Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-06-12 Meeting participants: Awaish Kumar, Fireflies.ai Notetaker Awaish, Robert Tseng, Annie Yu, Demilade Agboola, Josh , rajakhan
WEBVTT
1 00:02:27.890 ⇒ 00:02:28.850 Robert Tseng: Hey! Wish.
2 00:02:31.620 ⇒ 00:02:32.280 Awaish Kumar: Hello!
3 00:02:35.390 ⇒ 00:02:36.390 Annie Yu: Hello!
4 00:02:37.300 ⇒ 00:02:38.050 Robert Tseng: And he
5 00:02:54.690 ⇒ 00:02:59.410 Robert Tseng: give me like a minute. I’m like kind of pulling up some stuff, and I’ll give them a lot a bit of time.
6 00:03:09.440 ⇒ 00:03:12.559 Robert Tseng: Actually, I think if if you guys could take the 1st
7 00:03:12.700 ⇒ 00:03:20.359 Robert Tseng: few minutes to just update tickets like I didn’t, I didn’t groom yesterday. So I think. Make sure everything kind of has an updated
8 00:03:21.060 ⇒ 00:03:25.710 Robert Tseng: that that needs to. And yeah, well, whoops!
9 00:03:26.070 ⇒ 00:03:30.760 Robert Tseng: I will do the same. We’ll just. We’ll just do that for the 1st like 3 min.
10 00:04:49.780 ⇒ 00:04:54.711 Robert Tseng: It didn’t want it. We were just taking some time to update tickets and stuff
11 00:04:55.690 ⇒ 00:04:59.849 Robert Tseng: but now that you’re here, I’ll I’ll share my screen and we’ll just kinda go through it all.
12 00:05:03.670 ⇒ 00:05:09.629 Robert Tseng: Okay. So I have. I’m gonna start by stuff that’s overdue just because we have a lot of stuff that’s kind of
13 00:05:09.950 ⇒ 00:05:11.190 Robert Tseng: document pass.
14 00:05:11.680 ⇒ 00:05:15.399 Robert Tseng: So I’m just gonna go through client review.
15 00:05:18.010 ⇒ 00:05:27.480 Robert Tseng: yeah, Mattesh had some updates. Yeah, I was waiting for, like our meeting transcript to kind of convert this ticket. So no movement on this. It’s kind of on me.
16 00:05:27.780 ⇒ 00:05:31.250 Robert Tseng: Same with the marketing dashboard as well, I believe.
17 00:05:34.670 ⇒ 00:05:36.860 Robert Tseng: Some I/O capabilities.
18 00:05:37.359 ⇒ 00:05:41.150 Robert Tseng: Yeah, I don’t really understand the follow up here between
19 00:05:41.290 ⇒ 00:05:45.449 Robert Tseng: Cutter and and Bobby, so I’ll have to go look at that
20 00:05:47.430 ⇒ 00:05:57.409 Robert Tseng: and then Jonah had some feedback here, I think, Andy, did you take a look at it? I know that you sent a message about updating formulas. But what about? Where? Where are we at with this.
21 00:05:58.120 ⇒ 00:06:05.180 Annie Yu: Yeah, I I’m just not sure if we have all the fields that he mentioned, though, like the merchant fees and all that.
22 00:06:11.830 ⇒ 00:06:20.210 Robert Tseng: Okay. Yeah. If you could make whatever we don’t have like.
23 00:06:20.840 ⇒ 00:06:36.450 Robert Tseng: you should if if you could let us know what we don’t have. And then I mean merchant fees. That’s just like a platform fee, like I mean, it’s a hard, coded assumption. We could add that in ourselves. So I just I don’t really know what what level of effort it is to get it to
24 00:06:36.600 ⇒ 00:06:39.600 Robert Tseng: the profitability that he uses.
25 00:06:39.920 ⇒ 00:06:41.480 Robert Tseng: But if it’s just like.
26 00:06:41.640 ⇒ 00:06:48.820 Robert Tseng: yeah, if it’s just merchant fees, that’s pretty easy. That’s like a probably 2.9% or or 4% or something. I don’t know off the top of my head.
27 00:06:49.160 ⇒ 00:06:59.969 Annie Yu: Okay, I think, yeah, I think there are 4 items that he mentioned. I can. I can list them out and see, like, I don’t believe we have any fields that indicate those, but I can list them out so
28 00:07:00.380 ⇒ 00:07:01.360 Annie Yu: you can see if we need.
29 00:07:01.360 ⇒ 00:07:09.270 Robert Tseng: I mean, I think most of the I mean, I’m assuming that most of these are just going to be hard coded assumptions. It’s just like a percentage of the total order or something.
30 00:07:09.620 ⇒ 00:07:11.810 Annie Yu: Kind of like what we’ve done for Joby before.
31 00:07:13.720 ⇒ 00:07:20.640 Robert Tseng: Okay, so we’ll follow up on that. And then yes, if you could follow up on Eden 3, 47
32 00:07:21.750 ⇒ 00:07:29.270 Robert Tseng: and leave a comment there, it’d be good. Okay, let’s talk about the okay staging model. Yeah. Did a lot of.
33 00:07:29.730 ⇒ 00:07:38.449 Robert Tseng: I know you were saying, this is something you were trying to get out by end of week. Kind of seems like there’s a bit of delay here. So what what’s kind of going on here.
34 00:07:39.744 ⇒ 00:07:48.270 Demilade Agboola: So I’m still trying to see if I get out by end of week. But I think more more realistically. Early next week will probably be the best thing.
35 00:07:49.062 ⇒ 00:07:51.590 Demilade Agboola: So with this, the
36 00:07:52.610 ⇒ 00:08:16.659 Demilade Agboola: for me, it’s just basically trying to test that the what I have makes sense. So basically, what I’m trying to do is for every like variant, id or product id, depending on what we’re using. I’m just trying to use the count the orders and then try and map it based off the type to the valve size that has been put in the sheet. And so I’m just trying to test, and be sure that, like it works properly.
37 00:08:17.097 ⇒ 00:08:29.289 Demilade Agboola: And then, once that once we’re once that testing phase is done, I’ll put out a staging model, for I need to be able to so early next week I need to be able to make a dashboard of it.
38 00:08:29.540 ⇒ 00:08:34.569 Demilade Agboola: and once Annie has like feedback off that we will be able to have it to production.
39 00:08:36.799 ⇒ 00:08:37.499 Robert Tseng: Got it.
40 00:08:39.339 ⇒ 00:08:40.209 Robert Tseng: Okay.
41 00:08:44.129 ⇒ 00:08:48.999 Robert Tseng: I’m hoping the note taker caught that. So someone asked, I’m just looking to give them that answer.
42 00:08:51.489 ⇒ 00:08:55.509 Robert Tseng: With this one. This is the
43 00:09:00.039 ⇒ 00:09:07.819 Robert Tseng: right. So I guess what you just talked about was like variant file size. This is more categories. We had asked some questions here.
44 00:09:10.340 ⇒ 00:09:18.870 Demilade Agboola: Yeah, I also. I bumped it again today. Cause they hadn’t responded. Neither of them responded. So I bumped it again. You could, you know. Say that?
45 00:09:19.347 ⇒ 00:09:25.960 Demilade Agboola: So yeah, that will just be once they respond. It shouldn’t be a hard fix from that point.
46 00:09:26.571 ⇒ 00:09:41.328 Demilade Agboola: Because we’ve already done the groundwork with, you know, been able to categorize all these various products into like high level product names. So being so, writing the product names back into like categories should be should be easy or easier.
47 00:09:42.180 ⇒ 00:09:42.880 Demilade Agboola: Yeah.
48 00:09:42.880 ⇒ 00:09:47.651 Robert Tseng: Okay? So yeah, it’s just that they haven’t given you the categories yet. That’s understandable.
49 00:09:49.250 ⇒ 00:09:54.150 Robert Tseng: I would say that this is really more like waiting on client review. Yeah.
50 00:09:55.810 ⇒ 00:10:05.830 Robert Tseng: yeah, I think for the record of this meeting transcript. I think it’d be great if we could have a automation that takes everything in our client review, and just like
51 00:10:06.320 ⇒ 00:10:07.540 Robert Tseng: reminds
52 00:10:07.770 ⇒ 00:10:15.990 Robert Tseng: the client of the things that we’re waiting on. It probably needs a human in the loop for us to like assign, which stakeholder needs to respond to it. But
53 00:10:16.160 ⇒ 00:10:17.719 Robert Tseng: I feel like a lot of
54 00:10:18.180 ⇒ 00:10:22.739 Robert Tseng: my time is just spent like following up on stuff, because people are slow to respond.
55 00:10:23.301 ⇒ 00:10:37.359 Annie Yu: Robert, I probably wouldn’t put that ticket into the review, because I I do believe there is a separate ticket for category mapping specifically, but this one is covering other items for the dashboard
56 00:10:38.720 ⇒ 00:10:41.100 Annie Yu: and and the categories is part of it.
57 00:10:41.700 ⇒ 00:10:42.750 Robert Tseng: Oh, I see.
58 00:10:42.950 ⇒ 00:10:46.534 Robert Tseng: Yeah, I know. We ended up. Okay, well, I think just
59 00:10:50.490 ⇒ 00:10:52.160 Robert Tseng: my understanding this correctly.
60 00:10:53.090 ⇒ 00:11:00.210 Annie Yu: And is this ticket for me? Cause? I thought that was for the malade.
61 00:11:07.340 ⇒ 00:11:12.020 Robert Tseng: I, this is this part of the same thing. So I guess it should be back in a lot. Okay.
62 00:11:13.100 ⇒ 00:11:16.060 Robert Tseng: should I just consolidate. This is this is the same thing. Right?
63 00:11:20.430 ⇒ 00:11:23.289 Robert Tseng: I’m gonna cancel. I’m gonna cancel this. I think this is the same thing.
64 00:11:29.490 ⇒ 00:11:30.230 Robert Tseng: Okay?
65 00:11:36.420 ⇒ 00:11:41.009 Robert Tseng: And I’ll tip off my filters here.
66 00:11:41.870 ⇒ 00:11:47.830 Robert Tseng: I was going off of due date in the past.
67 00:11:48.870 ⇒ 00:11:51.469 Robert Tseng: It’s still active. Okay, great.
68 00:11:55.400 ⇒ 00:11:56.969 Robert Tseng: Great. What about this one?
69 00:11:58.940 ⇒ 00:12:01.545 Robert Tseng: Oh, this is really on me.
70 00:12:02.390 ⇒ 00:12:07.820 Robert Tseng: yeah, I mean, it’s kind of this kind of died I mean. I said it to Annie, because it was like
71 00:12:08.230 ⇒ 00:12:15.679 Robert Tseng: we had already sent the query. They wanted to make some enhancements. I don’t know. Just not gonna get on our roadmap. It’s not that important. So
72 00:12:16.920 ⇒ 00:12:17.750 Robert Tseng: that’s fine.
73 00:12:21.820 ⇒ 00:12:27.759 Robert Tseng: Yeah, okay, I need to take Eden 3 0. 6, and you split it out into a new ticket. This will then be
74 00:12:28.080 ⇒ 00:12:30.480 Robert Tseng: Mark. This done, but then we didn’t need to pay for this.
75 00:12:32.287 ⇒ 00:12:36.559 Robert Tseng: Okay, then, so product sequence model, let’s talk about that.
76 00:12:40.593 ⇒ 00:12:43.160 Demilade Agboola: So product, sequest model is
77 00:12:43.600 ⇒ 00:12:47.280 Demilade Agboola: also in progress. So it’s basically
78 00:12:51.400 ⇒ 00:12:57.280 Demilade Agboola: the 1st product they got on. And then then subsequent products. They got on after that 1st product.
79 00:12:58.405 ⇒ 00:13:02.900 Demilade Agboola: So I had a call with Annie.
80 00:13:03.350 ⇒ 00:13:11.244 Demilade Agboola: and we’ve been able to like come to some conclusion on how that model look like. So I am. That is also being built out
81 00:13:11.660 ⇒ 00:13:17.929 Demilade Agboola: should be done today tomorrow, and then we can have that tested.
82 00:13:19.890 ⇒ 00:13:20.520 Robert Tseng: Okay.
83 00:13:23.610 ⇒ 00:13:25.759 Robert Tseng: yeah, I think this one.
84 00:13:32.520 ⇒ 00:13:36.130 Demilade Agboola: To be fair, like, I think also, the tech, like the
85 00:13:36.790 ⇒ 00:13:45.049 Demilade Agboola: the model that Annie had in mind isn’t necessarily a model that we’re gonna build out. Because we once we had conversations, certain things were tweaked.
86 00:13:45.450 ⇒ 00:13:48.250 Demilade Agboola: So that doesn’t necessarily reflect in ticket right now.
87 00:13:49.426 ⇒ 00:13:53.889 Demilade Agboola: But effectively, it’s just basically trying to see
88 00:13:54.500 ⇒ 00:14:02.629 Demilade Agboola: the first, st the second, 3, rd 4, th that kind of thing, and just kind of see the dates as well, so we can see what dates
89 00:14:03.367 ⇒ 00:14:07.200 Demilade Agboola: people are switching across different products.
90 00:14:10.690 ⇒ 00:14:15.450 Robert Tseng: Yeah, okay, so you’re saying, it’s not gonna look like this. Yeah, I mean, before, yeah.
91 00:14:15.450 ⇒ 00:14:16.570 Robert Tseng: build it out. If you can.
92 00:14:17.070 ⇒ 00:14:18.210 Robert Tseng: Huh? Sorry.
93 00:14:18.590 ⇒ 00:14:20.960 Demilade Agboola: Not exactly like this, but it’ll okay.
94 00:14:20.960 ⇒ 00:14:21.570 Demilade Agboola: Cool.
95 00:14:23.270 ⇒ 00:14:28.229 Robert Tseng: Yeah, can you? Can you send me what you think the schema should look like? So I can just look at it before you start to build it.
96 00:14:28.640 ⇒ 00:14:29.970 Demilade Agboola: Alright, sounds good.
97 00:14:29.970 ⇒ 00:14:30.785 Robert Tseng: Okay?
98 00:14:31.750 ⇒ 00:14:37.940 Robert Tseng: yeah, I think just if we’re gonna change the requirements here, like, I kind of want to know and what what’s what we’re changing it to.
99 00:14:38.870 ⇒ 00:14:39.980 Demilade Agboola: Okay. Sounds good.
100 00:14:39.980 ⇒ 00:14:49.619 Robert Tseng: I think Annie understood the con. I think Andy understands the use case for it, so I I don’t have a doubt that you I don’t. I don’t doubt that you guys have a you guys came up with a better way. But
101 00:14:50.190 ⇒ 00:14:52.500 Robert Tseng: yeah, I just, I just want to know what the change was.
102 00:14:52.650 ⇒ 00:14:53.450 Robert Tseng: Yeah.
103 00:14:53.790 ⇒ 00:14:54.470 Demilade Agboola: Sounds good.
104 00:14:54.470 ⇒ 00:15:03.580 Robert Tseng: Thanks. Okay, so that’s that. Then, okay, refunds. I think this is where we are with this.
105 00:15:03.710 ⇒ 00:15:10.240 Robert Tseng: Oh, you okay, I didn’t. Sorry I just. I didn’t see it yesterday. So any you wanna just let me know where we’re at here.
106 00:15:11.000 ⇒ 00:15:17.470 Annie Yu: Yeah, I believe this is done. They reacted to this, and we haven’t heard any
107 00:15:17.600 ⇒ 00:15:24.460 Annie Yu: issues with it. But we were able to deduplicate the rose.
108 00:15:26.210 ⇒ 00:15:26.535 Robert Tseng: Okay.
109 00:15:28.765 ⇒ 00:15:29.640 Robert Tseng: Yes.
110 00:15:29.640 ⇒ 00:15:35.500 Annie Yu: The with certain order numbers. We thought there are multiple, different
111 00:15:35.750 ⇒ 00:15:42.060 Annie Yu: refund amounts. And Katie said, in that case we should keep them all.
112 00:15:43.020 ⇒ 00:15:44.299 Annie Yu: So that’s what we did.
113 00:15:46.960 ⇒ 00:15:55.039 Robert Tseng: Multiple rows. Different refund amounts. Keep them all like, doesn’t that? Doesn’t that just keep the duplications like I don’t understand why what.
114 00:15:55.040 ⇒ 00:16:07.460 Annie Yu: Yeah, we’re just duplicating the rows with the identical refund amounts. But because, she said, there are different amounts that might mean like different transactions.
115 00:16:08.200 ⇒ 00:16:10.829 Robert Tseng: Yeah, okay, sure. I think that that’s.
116 00:16:11.530 ⇒ 00:16:16.869 Robert Tseng: And then we have the schedule. Seems like, we have the daily refund set up here. Okay, so
117 00:16:17.560 ⇒ 00:16:24.910 Robert Tseng: yeah, we added, and then the new that I’m I’m assuming it’s reflected here, like the changes in the emails. Okay, cool.
118 00:16:25.260 ⇒ 00:16:28.308 Robert Tseng: alright, great. Let’s just let’s consider that. Then.
119 00:16:31.280 ⇒ 00:16:35.500 Robert Tseng: yeah, I won’t talk about the recurring, and I won’t talk about this.
120 00:16:37.940 ⇒ 00:16:41.060 Robert Tseng: The spikes. Yeah, we can skip that yeah.
121 00:16:41.060 ⇒ 00:16:48.790 Annie Yu: I do have one question. I see the order to deliver investigation, and for this one
122 00:16:49.430 ⇒ 00:16:52.120 Annie Yu: we will also be able to fix.
123 00:16:52.390 ⇒ 00:16:59.080 Annie Yu: We the model added the fields, pre-calculated fields. So we updated that
124 00:16:59.910 ⇒ 00:17:08.930 Annie Yu: on tableau as well. But even after that we still have higher, like longer hours compared to
125 00:17:09.490 ⇒ 00:17:13.949 Annie Yu: the stats that Rebecca provided for Looker and Basque.
126 00:17:18.230 ⇒ 00:17:24.039 Robert Tseng: I don’t really care about the looker. But yeah, we’re higher than bass by 30%. That’s
127 00:17:25.150 ⇒ 00:17:26.620 Robert Tseng: concerning to me.
128 00:17:29.060 ⇒ 00:17:35.230 Robert Tseng: what did you update like, what? What change did we push like? I see it dropped by like 10 h, but, like.
129 00:17:35.230 ⇒ 00:17:47.289 Annie Yu: Yeah, previously, I did the calculation in tableau, but we didn’t have time stamp for order placed so able to do that calculation using the Timestamps. That’s why
130 00:17:47.780 ⇒ 00:17:49.220 Annie Yu: it’s lower now.
131 00:17:49.790 ⇒ 00:17:56.340 Robert Tseng: Okay, can you just run your logic by Chris Christiana? I think she would know. So I’m gonna have you do that
132 00:17:57.796 ⇒ 00:18:04.750 Robert Tseng: but she she she she knows the best best platform in inside out, so she’ll be able to tell you like
133 00:18:05.400 ⇒ 00:18:10.619 Robert Tseng: roughly, how it’s calculated, and then we can see if that matches what we’re saying.
134 00:18:12.260 ⇒ 00:18:12.950 Annie Yu: Okay.
135 00:18:22.190 ⇒ 00:18:26.149 Josh : Cool, hey? I I had a couple of things, Robert.
136 00:18:26.150 ⇒ 00:18:26.740 Robert Tseng: Yeah.
137 00:18:28.300 ⇒ 00:18:37.280 Josh : so definitely. Just just hop on my calendar later today. Just have Caitlin throw something on. We can chat about a couple of those other things you want to talk about.
138 00:18:37.570 ⇒ 00:18:38.050 Robert Tseng: Sure.
139 00:18:38.160 ⇒ 00:19:06.680 Josh : I have some ideas, some good ideas make a like and then and then in terms of like the couple of quick things I was hoping to add. So like, you know, like on those daily reports that we get that are sent to the the channels. I love those. By the way, I use those every day. I think they’re great. The couple of quick ads. So at the bottom of each one, it would be great to essentially have, like a really small like weekly
140 00:19:06.880 ⇒ 00:19:14.680 Josh : trend line where it shows like. For, like, you know, basically like a rolling week, are the numbers like, you know, total sales
141 00:19:14.790 ⇒ 00:19:32.160 Josh : across all of those products, and then total revenue across all those products is 2 different charts, just showing the like a line graph of if it’s going up, or if it’s going down, or if it’s staying the same, it’s just rolling for a week like super simple app. But I just wanna be able to see like, hey, based on.
142 00:19:32.160 ⇒ 00:19:50.210 Josh : you know, like, Hey, we yeah, we get the day daily snap. But then, like for me, I find myself always going back like the last couple of days saying, Okay, yesterday we had 82. Today we have 87, you know, the next, like, you know, day before that we had 93. And then, like, I just want like a a line graph. And then like a total, for like a week.
143 00:19:51.600 ⇒ 00:19:54.389 Robert Tseng: You want that rolled up, or you still want to broke it out by product.
144 00:19:54.770 ⇒ 00:19:59.889 Josh : You can just do it all rolled up just like, just generally speaking, just one line graph.
145 00:20:00.450 ⇒ 00:20:02.720 Robert Tseng: Okay, got it?
146 00:20:02.720 ⇒ 00:20:09.200 Josh : Because it works really well, because I’m trying to segment out new products versus glps.
147 00:20:09.700 ⇒ 00:20:11.170 Robert Tseng: Yeah. Directionally.
148 00:20:12.390 ⇒ 00:20:15.029 Robert Tseng: Okay, that’s really just for the fort. Josh, snapshot.
149 00:20:15.810 ⇒ 00:20:18.170 Josh : The 4 Josh, and then the new product.
150 00:20:19.050 ⇒ 00:20:20.190 Robert Tseng: Oh, right? Okay.
151 00:20:20.190 ⇒ 00:20:21.160 Demilade Agboola: And what about the.
152 00:20:21.160 ⇒ 00:20:21.689 Robert Tseng: You were. Gonna say.
153 00:20:22.720 ⇒ 00:20:35.269 Josh : The new product, and then the glp. One ones are the ones I really care about. It’s like, there’s like a glp, one report, and then there’s like a there’s a new product report, and like those are the 2 that I’d like to just see it added to.
154 00:20:35.640 ⇒ 00:20:46.479 Josh : And then we do have 1 1 major product that’s missing from both of those which is okay. I think maybe we have to add the Nad product to the new products.
155 00:20:46.770 ⇒ 00:20:55.669 Josh : But I am hesitant to do that because nad injections are not. They’re kind of in this weird in between, where like, they’re not exactly new, but they’re also not glps.
156 00:20:55.930 ⇒ 00:20:56.510 Josh : So
157 00:20:57.500 ⇒ 00:21:04.410 Josh : I gotta think about that. So no action on that one. But yeah, just the the new product report. Add the rolling
158 00:21:05.070 ⇒ 00:21:14.589 Josh : rolled up Macro sales thing for a week, and then for the glp product added there as well.
159 00:21:18.110 ⇒ 00:21:20.180 Robert Tseng: I’ll I’ll take it it out to the water. Yeah.
160 00:21:20.640 ⇒ 00:21:21.070 Demilade Agboola: Alright!
161 00:21:21.070 ⇒ 00:21:24.630 Josh : I mean, sure you could add to the fort, Josh. One, too. It would probably help. But
162 00:21:25.090 ⇒ 00:21:39.920 Josh : I mean, I was just saying those all those reports is a simple line graph. That’s it. So that was the one real quick thing for me. And then I also have Raja on the call. He’s running a lot of stuff for community and the stuff that we’re trying to do on circle
163 00:21:40.987 ⇒ 00:21:47.050 Josh : so I’ve been trying to get some data from this group for a while.
164 00:21:48.152 ⇒ 00:21:53.510 Josh : Trying to demonstrate the you know, hey, people that are active in circle?
165 00:21:53.780 ⇒ 00:22:17.290 Josh : Are they creating higher Ltvs for us or not so really just trying to like. Come up with some sort of metric, or, you know, some sort of like real, quick down and dirty dash that shows login activity of circle. And then the correlation to Ltv, so it’d be like a a circle user versus non circle user. And then who is delivering us more money.
166 00:22:18.500 ⇒ 00:22:18.840 Robert Tseng: Okay.
167 00:22:20.260 ⇒ 00:22:29.429 Robert Tseng: So I mean, I think I I just saw we had a we had a Circle project kind of queued up like the last month, but I don’t think we ever execute on it. So we still probably
168 00:22:29.620 ⇒ 00:22:32.680 Robert Tseng: I mean, I wish this will probably come your way. We’ll need the.
169 00:22:33.150 ⇒ 00:22:45.300 Robert Tseng: I think we looked into what it was gonna take to bring in the circle data. And then it seems like we just need some basic metrics on like a login activity, summary model or something. So
170 00:22:46.880 ⇒ 00:22:47.790 Robert Tseng: I think
171 00:22:48.550 ⇒ 00:22:56.810 Robert Tseng: I mean I’ll I’ll start the the I’ll start breaking it out. But then I think we’ll you’ll you’re gonna have to help fill in the details.
172 00:23:03.520 ⇒ 00:23:04.600 Josh : If we have.
173 00:23:05.130 ⇒ 00:23:16.599 Robert Tseng: What logins. And I mean, we just this is just a new just treated as a new new data source. I don’t really think we have it in in bigquery right now. So we have to build out a pipeline for it.
174 00:23:19.080 ⇒ 00:23:19.680 Josh : Yeah.
175 00:23:20.660 ⇒ 00:23:22.680 rajakhan: What would you need from my end.
176 00:23:26.420 ⇒ 00:23:26.930 rajakhan: Circle.
177 00:23:26.930 ⇒ 00:23:27.530 Robert Tseng: I will!
178 00:23:27.530 ⇒ 00:23:27.870 rajakhan: So.
179 00:23:30.410 ⇒ 00:23:42.559 Robert Tseng: Well, I mean we’ll we’ll let you know, I think. I’ll just have to see. What what do they actually allow us to bring in like we have to go read the Api docs figure out like, what’s the do we? Yeah, do we just.
180 00:23:42.560 ⇒ 00:23:47.480 Awaish Kumar: Yeah, we will need, like authentication to go log into the circle and see.
181 00:23:48.260 ⇒ 00:23:48.830 Robert Tseng: Yeah.
182 00:23:50.270 ⇒ 00:23:50.970 rajakhan: Okay.
183 00:23:53.895 ⇒ 00:23:54.210 rajakhan: Yeah.
184 00:23:54.210 ⇒ 00:23:58.770 Robert Tseng: So if you can give to.
185 00:24:01.580 ⇒ 00:24:08.930 Robert Tseng: I just slacked you 2 emails to add to circle for to give us full permissions there to go and look at it.
186 00:24:10.850 ⇒ 00:24:12.509 rajakhan: I’ll get that going for you guys.
187 00:24:18.330 ⇒ 00:24:21.400 Robert Tseng: Okay, and then any type form.
188 00:24:21.690 ⇒ 00:24:28.440 Robert Tseng: yeah, where are we at this? Can we? I feel like this is, we were confused on like where this was, but I feel like we could. We could get this out quickly.
189 00:24:29.120 ⇒ 00:24:32.960 Annie Yu: From my understanding yesterday. A wish will
190 00:24:33.790 ⇒ 00:24:40.190 Annie Yu: confirm with, I think, utam, because he built this out to make sure we have the updated data.
191 00:24:43.010 ⇒ 00:24:46.110 Awaish Kumar: Yeah, like, we have the updated data now. And
192 00:24:46.510 ⇒ 00:24:48.529 Awaish Kumar: Kutham said, it is coming from
193 00:24:48.940 ⇒ 00:24:52.940 Awaish Kumar: Basque related events. So it’s may maybe coming from segment.
194 00:24:53.420 ⇒ 00:24:55.880 Awaish Kumar: and it is being updated every day.
195 00:24:57.770 ⇒ 00:25:00.890 Robert Tseng: Okay, yeah. Then, Annie, can we can we get this one out? Yeah.
196 00:25:01.280 ⇒ 00:25:07.599 Annie Yu: So if that’s the case, that dashboard is ready, and it’s using that data source already.
197 00:25:07.980 ⇒ 00:25:11.330 Robert Tseng: Great, and it has the
198 00:25:11.630 ⇒ 00:25:16.799 Robert Tseng: didn’t he ask for like a search function in it like I forgot what the requirement was. It’s been like a week.
199 00:25:17.030 ⇒ 00:25:17.750 Annie Yu: I don’t see
200 00:25:17.750 ⇒ 00:25:23.199 Annie Yu: not in the ticket, though I don’t know, so he wants a search function. Is that it.
201 00:25:28.380 ⇒ 00:25:32.799 Annie Yu: We can. Hi, I I did add a highlight on the right.
202 00:25:34.075 ⇒ 00:25:38.630 Annie Yu: upper right. So in that highlight box you can search
203 00:25:39.410 ⇒ 00:25:44.020 Annie Yu: a keyword, and then it will highlight those questions with the keyword.
204 00:25:44.020 ⇒ 00:25:49.459 Robert Tseng: Yeah, okay, I will. I’ll I’ll have to double check.
205 00:25:49.810 ⇒ 00:25:51.130 Robert Tseng: I don’t know off the top of my head.
206 00:25:51.770 ⇒ 00:25:52.650 Robert Tseng: I mean.
207 00:25:55.610 ⇒ 00:26:06.360 Robert Tseng: alright. So this is for me to I. So if if it’s if it’s like ready to ship like, I’ll I can send it. I could send it to him. I’ll just make sure that we have. We’ve we’ve met everything that he asked for here.
208 00:26:06.360 ⇒ 00:26:10.250 Annie Yu: Yeah, yeah, let me know if there’s more adjustments needed.
209 00:26:10.660 ⇒ 00:26:11.810 Robert Tseng: Okay, cool
210 00:26:12.705 ⇒ 00:26:18.740 Robert Tseng: so that covers we’re saying, Yeah, this, you’re gonna you’re gonna do it. We’re gonna escalate that.
211 00:26:19.890 ⇒ 00:26:23.509 Robert Tseng: And then I’m assuming you haven’t touched this yet.
212 00:26:25.106 ⇒ 00:26:26.159 Annie Yu: Not yet.
213 00:26:27.060 ⇒ 00:26:27.700 Robert Tseng: Okay.
214 00:26:30.480 ⇒ 00:26:41.539 Annie Yu: I’m thinking, cause for this one in my kind of proposal thing. I broke them out into 3 phases. So 1st one’s just exploration.
215 00:26:43.280 ⇒ 00:26:48.150 Annie Yu: And then for that, even for that stage, I can have some takeaways.
216 00:26:48.870 ⇒ 00:26:59.190 Annie Yu: So we can show, like what fields are, what fields have higher correlation with the final Ltv.
217 00:27:00.580 ⇒ 00:27:01.240 Robert Tseng: Okay.
218 00:27:01.980 ⇒ 00:27:11.748 Robert Tseng: yeah, I think. I shared a notion, Doc, with you of like kind of my approach to this before. And it’s kind of where we had taken it. We pause it, but I guess
219 00:27:12.470 ⇒ 00:27:22.210 Robert Tseng: I don’t know if I see yours yet. If you want to just use the same, Doc, or I mean, it’s probably on a different ticket at this point. But if you do, you do, you recall me? Kind of going through? That would be.
220 00:27:23.988 ⇒ 00:27:27.899 Annie Yu: I did see one that you share before
221 00:27:29.480 ⇒ 00:27:35.719 Annie Yu: we I don’t. I don’t think I I don’t think that one’s for Ltv. Though, so I might be.
222 00:27:36.050 ⇒ 00:27:37.500 Annie Yu: Remember it wrong.
223 00:27:39.200 ⇒ 00:27:39.830 Robert Tseng: Okay.
224 00:27:42.340 ⇒ 00:27:52.510 Robert Tseng: yeah. Well, I guess to me, like the if we’re gonna if you’re gonna kind of get this, this spike was meant for you to go, and you kind of review what I had sent you before. I mean, if you, if
225 00:27:52.730 ⇒ 00:27:58.980 Robert Tseng: I can go and figure figure out like where where that was dropped. But I I kind of. I need a
226 00:27:59.570 ⇒ 00:28:06.440 Robert Tseng: yep. I want like an outline from you, so I can kind of figure out like what your phases are like. I don’t really have any context on how you were.
227 00:28:06.440 ⇒ 00:28:08.574 Annie Yu: I do have an outline, though.
228 00:28:09.270 ⇒ 00:28:15.550 Annie Yu: back in that, doc that we share when we propose the short term solution. The otv heat map.
229 00:28:18.070 ⇒ 00:28:22.480 Annie Yu: I can find it, too, but I do have every steps.
230 00:28:23.490 ⇒ 00:28:25.759 Annie Yu: I do have every step outlined there.
231 00:28:27.250 ⇒ 00:28:28.250 Robert Tseng: Hold on.
232 00:28:28.460 ⇒ 00:28:33.570 Robert Tseng: Okay, yeah, I I can’t find it. So if you can send it to me, that would be helpful.
233 00:28:34.970 ⇒ 00:28:35.760 Annie Yu: Here.
234 00:28:37.490 ⇒ 00:28:46.522 Robert Tseng: Okay. I think that should have caught us up on everything that’s overdue. There’s stuff that’s in flight. That’s kind of due. So I mean, I’m not. We don’t have time to go through it today. But
235 00:28:47.290 ⇒ 00:28:50.070 Robert Tseng: yeah, I think if we can close out everything that’s
236 00:28:51.693 ⇒ 00:28:57.299 Robert Tseng: I wanna follow up one more thing. Which is the request from Mattesh yesterday.
237 00:28:58.070 ⇒ 00:28:59.240 Robert Tseng: Yeah, I.
238 00:28:59.240 ⇒ 00:29:01.550 Annie Yu: And gross Kpi.
239 00:29:01.820 ⇒ 00:29:02.430 Robert Tseng: Yeah.
240 00:29:02.780 ⇒ 00:29:04.569 Annie Yu: Yeah, I saw your 310.
241 00:29:05.470 ⇒ 00:29:13.170 Annie Yu: Yeah. And there are some questions. So, and that’s also like one question I want to
242 00:29:13.940 ⇒ 00:29:18.570 Annie Yu: confirm with you, Robert. So I was able to make some
243 00:29:18.830 ⇒ 00:29:25.520 Annie Yu: change yesterday, and he said he wanted to see free to paid ratio.
244 00:29:26.180 ⇒ 00:29:33.290 Annie Yu: which I’m not sure that’s like revenue, if you remember.
245 00:29:33.620 ⇒ 00:29:39.614 Robert Tseng: No, I I remember. It’s all so I guess. Yeah, I thank you for even taking a crack at it
246 00:29:40.080 ⇒ 00:29:47.813 Robert Tseng: our like Zoom Transcript to like ticket generator thing wasn’t working yesterday, so I didn’t. I didn’t produce tickets from the Texas meeting.
247 00:29:48.270 ⇒ 00:29:52.784 Robert Tseng: I think it’s it’s working now. So I need. I need to run it through. I I know the details
248 00:29:53.320 ⇒ 00:30:03.113 Robert Tseng: pretty much like he thinks that the way that we’ve broken out paid and unpaid channels. We need to move the offer into. We need to move the offer into a paid channel.
249 00:30:03.410 ⇒ 00:30:03.830 Robert Tseng: No one’s.
250 00:30:03.830 ⇒ 00:30:04.569 Annie Yu: Have, like a clear.
251 00:30:05.790 ⇒ 00:30:09.500 Robert Tseng: That was done. Okay? And then, yeah, like the free to paid.
252 00:30:09.950 ⇒ 00:30:25.119 Robert Tseng: yeah, he, he just wants to know the the ratio of like free like channel from free, which is customer I/O to paid to pay channels. So we don’t like. It’s kind of sitting as uncategorized. And so there’s a fix that we need to make there on like
253 00:30:25.641 ⇒ 00:30:31.379 Robert Tseng: I mean, I’m assuming that most of that uncategorized is Customer I/O. We just didn’t label it that way.
254 00:30:31.801 ⇒ 00:30:45.680 Robert Tseng: So once we once we’re clear on what the freed should look like in the model, or then then I mean, I I trust what we have on the paid side. So I think that’s what’s blocking you from being able to do that calculation.
255 00:30:46.640 ⇒ 00:30:50.620 Annie Yu: Okay, then, can you? Please write kind of
256 00:30:50.820 ⇒ 00:30:54.959 Annie Yu: write the definition of the free to pay down.
257 00:30:55.090 ⇒ 00:30:57.129 Robert Tseng: Yeah, yeah, this, my.
258 00:30:57.570 ⇒ 00:30:59.199 Annie Yu: Also be a
259 00:30:59.420 ⇒ 00:31:08.189 Annie Yu: a thing that we want to address. So right now with the Kpis here in that marketing kpis, we are using product sales
260 00:31:08.480 ⇒ 00:31:30.390 Annie Yu: summary by transaction. Which match so that’s why, like all the numbers, match the rows and Ltv. Dashboard which Mattesh trusts. But if we want to get to the order level metrics, we would be using another model. That’s channel sales summary
261 00:31:30.810 ⇒ 00:31:43.130 Annie Yu: that we use for part of the marketing dashboard. But then, in that one we don’t have exactly matched revenue order, count and row as there.
262 00:31:44.600 ⇒ 00:31:48.680 Awaish Kumar: Yeah, like, I just applied the channel sales. Summary is basically.
263 00:31:49.100 ⇒ 00:31:52.030 Awaish Kumar: we are just getting all the orders and
264 00:31:52.200 ⇒ 00:32:00.370 Awaish Kumar: trying to categorize them into different channels. But we haven’t filtered out the abundant orders or things like that from there.
265 00:32:01.270 ⇒ 00:32:03.620 Awaish Kumar: Yeah, why can’t we just use the same model.
266 00:32:04.750 ⇒ 00:32:09.460 Annie Yu: Because in product sales summary there is no channel level breakdown.
267 00:32:09.460 ⇒ 00:32:10.300 Robert Tseng: Oh, I see!
268 00:32:11.410 ⇒ 00:32:12.740 Annie Yu: But I remember.
269 00:32:12.740 ⇒ 00:32:21.720 Awaish Kumar: How that now that we have channel information for both Addis plan and the revenue, like the orders data.
270 00:32:21.870 ⇒ 00:32:25.839 Awaish Kumar: should we move the channel field into the product, sales summary.
271 00:32:26.490 ⇒ 00:32:35.359 Robert Tseng: Yeah, let’s just use the same model. Like, I, I think, yeah, if we can. If we have that channel data, we should just put it into the same model, and that way
272 00:32:35.530 ⇒ 00:32:37.600 Robert Tseng: fewer things to maintain. Like I,
273 00:32:37.800 ⇒ 00:32:44.399 Robert Tseng: the Channel sales summary model is not. I don’t see it being used for anything else, so we should just put in the same one.
274 00:32:46.230 ⇒ 00:32:51.899 Awaish Kumar: Okay? So if we just if we trust, like how the like using the Utm
275 00:32:53.731 ⇒ 00:33:05.619 Awaish Kumar: source, we are categorizing it into my like, which, from what channel the order is coming from. If that data like, we trust that I can just move that channel field into our product sales summary table.
276 00:33:06.370 ⇒ 00:33:19.830 Robert Tseng: So I mean Utms. I think Mattesh will will be fine with it. I think we know that it’s, you know, like 20% of Utms are dropped so like there’s still like the the issue of Utms, not being reliable for everything.
277 00:33:19.930 ⇒ 00:33:25.490 Robert Tseng: We have some assumptions in place, for, like what happens if we don’t have utm sourcing, we do the distribution and everything.
278 00:33:26.056 ⇒ 00:33:33.560 Robert Tseng: I don’t think that the team has changed their like. We we haven’t updated like how they should be thinking about it. So
279 00:33:33.910 ⇒ 00:33:39.950 Robert Tseng: I think it was fine. But just keep in mind, like, yeah, we haven’t solved the Utm problem yet.
280 00:33:40.630 ⇒ 00:33:40.970 Awaish Kumar: Okay.
281 00:33:41.610 ⇒ 00:33:42.370 Robert Tseng: Yeah.
282 00:33:44.831 ⇒ 00:33:48.090 Annie Yu: So with the marketing dashboard.
283 00:33:48.610 ⇒ 00:34:03.460 Annie Yu: is it okay? If we still keep some of those using channel sales summary? And one thing that I think that model has both data from North Beam and the offline spreadsheet.
284 00:34:03.460 ⇒ 00:34:08.939 Robert Tseng: Yeah, yeah, it should be because North Queen doesn’t have everything.
285 00:34:09.829 ⇒ 00:34:11.529 Awaish Kumar: Yes, it has both exactly.
286 00:34:11.849 ⇒ 00:34:20.249 Awaish Kumar: Channel sales. Summaries, basically combines the data from both Northweam and the sheet we get from probe.
287 00:34:21.320 ⇒ 00:34:25.360 Annie Yu: So I should keep using that for the marketing dashboard right.
288 00:34:25.909 ⇒ 00:34:30.529 Robert Tseng: Yeah, I mean, when he’s doing channel level spend, I mean, as long as the revenue, like
289 00:34:31.169 ⇒ 00:34:37.050 Robert Tseng: the revenue should line up the spend, obviously won’t, because we don’t reflect that in product sales summary. But
290 00:34:37.719 ⇒ 00:34:44.219 Robert Tseng: yeah, like he’ll never look into orders from that view. He’ll he’ll only be looking at. He’ll only be comparing channels so
291 00:34:44.329 ⇒ 00:34:54.279 Robert Tseng: as long as like the highest level metric which is the revenue matches like our other model. I think it’s like, yeah, we should. Just, you can use. You can keep using it.
292 00:34:55.100 ⇒ 00:35:10.109 Annie Yu: Okay? So the revenue now doesn’t match. And I think that’s because we don’t exclude abandoned orders in that channel sales summary. So which is it okay? If we can exclude those orders to make sure they align.
293 00:35:10.880 ⇒ 00:35:17.390 Awaish Kumar: Yeah, you you can try that. Otherwise. Maybe you can. Maybe if you want to assign it to me, I can also investigate.
294 00:35:20.890 ⇒ 00:35:24.130 Annie Yu: Okay, yeah, I can. I can write like a simple ticket.
295 00:35:24.941 ⇒ 00:35:28.449 Annie Yu: And, Robert, I’ll add you to review.
296 00:35:28.900 ⇒ 00:35:36.760 Robert Tseng: Yeah, no, you don’t have to do it. It’s okay. We’ve been like, I, I’ve been working with the AI team to get like our ticket.
297 00:35:37.230 ⇒ 00:35:40.220 Robert Tseng: Yeah, like the the Transcript should pick it up.
298 00:35:40.600 ⇒ 00:35:41.390 Annie Yu: Yeah, I’ll just.
299 00:35:41.390 ⇒ 00:35:44.819 Robert Tseng: To minimize the time you guys spend on writing tickets. Yeah.
300 00:35:45.490 ⇒ 00:35:55.149 Robert Tseng: yeah. Oh, any, just anything that’s urgent that you can just always like, create something and add me. But then, generally, like anything that comes up in these calls like it should automatically create tickets.
301 00:35:55.340 ⇒ 00:35:56.090 Annie Yu: Okay.
302 00:35:56.360 ⇒ 00:35:57.080 Robert Tseng: Yeah.
303 00:35:58.654 ⇒ 00:35:59.170 Robert Tseng: Okay.
304 00:36:01.150 ⇒ 00:36:04.440 Robert Tseng: Cool. Alright. Well, thanks. Everyone. I gotta jump.
305 00:36:05.360 ⇒ 00:36:06.720 Awaish Kumar: Okay, yeah, thank you.