42:55.670 Demilade Agboola: It doesn’t seem to be put up in numbers. I don’t necessarily know why, because the refreshes are.
339 00:42:57.980 ⇒ 00:43:00.520 Demilade Agboola: or like the refreshes that have been happening
340 00:43:01.510 ⇒ 00:43:02.850 Aakash Tandel: Don’t think it works
341 00:43:05.230 ⇒ 00:43:08.100 Demilade Agboola: So last 6 days does bring something, I believe.
342 00:43:16.610 ⇒ 00:43:20.758 Demilade Agboola: but like the data sources refreshed this morning, and
343 00:43:22.010 ⇒ 00:43:22.929 Aakash Tandel: It’s not working.
344 00:43:27.840 ⇒ 00:43:28.710 Demilade Agboola: Yeah.
345 00:43:34.160 ⇒ 00:43:39.479 Robert Tseng: Because it’s at weekly. So if you do like less than a week, it’s not gonna show anything
346 00:43:40.530 ⇒ 00:43:43.720 Demilade Agboola: Okay, that might be some something to just like note.
347 00:43:44.230 ⇒ 00:43:49.749 Demilade Agboola: Also, like, if you do like the yesterday, you still get the top top level numbers. You just don’t get the charts
348 00:43:50.290 ⇒ 00:43:50.790 Robert Tseng: Hey?
349 00:43:52.570 ⇒ 00:43:53.700 Robert Tseng: Do you?
350 00:43:55.480 ⇒ 00:43:55.870 Robert Tseng: Yeah.
351 00:43:55.870 ⇒ 00:44:00.039 Demilade Agboola: Okay. Those are. Those are, those are static, though those are just based on the month
352 00:44:00.040 ⇒ 00:44:02.330 Robert Tseng: Yeah. I don’t think we can show day
353 00:44:04.020 ⇒ 00:44:08.750 Demilade Agboola: Okay. So if yeah, in that case, maybe we should just like put a note somewhere
354 00:44:09.730 ⇒ 00:44:13.540 Demilade Agboola: so that it’s like we don’t get questions about why it seems broken
355 00:44:27.400 ⇒ 00:44:29.193 Aakash Tandel: Okay, yeah,
356 00:44:31.310 ⇒ 00:44:38.030 Aakash Tandel: like the like, limited, like, the the lowest amount of you have to visualize this in weeks. And
357 00:44:38.340 ⇒ 00:44:42.090 Aakash Tandel: basically, you have to see more than one, you know. That’s like kind of the Tldr
358 00:44:42.720 ⇒ 00:44:43.610 Demilade Agboola: Yeah.
359 00:44:43.610 ⇒ 00:44:49.459 Robert Tseng: Which is fine. I think we have the snapshot view, which is like, if you want to see day, just use that one
360 00:44:51.530 ⇒ 00:44:53.099 Aakash Tandel: That’s fair. Okay?
361 00:44:53.370 ⇒ 00:44:55.529 Aakash Tandel: Okay? So we feel good about this one.
362 00:45:04.180 ⇒ 00:45:05.289 Aakash Tandel: Everything looks good.
363 00:45:07.100 ⇒ 00:45:10.360 Robert Tseng: Yeah, well, I don’t.
364 00:45:11.930 ⇒ 00:45:18.209 Robert Tseng: The tiles like don’t work. Once you change to 6 months, or whatever
365 00:45:20.410 ⇒ 00:45:23.379 Robert Tseng: or or do they like that at the top
366 00:45:23.940 ⇒ 00:45:29.960 Robert Tseng: like I don’t know, for is there a supposed? Is that it? That’s it for the tiles? It’s just like March, April, or something
367 00:45:31.260 ⇒ 00:45:34.580 Demilade Agboola: Yeah, I think they’re static. I don’t. I don’t necessarily think
368 00:45:34.580 ⇒ 00:45:39.250 Robert Tseng: Isn’t there supposed to be like a percent change like thing? And
369 00:45:40.070 ⇒ 00:45:46.109 Demilade Agboola: I think you. I saw that when you hovered over. But I don’t think again. I think I don’t think that’s necessary. Dynamic.
370 00:45:46.630 ⇒ 00:45:48.370 Demilade Agboola: Okay, note that it’s not there.
371 00:45:48.860 ⇒ 00:45:49.620 Robert Tseng: Okay.
372 00:45:49.880 ⇒ 00:45:52.529 Aakash Tandel: Yeah, it’s a yeah. It’s a tool tip, I guess
373 00:45:53.790 ⇒ 00:45:55.349 Robert Tseng: Okay. Okay.
374 00:46:00.750 ⇒ 00:46:01.580 Robert Tseng: Okay.
375 00:46:04.510 ⇒ 00:46:05.629 Aakash Tandel: Do they go over this one
376 00:46:11.700 ⇒ 00:46:14.629 Demilade Agboola: Yeah, this is just like a start. But this is fine.
377 00:46:14.630 ⇒ 00:46:16.960 Robert Tseng: Yeah, this one is, yeah, we’ve
378 00:46:17.276 ⇒ 00:46:21.073 Demilade Agboola: Send every day, or like, just send the wrong numbers. That’s fine.
379 00:46:31.990 ⇒ 00:46:36.570 Robert Tseng: Last 5 days. No gummies. Yeah, they haven’t been spending on.
380 00:46:36.740 ⇒ 00:46:38.009 Robert Tseng: or they’re going to scale it.
381 00:46:38.570 ⇒ 00:46:44.660 Robert Tseng: Well, it’s like, huh interesting.
382 00:46:49.050 ⇒ 00:46:57.190 Robert Tseng: Well, some of those I’ve been with them. Yeah, sure, yeah, can we click on cerm
383 00:47:01.480 ⇒ 00:47:03.319 Robert Tseng: some Moreland? Yeah.
384 00:47:07.400 ⇒ 00:47:15.990 Demilade Agboola: Again. This chart has the same thing where you also can still see yesterday, but, like the last couple of days
385 00:47:22.660 ⇒ 00:47:23.370 Aakash Tandel: Yeah.
386 00:47:25.450 ⇒ 00:47:28.289 Demilade Agboola: Now we see Bill from the last 5 days.
387 00:47:29.140 ⇒ 00:47:32.590 Demilade Agboola: So once you make it less than 5 days, you you can’t see it again.
388 00:47:51.360 ⇒ 00:47:52.909 Demilade Agboola: so it just
389 00:47:55.900 ⇒ 00:47:58.290 Robert Tseng: Yeah. The Ltv and Ncac section.
390 00:47:58.440 ⇒ 00:48:04.959 Robert Tseng: Well, like, yeah, you’re always looking at monthly. Can you hover over the 1.4 1 k.
391 00:48:09.050 ⇒ 00:48:13.070 Aakash Tandel: 1.4 1 k. Hold on! It’s frozen.
392 00:48:18.820 ⇒ 00:48:21.860 Aakash Tandel: 1.4 1 k. This year.
393 00:48:22.570 ⇒ 00:48:24.060 Robert Tseng: Yeah. October.
394 00:48:24.940 ⇒ 00:48:27.289 Robert Tseng: Let me just take a quick screenshot of that
395 00:48:28.100 ⇒ 00:48:28.999 Aakash Tandel: Yeah let me zoom in
396 00:48:32.130 ⇒ 00:48:36.220 Robert Tseng: That’s that is interesting.
397 00:48:38.760 ⇒ 00:48:40.180 Robert Tseng: 1.4.
398 00:48:41.880 ⇒ 00:48:45.270 Robert Tseng: Now, what the heck is she even showing me it’s the same thing.
399 00:48:47.560 ⇒ 00:48:51.010 Robert Tseng: Ltv, check breakdown?
400 00:48:52.020 ⇒ 00:48:56.430 Robert Tseng: Well, yeah, something is off about that.
401 00:48:56.720 ⇒ 00:49:01.560 Robert Tseng: It didn’t show that before, like
402 00:49:07.250 ⇒ 00:49:11.510 Robert Tseng: this is what I don’t know how to post it. Maybe I’ll just post it in zoom. But, like
403 00:49:12.510 ⇒ 00:49:22.769 Robert Tseng: you guys, look at this. This is what I was sent from like their team. And it’s like, why is the Ltv so low? It looks like it’s point 7, whatever like.
404 00:49:25.850 ⇒ 00:49:37.869 Robert Tseng: And so I was confused. Because that’s not what it’s like. The filters are the same like this is what Akash is filtering for? Just not what they’re showing, or am I reading this wrong? I don’t. What is going on here
405 00:49:43.960 ⇒ 00:49:48.650 Demilade Agboola: So what what about the month here? Cause could that like? How does that filter?
406 00:49:50.540 ⇒ 00:50:01.650 Demilade Agboola: Because potentially, maybe they selected different multiple values quote unquote, because that we we don’t necessarily know the filters are there, or does it automatically, just like when you pick the range of date, does it filter as you should
407 00:50:02.620 ⇒ 00:50:09.229 Robert Tseng: I don’t know. It’s a good question. I imagine. If you pick the range of dates it should automatically impact the month, year
408 00:50:09.890 ⇒ 00:50:12.509 Demilade Agboola: I imagine that as well. But you know.
409 00:50:13.640 ⇒ 00:50:18.270 Demilade Agboola: since the filters look exactly the same, my guess is that’s where the difference is.
410 00:50:20.450 ⇒ 00:50:21.200 Robert Tseng: Okay.
411 00:50:23.550 ⇒ 00:50:25.500 Aakash Tandel: Next bus.
412 00:50:27.580 ⇒ 00:50:35.200 Aakash Tandel: Okay? And then this was no, it does not Whoa.
413 00:50:35.730 ⇒ 00:50:40.330 Aakash Tandel: The visualization just has, like the last 12 months. But
414 00:50:41.190 ⇒ 00:50:44.770 Aakash Tandel: I guess the data set underlying contains this.
415 00:50:45.070 ⇒ 00:50:45.960 Aakash Tandel: All of us
416 00:50:50.760 ⇒ 00:50:53.840 Demilade Agboola: So that potentially could be the disparity. And
417 00:50:54.010 ⇒ 00:51:00.050 Demilade Agboola: I’m not exactly sure what is happening with month here and how we’re using that to calculate this
418 00:51:03.550 ⇒ 00:51:14.990 Robert Tseng: Yeah, I don’t. I don’t get it. I mean, I don’t even know why we have month, year, if we have range of dates, but I guess it’s just to make it easier for them to select the month.
419 00:51:15.210 ⇒ 00:51:16.279 Robert Tseng: But it’s not
420 00:51:17.420 ⇒ 00:51:23.000 Robert Tseng: like, okay, let’s let’s just okay. Range of dates last 12 months month year. Let’s select October 2024.
421 00:51:35.250 ⇒ 00:51:40.889 Robert Tseng: Okay, yeah. So that tells us 1.4 K, which is which is like what that that would match looker.
422 00:51:42.530 ⇒ 00:51:46.306 Robert Tseng: Okay, I’m like believing that this is still user error. And
423 00:51:47.270 ⇒ 00:51:55.559 Robert Tseng: I don’t know. I mean this whole like explanation that I wish. And I would have to be digging into. It’s like, what are we even really explaining here
424 00:52:00.290 ⇒ 00:52:00.770 Aakash Tandel: Yeah.
425 00:52:00.770 ⇒ 00:52:02.450 Robert Tseng: Okay. But I wish this kind of like.
426 00:52:03.170 ⇒ 00:52:05.930 Robert Tseng: oh, and we were saying, earlier
427 00:52:06.050 ⇒ 00:52:19.539 Robert Tseng: revenue is different in between the 2 models. And so you would expect Ltv. To be different. But now what this is showing is this is actually the same as user summary, or it’s not exactly the same, but it’s it’s pretty close. It’s not a hundred percent off
428 00:52:21.740 ⇒ 00:52:33.880 Awaish Kumar: Yeah, the the revenue is different when we actually select by the or like, the transaction revenue.
429 00:52:35.900 ⇒ 00:52:43.790 Awaish Kumar: When we select, based on this transaction rank, then it becomes different, because
430 00:52:46.460 ⇒ 00:53:15.819 Awaish Kumar: because in order details, they just partition by transaction id. And for those orders where there’s only one product being like bought, so the transaction id is null. Hence the value for this transaction rank becomes null, and the transaction revenue in the order details. Table is mismatched with our transaction revenue in production.
431 00:53:16.080 ⇒ 00:53:19.020 Awaish Kumar: But if they use the order, total
432 00:53:19.410 ⇒ 00:53:25.970 Awaish Kumar: value from the order details, then it kind of gives a
433 00:53:26.250 ⇒ 00:53:31.059 Awaish Kumar: around the same value. It depends on which field they are using
434 00:53:33.510 ⇒ 00:53:34.360 Robert Tseng: I see.
435 00:53:39.660 ⇒ 00:53:50.839 Robert Tseng: I believe, that they’re using total like or order, like total orders from the Order summary because they’re using order, summary order, summary. The legacy orders summary is not using transaction revenue
436 00:53:55.690 ⇒ 00:53:56.300 Awaish Kumar: Okay.
437 00:53:59.850 ⇒ 00:54:03.180 Robert Tseng: I mean, I’m checking the looker. Dash right now, just to be sure, but
438 00:54:07.390 ⇒ 00:54:16.279 Robert Tseng: user summary cohort margin, da-da profit orders revenue per user revenue revenue orders.
439 00:54:17.500 ⇒ 00:54:26.220 Robert Tseng: The heck revenue per user revenue per user lifetime revenue per user.
440 00:54:30.560 ⇒ 00:54:36.450 Robert Tseng: Yeah, it’s just total revenue over total users. Total revenue. It comes from here
441 00:54:37.970 ⇒ 00:54:41.210 Robert Tseng: sum of revenue within the user summary model.
442 00:54:41.370 ⇒ 00:54:46.709 Robert Tseng: Yeah. So whatever the revenue field is in the user summary model, that’s what they’re using
443 00:54:47.570 ⇒ 00:54:50.870 Awaish Kumar: Okay, the the. It is order total in the
444 00:54:51.110 ⇒ 00:54:51.790 Robert Tseng: Okay?
445 00:54:54.280 ⇒ 00:55:01.029 Robert Tseng: So since it’s using order totals, then you you said you would expect it to be closer because they’re not using the
446 00:55:01.140 ⇒ 00:55:07.900 Robert Tseng: transaction logic that we’re using on transaction revenue, but on order details, or I mean on order totals. It’s closer.
447 00:55:08.020 ⇒ 00:55:12.369 Robert Tseng: Sorry I didn’t really catch that that state that that explanation
448 00:55:12.370 ⇒ 00:55:21.820 Awaish Kumar: Yeah, like, right now, I’m not 100% sure. But I think, like in one of the past investigations we’ve, I found that
449 00:55:22.330 ⇒ 00:55:31.950 Awaish Kumar: like this, when we create this ranking based on transaction. Id, it is basically flawed in order details, because
450 00:55:32.170 ⇒ 00:55:41.810 Awaish Kumar: when it is the value of transaction, Id is null. This rank becomes null, and if, when and whenever we calculate basically
451 00:55:41.950 ⇒ 00:55:48.369 Awaish Kumar: 1st transaction revenue, where the transaction rank is one, it gives us the wrong numbers.
452 00:55:48.570 ⇒ 00:55:54.059 Awaish Kumar: And and it should not do that for order. Total like, that’s what.
453 00:55:54.330 ⇒ 00:55:56.299 Awaish Kumar: But I think right now. What if
454 00:55:56.680 ⇒ 00:56:00.160 Robert Tseng: Oh, I see. So what what you’re saying is, yeah, I mean the
455 00:56:00.650 ⇒ 00:56:10.650 Robert Tseng: in our model we’ve adjusted for the transactions situation. Where, if? Like, Yeah, where’s order? Details
456 00:56:11.110 ⇒ 00:56:19.919 Robert Tseng: may not count like valid transaction, like the what they consider to be the 1st transaction is not the same as what we do, and that’s why the totals would be off
457 00:56:22.654 ⇒ 00:56:23.179 Awaish Kumar: Yes!
458 00:56:24.360 ⇒ 00:56:29.030 Robert Tseng: Yeah. And that would only really matter when we’re trying to do stuff that’s like,
459 00:56:31.020 ⇒ 00:56:37.760 Robert Tseng: well, I mean, this is like, kind of a 10 of worms, but share of like, if we’re trying to do retention of
460 00:56:38.130 ⇒ 00:56:44.710 Robert Tseng: customers since their 1st order, like you would be using like 1st transaction.
461 00:56:44.870 ⇒ 00:56:55.719 Robert Tseng: And if you built that off of order details, it would be different than our model, because their 1st order for the 1st transaction for the 1st order is not going to be the same
462 00:56:56.220 ⇒ 00:57:07.690 Robert Tseng: as what we show in our model, so like the that that retention number would be off. But, Ltv. If it’s just purely off of summing up order totals.
463 00:57:08.464 ⇒ 00:57:15.240 Robert Tseng: Then it doesn’t really matter about the transactions, and that’s similar to
464 00:57:15.530 ⇒ 00:57:23.930 Robert Tseng: well, I I thought we were summing transaction revenue in our model. So I think that’s why I was confused on why, that would still be the same as order totals
465 00:57:25.270 ⇒ 00:57:31.679 Awaish Kumar: Yeah, we are summing transaction revenue because we are like counting it as
466 00:57:31.790 ⇒ 00:57:38.709 Awaish Kumar: so whenever there are multiple transactions for the same order, then we take the 1st
467 00:57:38.850 ⇒ 00:57:55.070 Awaish Kumar: where transaction rank is one, we take the total value because this total price is basically the total for all the products, even though if there are multiple transaction rows there, and secondly, for the orders where there’s only
468 00:57:55.080 ⇒ 00:58:10.220 Awaish Kumar: a single product being bought. So hence there’s only single row and transaction id for those rows is null, but in our model we are take. We take care of that, using, saying that if transaction id is null, use the
469 00:58:10.370 ⇒ 00:58:17.909 Awaish Kumar: order Id instead, and hence we will have some rank for those as well, and we don’t have enough for those orders
470 00:58:18.660 ⇒ 00:58:29.549 Robert Tseng: Yeah, I, I don’t know. Yeah, okay, sure, I, yeah, I, yeah, okay.
471 00:58:31.460 ⇒ 00:58:34.769 Robert Tseng: okay, yeah, that’s fine. I don’t want to go down this rabbit hole anymore. I think
472 00:58:34.900 ⇒ 00:59:00.549 Robert Tseng: as long as I have a clear understanding of why or as long as I have a clear understanding that summing order totals on the order. Details is, is it close to what we have in transaction revenue like I? I think that to me is still like the the question there. I I understand that the difference in the transaction revenue would is, is there? But I I from your answer. I still don’t think it’s clear to me. Why order totals is is
473 00:59:00.650 ⇒ 00:59:10.350 Robert Tseng: is is similar to transaction revenue. I think that’s that’s probably the one question I would wanna be able to answer, and then I can write the explanation to them.
474 00:59:12.080 ⇒ 00:59:15.540 Awaish Kumar: Okay, let me have a look and then write down
475 00:59:15.670 ⇒ 00:59:19.080 Awaish Kumar: the findings or or the answer to this question.
476 00:59:19.080 ⇒ 00:59:20.320 Robert Tseng: Okay, thank you.
477 00:59:22.500 ⇒ 00:59:32.580 Robert Tseng: Cool. I know we went down this kind of tangent here. But yeah, I mean, honestly, guys, we should be, we should be doing this like once a week, just clicking through the dashboards and figuring out like.
478 00:59:32.860 ⇒ 00:59:50.931 Robert Tseng: well, if I were the end consumer like, how would I be using this. And you know, I think this is a much needed exercise. I mean, I’ll we you guys don’t have to stay on to do this like I can keep doing this in my own time. I probably just won’t go to Demos, and I’m gonna just kinda keep clicking through the rest of the dashboards.
479 00:59:51.440 ⇒ 00:59:57.313 Robert Tseng: Dave, a lot of I know we didn’t end up talking about some stuff.
480 00:59:58.020 ⇒ 01:00:04.490 Demilade Agboola: Okay, just I’ve I’ve moved the retention dashboard to the published dashboard folder. So
481 01:00:05.160 ⇒ 01:00:05.900 Robert Tseng: Okay.
482 01:00:06.950 ⇒ 01:00:14.940 Robert Tseng: yeah. I mean, it’s not urgent. So maybe we will not end up meeting today. But I’m pretty. I’m pretty booked, so I don’t know if I’ll be able to find another slot right now.
483 01:00:15.360 ⇒ 01:00:18.400 Demilade Agboola: Yeah, I noticed your calendar was quite, quite busy
484 01:00:18.620 ⇒ 01:00:19.210 Robert Tseng: Okay.
485 01:00:20.342 ⇒ 01:00:35.590 Robert Tseng: Alright. Well, that’s fine. We’ll just let’s just kind of step out of it, for now. I’ll I’ll ping you guys on slack. We need to. But I think this is this was good. I think it all kind of related to the questions that we were asking earlier. So thanks, guys.
486 01:00:36.300 ⇒ 01:00:37.179 Aakash Tandel: Thanks. Y’all
487 01:00:37.460 ⇒ 01:00:38.140 Robert Tseng: Bye.
488 01:00:38.140 ⇒ 01:00:39.030 Demilade Agboola: Later, bye.