Meeting Title: [Eden] Daily Standup Date: 2025-04-10 Meeting participants: Annie Yu, Demilade Agboola, Robert Tseng, Rob, Awaish Kumar
WEBVTT
1 00:02:21.290 ⇒ 00:02:22.860 rob: So Robert.
2 00:02:23.140 ⇒ 00:02:24.679 Robert Tseng: Hey, Rob! How’s it going.
3 00:02:25.350 ⇒ 00:02:26.430 rob: Good.
4 00:02:27.080 ⇒ 00:02:28.624 rob: Hey? Dude? So
5 00:02:29.250 ⇒ 00:02:39.060 rob: yeah, anyway, on that. He just wants to know why Larry Bluetides, not showing up yet. But pretty sure if we had sales it would be so.
6 00:02:39.060 ⇒ 00:02:41.890 Robert Tseng: Yeah, I mean, we have already been
7 00:02:42.240 ⇒ 00:02:44.669 Robert Tseng: like this issue kind of came up
8 00:02:44.960 ⇒ 00:02:55.169 Robert Tseng: a few weeks ago. We we were the ones that were kind of like. Oh, what’s this lyric glue type thing coming down the pipeline? So I thought it was already kind of taken care of.
9 00:02:56.020 ⇒ 00:02:59.240 rob: Well, maybe this particular screen.
10 00:03:00.070 ⇒ 00:03:03.170 rob: Yeah, it looks like we have 2 sales for it.
11 00:03:04.400 ⇒ 00:03:09.060 rob: Let me just send you the screen. He was curious about. Sorry I don’t want to overtake the meeting. I just.
12 00:03:09.060 ⇒ 00:03:09.829 Robert Tseng: No, no.
13 00:03:09.830 ⇒ 00:03:11.140 rob: It’s good.
14 00:03:11.140 ⇒ 00:03:13.949 Robert Tseng: To to get get it out of the way early. So.
15 00:03:18.670 ⇒ 00:03:21.660 rob: Okay, he said. It was added last week.
16 00:03:24.170 ⇒ 00:03:26.249 rob: I’m just saying it to you in slack.
17 00:03:39.180 ⇒ 00:03:44.230 rob: It’s kind of cut off, but I’m wondering if that’s the Sema kings. Oh, yeah.
18 00:03:44.380 ⇒ 00:03:47.380 rob: that’s what it is. It’s the Sema dashboard.
19 00:03:47.660 ⇒ 00:03:51.509 rob: I’m not sure why he wants lyrically tied on this, so I’ll ask him. But
20 00:03:52.220 ⇒ 00:03:55.919 rob: anyway, I don’t think that’s there’s anything.
21 00:03:55.920 ⇒ 00:03:59.089 Robert Tseng: Because we build a tableau dashboard for Sema, and he’s saying.
22 00:03:59.090 ⇒ 00:04:05.309 rob: Yeah, yeah, I think I think he’s wondering why it’s not on semi dashboard. I’m like, well, it’s not Sema, but.
23 00:04:05.675 ⇒ 00:04:10.420 Robert Tseng: But I mean, this is just the we I asked you to like
24 00:04:10.700 ⇒ 00:04:21.020 Robert Tseng: check. Why, lira glutide might not be showing up in the models like, I mean, I think that’s that’s the question. Maybe it’s just not being visualized in the right way for for Cutter to know where it is.
25 00:04:22.429 ⇒ 00:04:25.420 Demilade Agboola: What dashboard is he trying to get the information from.
26 00:04:26.410 ⇒ 00:04:35.650 rob: I think it’s the Sema kings because it says Sema, product orders and revenue dashboard. So I just think it’s because you guys are doing it right.
27 00:04:37.640 ⇒ 00:04:45.329 rob: but if he wants to add layer glutide to the sum of dashboard, he can right, but it does make sense that it’s not on there now. So.
28 00:04:45.470 ⇒ 00:04:46.100 Robert Tseng: Yeah.
29 00:04:46.470 ⇒ 00:04:50.029 rob: Okay, what’s he confirms that I’ll let you know Demo a.
30 00:04:51.830 ⇒ 00:04:57.989 Demilade Agboola: Yeah, that’s that’s an easy fix. But yeah, it’s currently not under. It’s just on the regular product revenue snapshots.
31 00:04:57.990 ⇒ 00:05:00.120 Robert Tseng: Okay, cool.
32 00:05:02.200 ⇒ 00:05:06.849 Robert Tseng: Yeah. Sometimes I mean, pretty much every day. I wake up and like, I would say, like.
33 00:05:07.510 ⇒ 00:05:15.530 Robert Tseng: there’s always a few questions that don’t actually need any action. They’re just people asking like, where stuff is. So
34 00:05:19.360 ⇒ 00:05:26.591 Robert Tseng: alright. Well, acaus and run isn’t gonna be joining today. So I’ll I’ll run, stand up. So we’ll just kind of talk through some things.
35 00:05:28.860 ⇒ 00:05:34.299 Robert Tseng: yeah, I guess since day, mulatta you’re on here first, st let me just kind of tee this up. So?
36 00:05:40.130 ⇒ 00:05:42.140 Robert Tseng: yeah. Where? Where do you want to start?
37 00:05:46.852 ⇒ 00:05:53.550 Demilade Agboola: So for these ones on the other status, I mean, we’re still waiting for.
38 00:05:53.970 ⇒ 00:05:57.569 Demilade Agboola: you know, the response from Zack from Basque.
39 00:05:58.110 ⇒ 00:06:00.280 Demilade Agboola: But, like, I said, if the
40 00:06:00.420 ⇒ 00:06:07.570 Demilade Agboola: they already exist as transaction ids and the the flow is there, all I just need to do will be to join it to our
41 00:06:08.380 ⇒ 00:06:19.289 Demilade Agboola: orders by transaction, Id and user Id, just so that we don’t have any duplicates just in case. And yeah, it exists in our in our orders table. So that is fine.
42 00:06:19.450 ⇒ 00:06:31.040 Robert Tseng: Yeah, I know you’re blocked on this because my investigation is not complete. I think that was probably a good chunk of where I spent my time yesterday, because I think it’s seems like it’s a bigger deal than I initially thought.
43 00:06:34.240 ⇒ 00:06:35.650 Robert Tseng: But yeah, I mean, I guess
44 00:06:35.880 ⇒ 00:06:47.179 Robert Tseng: just cause Rob is also on the call, and I also messaged him about it as well. I don’t know if you were able to look through my messages, Rob, but if I could summarize like what I basically found
45 00:06:49.620 ⇒ 00:06:55.530 rob: Yeah, I I looked through it. I just I still don’t understand. Yeah, go go through what you found.
46 00:06:56.570 ⇒ 00:06:59.139 Robert Tseng: Yeah. So basically, like
47 00:07:00.040 ⇒ 00:07:08.922 Robert Tseng: Zack had told me, had kind of like, had 2 explanations. Right? Sorry. There’s a lot of text here, but I, I like to write what I’m doing.
48 00:07:10.310 ⇒ 00:07:28.759 Robert Tseng: one is like, Okay, if a if a payment intent or transaction goes through and doesn’t actually lead to an order, then it’s it’s probably that they will get replaced by another. You know, a purchase interest, or whatever transaction that it will be tied to an order.
49 00:07:30.840 ⇒ 00:07:51.939 Robert Tseng: I don’t think that’s actually true in terms of like what I found. I mean, there are a few that do get this that do get replaced, but it’s like very few, and it’s not most of them. So I sent you like a table where I’m looking at what I call orphaned transactions or successful transactions that don’t actually lead to an order being created.
50 00:07:51.990 ⇒ 00:08:06.270 Robert Tseng: And like, I mean, in the screenshot I showed you, there’s like one. But if you expand the the window a bit more like it’s it’s just the point, is, it’s just very few that actually get replaced. And actually, most of them just end up, not
51 00:08:06.450 ⇒ 00:08:32.879 Robert Tseng: being like they end up not having an order in the next 7 days. So even if I expand that window to like okay, successful transaction. Look for any order within 30 days. Still, don’t get that many or like, there’s still a bunch of transactions that don’t end up having orders. So anyway, I I came up with like an order list or a customer list of
52 00:08:33.280 ⇒ 00:09:02.349 Robert Tseng: customers that could have been impacted by this because I was able to tie their transactions back to customers. And I sent it to the Member care team to kind of go and basically fish out. Hey? Are there any issues that these customers have been signaling to us over the past 2 weeks? So that’s kind of what I’m waiting back from to like, kind of get a bit more confidence in what I’m finding. But I think what I’ve looked into so far just kind of
53 00:09:02.460 ⇒ 00:09:08.069 Robert Tseng: contradicts what Zach was saying that to us. So that’s that’s basically where I left off.
54 00:09:09.670 ⇒ 00:09:23.250 rob: It’s good to figure this out because we have some influencers, or they were affiliates. And they were like, Hey, I’m seeing successful because we use the data layer for those guys. They’re like, we’re seeing successful charges. We’re not getting paid for them.
55 00:09:23.480 ⇒ 00:09:30.299 rob: So I’m glad you you did that cause. Dude, if we’re losing, were you saying it’s up to 15% of the orders.
56 00:09:31.362 ⇒ 00:09:33.790 Robert Tseng: Yeah, I mean it, it could be up to 15%. So.
57 00:09:33.790 ⇒ 00:09:36.379 rob: Jeez that’d be huge man. So.
58 00:09:36.380 ⇒ 00:09:46.749 Robert Tseng: I I think it’s probably not 15% like I believe the it’s probably somewhere between 5 and 10. But it’s like, I think you know, just trying to give a range of like what the magnitude is, you know.
59 00:09:46.750 ⇒ 00:09:51.179 rob: That’s still millions of dollars that’s crazy. That’s awesome.
60 00:09:51.740 ⇒ 00:10:01.559 Robert Tseng: So, yeah, I think that’s the main thing that I’m continuing to just push on like, I’m trying to get to the bottom of this hopefully today. But I think that’s
61 00:10:03.930 ⇒ 00:10:10.997 Robert Tseng: them. A lot of that’s why I haven’t, I guess, really handed off the logic to you to go and and actually model that in
62 00:10:11.460 ⇒ 00:10:13.390 Robert Tseng: that said, you know, I think
63 00:10:14.860 ⇒ 00:10:35.840 Robert Tseng: I I actually now that I’m as I’m talking out loud like I don’t think we’re actually blocked here like you could actually work on this, because I think I have an idea of what the what the and we know how it should go. There are a few edge cases that there are some edge cases that I’m investigating. But we should know, like the progression of the order statuses already. So I actually don’t think you’re blocked on this ticket.
64 00:10:36.670 ⇒ 00:10:39.170 Robert Tseng: So kind of I’m gonna pull it back into in progress.
65 00:10:41.060 ⇒ 00:10:53.199 Robert Tseng: Is that clear to you like, I mean, I I can. It’s basically like we have all these. We have these different timestamps like. We know what order they should go in. So we should be able to put it into the model.
66 00:10:54.995 ⇒ 00:10:59.020 Demilade Agboola: Yeah. So do you want this in the fact transaction model? Right?
67 00:11:00.112 ⇒ 00:11:05.009 Robert Tseng: I mean, I kind of want you to tell me like, what’s where’s the best place to put it, you know. Ultimately we’re
68 00:11:05.570 ⇒ 00:11:13.489 Robert Tseng: is it going to be? In fact, orders? Is it going to be? In fact, transactions like, you know, you’re you’re kind of designing like what? What that should look like.
69 00:11:14.590 ⇒ 00:11:25.630 Demilade Agboola: Yeah. So I I think I’ll put in 5 transactions. And then, we’re gonna have to create a like when we’re going to do the transaction journey more marketing.
70 00:11:25.630 ⇒ 00:11:26.070 Robert Tseng: Yeah.
71 00:11:26.338 ⇒ 00:11:39.221 Demilade Agboola: We’ll then create a model specifically for that such that we can be able to. It can be dynamic enough to be able to track what’s happening. But we can also filter by like date and by product. So we can kind of have an idea of
72 00:11:39.720 ⇒ 00:11:46.199 Demilade Agboola: what is happening by different products on different days, for the like, in terms of conversion.
73 00:11:46.970 ⇒ 00:12:03.359 Robert Tseng: Yeah. So actually, I mean, the naming of this ticket is a bit misleading because the web hook data is really just transaction status. It’s not really order status. So. But so I guess it’s it’s really there’s 2 asks here, I think, like the new web hook is. Maybe I need to split this up into 2 tickets. But
74 00:12:03.920 ⇒ 00:12:10.409 Robert Tseng: like, I’ll just write in the description, for now, like one, it’s like new web hook.
75 00:12:13.490 ⇒ 00:12:28.990 Robert Tseng: Our transaction statuses that or show transaction journey before an order is created.
76 00:12:29.140 ⇒ 00:12:33.679 Robert Tseng: and then we also need the like after order is created.
77 00:12:35.470 ⇒ 00:12:36.270 Robert Tseng: What
78 00:12:36.380 ⇒ 00:13:01.650 Robert Tseng: are the different stages to the order journey like. And I mean, if you want, I’m gonna I can rewrite this ticket. And I’m gonna add a lot more context. Because when I write tickets. It’s more like this. But yeah, I guess this is kind of how we start. We just started off with something that was a bit more naive, but I think we have more nuance to it now. So yeah, you tell me, like, what what do you like?
79 00:13:01.970 ⇒ 00:13:13.000 Robert Tseng: I feel like we have everything for 2 like the second one. For sure, this one was what you were kind of waiting on. But I think we can actually move forward with this. So how? How do you want to proceed.
80 00:13:15.940 ⇒ 00:13:23.949 Demilade Agboola: yes, I think at this point, yeah, we will add this information to the fact transactions. Just basically. So we can have some context.
81 00:13:24.713 ⇒ 00:13:30.619 Demilade Agboola: for each transaction, like what the journey is on, how they got to that stage.
82 00:13:31.372 ⇒ 00:13:38.159 Demilade Agboola: And then eventually, we will be able to use that to create like a summarized
83 00:13:38.792 ⇒ 00:13:43.660 Demilade Agboola: other journey model where we can kind of map everything together.
84 00:13:44.137 ⇒ 00:13:46.150 Demilade Agboola: So we can have the transaction flow
85 00:13:46.931 ⇒ 00:13:57.770 Demilade Agboola: and potentially over time. Maybe even add the shipment flow as well. So we can in one place, kind of see how long things happened in the entire flow.
86 00:13:57.910 ⇒ 00:13:59.149 Robert Tseng: That’s exactly yeah.
87 00:13:59.150 ⇒ 00:14:12.098 Robert Tseng: Yeah. That’s exactly what I want to have at the end. So I want to work backwards from there like you can. Maybe we need to like plan this out a bit more clear, like, I don’t want this like, yeah. So
88 00:14:13.000 ⇒ 00:14:19.080 Robert Tseng: I guess I’m gonna work through this kind of you know, you know, end state is to
89 00:14:19.650 ⇒ 00:14:26.909 Robert Tseng: have a single journey like whatever like conduct shipment journey
90 00:14:28.440 ⇒ 00:14:32.980 Robert Tseng: model that it’s like from the moment.
91 00:14:33.140 ⇒ 00:14:36.950 Robert Tseng: like from payment, all like all the way
92 00:14:37.750 ⇒ 00:14:52.050 Robert Tseng: through delivery, right? Like that’s that’s that’s the main model we need. So like we, I know we have a couple of intermediary things to get there, but I think I want a clear like roadmap from you on like, where? Like what? What that looks like, so we could break that out.
93 00:14:54.530 ⇒ 00:15:00.277 Demilade Agboola: Yeah, so I will. I will update this ticket with the table, the different models we’re going to
94 00:15:00.700 ⇒ 00:15:12.260 Demilade Agboola: we’re gonna add and create but for now we already have the flow of the transaction. So it’s just about like we’ll add to transactions, and then I will break down further.
95 00:15:12.823 ⇒ 00:15:22.169 Demilade Agboola: What we’re waiting for. What else could be blocking this ticket and the final table? That we will get that will power. The dashboards.
96 00:15:23.150 ⇒ 00:15:35.480 Robert Tseng: Okay, cool. I’m gonna rewrite this a bit with you. And then I mean, after after this call, and then we’ll I mean, I’ll probably split this up into a couple of sub issues. But I mean, yeah, let’s let’s kinda keep pushing on that one.
97 00:15:37.830 ⇒ 00:15:42.729 Robert Tseng: Any any movement on these models. Just it’s the stack bar chart.
98 00:15:46.800 ⇒ 00:15:58.010 Demilade Agboola: So these ones are, I should push a pr within the hour. And then, once the pr is pushed on merge, it should become available to the entire
99 00:16:00.250 ⇒ 00:16:01.369 Demilade Agboola: Entire team.
100 00:16:01.660 ⇒ 00:16:02.230 Robert Tseng: Great.
101 00:16:03.870 ⇒ 00:16:10.849 Robert Tseng: okay, that sounds good. Anything else that I’m missing. I know we have a couple of things I haven’t pulled into cycle yet, but
102 00:16:13.230 ⇒ 00:16:17.680 Demilade Agboola: I mean nothing like on like the anything. Is the retention dashboard?
103 00:16:18.443 ⇒ 00:16:27.370 Demilade Agboola: Issues. We figured that out. So now we had to republish. It was not published properly, so it had to be republished. That is done.
104 00:16:28.111 ⇒ 00:16:34.289 Demilade Agboola: So now the extracts are refreshing, and like today, we had like
105 00:16:34.480 ⇒ 00:16:38.279 Demilade Agboola: runs on every single extract. So that’s
106 00:16:39.700 ⇒ 00:16:47.110 Demilade Agboola: that’s good news. The only the only other thing I would want to take anything left for me to do is push out before the weekends. I want to push out the call
107 00:16:47.380 ⇒ 00:16:50.350 Demilade Agboola: smart models on tests for the call marts.
108 00:16:51.220 ⇒ 00:16:57.659 Demilade Agboola: So my focus is the sales match. I wish is handling some other models, but I will just push that out. Yeah.
109 00:16:57.940 ⇒ 00:16:59.009 Robert Tseng: Tag me on this one.
110 00:17:00.425 ⇒ 00:17:05.300 Demilade Agboola: So the alert, the alert is still in the alert, is still ending.
111 00:17:05.650 ⇒ 00:17:06.020 Robert Tseng: Okay.
112 00:17:06.020 ⇒ 00:17:13.270 Demilade Agboola: But yes, it is part of it. So think of it like a sub issue of the general, pushing out all the core. Sales. Yeah.
113 00:17:22.680 ⇒ 00:17:25.050 Robert Tseng: Okay, I’ll just add that to you.
114 00:17:25.802 ⇒ 00:17:29.240 Robert Tseng: Okay, do you? I mean, sounds like you got.
115 00:17:29.370 ⇒ 00:17:33.722 Robert Tseng: Seems like you’re pretty full. So I think for that, at least for the rest of the week. So I think
116 00:17:34.090 ⇒ 00:17:35.880 Robert Tseng: won’t add anything to your play right now.
117 00:17:37.360 ⇒ 00:17:38.400 Demilade Agboola: Yeah, sounds good.
118 00:17:38.400 ⇒ 00:17:42.499 Robert Tseng: Okay, let’s go to, I guess.
119 00:17:42.920 ⇒ 00:17:44.210 Robert Tseng: A wish.
120 00:17:47.120 ⇒ 00:17:54.240 Robert Tseng: Alright. Anything that you want to update on.
121 00:17:55.390 ⇒ 00:18:06.069 Robert Tseng: I I know that you were working with Sahana on trying to fix the marketing models. I I’m sorry I don’t. I just didn’t know how to jump in to support that. I got. Understand, she’s just using the wrong model.
122 00:18:08.360 ⇒ 00:18:27.540 Awaish Kumar: Yeah, actually, she was using factor performance, which is basically modeling the north beam data only. And whatever is coming from Northweam, we just put it there. So there’s also very fields like Cac. But that is coming from north beam and and not the one we calculate like using product sales summary.
123 00:18:27.710 ⇒ 00:18:38.640 Awaish Kumar: So there was a difference like in the data depending on the model. So yeah, in the fact at performance, our channel product and the span
124 00:18:38.790 ⇒ 00:18:46.780 Awaish Kumar: fields are exact, the correct ones, but other ones which are coming from Northeam we are not sure like how they calculated.
125 00:18:49.420 ⇒ 00:18:57.140 Robert Tseng: Okay? Like, I mean, I just see that that message is sitting there. So like, how do I support? What’s what’s the action here?
126 00:18:59.802 ⇒ 00:19:04.344 Awaish Kumar: Yeah, I like, and I’m not sure like how
127 00:19:05.520 ⇒ 00:19:08.190 Awaish Kumar: the Channel level Cac is being used.
128 00:19:08.520 ⇒ 00:19:13.979 Awaish Kumar: So I think we need to join this data with
129 00:19:14.300 ⇒ 00:19:22.039 Awaish Kumar: sales data to get the Cac value. Like, if we want the similar cac value as in product sales summary by transaction, we need
130 00:19:22.450 ⇒ 00:19:25.617 Awaish Kumar: to join it with sales data. But
131 00:19:26.760 ⇒ 00:19:30.909 Awaish Kumar: the the thing which I want to clarify here is the ad spend which we
132 00:19:31.210 ⇒ 00:19:39.789 Awaish Kumar: get is basically we can get by channel. But the sales data we we don’t have any way to associate our orders
133 00:19:40.050 ⇒ 00:19:44.159 Awaish Kumar: with the Channel like the Amazon ads or Tiktok ads. So how.
134 00:19:44.160 ⇒ 00:19:48.000 Robert Tseng: Yeah, I I see what you mean. We have tracked by product in product sales, summary.
135 00:19:48.000 ⇒ 00:19:48.520 Awaish Kumar: Yeah.
136 00:19:48.520 ⇒ 00:19:51.166 Robert Tseng: Session, but we don’t have Cac by channel and
137 00:19:51.460 ⇒ 00:19:51.970 Awaish Kumar: Yes.
138 00:19:51.970 ⇒ 00:19:52.530 Robert Tseng: Yeah.
139 00:19:56.467 ⇒ 00:20:10.000 Awaish Kumar: And the other one about the Zenote api integration. So I did. I did that integration we don’t have this connector in 5, 10, or the polytomic. But I we had this connector in the I find it in the portable. I I
140 00:20:10.543 ⇒ 00:20:25.120 Awaish Kumar: establish the connection, but I’m only getting one table called centers, and it only has 2 rows. I’m not sure if that is expected or like. If there’s any place I can verify that that the data we are getting is the correct one.
141 00:20:27.520 ⇒ 00:20:32.046 Robert Tseng: Yeah, so actually, I want, I want us to use corral and not
142 00:20:33.160 ⇒ 00:20:33.990 Awaish Kumar: Okay.
143 00:20:33.990 ⇒ 00:20:39.770 Robert Tseng: So actually, I’ll yeah, I’ll I think.
144 00:20:40.690 ⇒ 00:20:47.420 Robert Tseng: Yep. I set up the crowd yesterday so we can. I can plug. We can plug into Noti, and then also, like some other stuff there so.
145 00:20:48.230 ⇒ 00:20:51.340 Robert Tseng: and then want you to go in there to take a look.
146 00:20:53.430 ⇒ 00:20:54.479 Awaish Kumar: Okay. Thank you.
147 00:21:00.920 ⇒ 00:21:03.850 Robert Tseng: Okay, so I’m actually going to pull this.
148 00:21:03.850 ⇒ 00:21:04.360 Awaish Kumar: Interesting.
149 00:21:04.360 ⇒ 00:21:09.400 Robert Tseng: Progress and escalated because I need to look. And then.
150 00:21:10.060 ⇒ 00:21:16.410 Awaish Kumar: But yeah, these tickets. I don’t saw any description, so I’m not sure like what is expected here.
151 00:21:16.410 ⇒ 00:21:18.249 Robert Tseng: Yeah, I think
152 00:21:21.540 ⇒ 00:21:29.882 Robert Tseng: so, Rob, if you’re still there, just quick question. I know you were doing some influencer like slash affiliate data ingestion.
153 00:21:31.510 ⇒ 00:21:36.570 Robert Tseng: yeah, kind of what’s your progress on there? And I guess
154 00:21:36.720 ⇒ 00:21:46.319 Robert Tseng: we were asked to do the mount or whatever mountain, and and they offer, but just wanted to kind of prioritize like which one to to bring in to the models first.st
155 00:21:46.760 ⇒ 00:22:12.699 rob: Yeah, this is I think a little bit different, because those are both kind of platforms, right? But these are the one off influencers. And so I’ve got only from their Monday board. I’ve got the details of the influencer, and they just gave me a Google sheet showing payouts. And so I’m just adding that bring it into big query. And then, yeah, you guys can add it in your model. I should have that done.
156 00:22:12.830 ⇒ 00:22:17.380 rob: I’m gonna be out of town tomorrow, actually, but I’ll have it done Monday.
157 00:22:18.070 ⇒ 00:22:18.790 Robert Tseng: Okay.
158 00:22:18.960 ⇒ 00:22:22.710 rob: And to that point, yeah, I won’t be able to stand up tomorrow because of that. But.
159 00:22:22.710 ⇒ 00:22:23.799 Robert Tseng: Yeah, all good.
160 00:22:25.195 ⇒ 00:22:27.010 Robert Tseng: We’ll have big
161 00:22:30.930 ⇒ 00:22:33.439 Robert Tseng: boy. I already wrote this, so I’ll.
162 00:22:34.810 ⇒ 00:22:40.019 rob: Yeah, so this is everything except except Mntm. And the offer.
163 00:22:40.240 ⇒ 00:22:40.790 Robert Tseng: Yep.
164 00:22:41.520 ⇒ 00:22:44.490 rob: Okay, and I need to jump. But.
165 00:22:44.880 ⇒ 00:22:45.689 Robert Tseng: Yeah. Yeah. No worries.
166 00:22:45.690 ⇒ 00:22:46.819 rob: Alright. Thanks. Guys.
167 00:22:46.940 ⇒ 00:22:47.590 Robert Tseng: Thanks.
168 00:22:47.590 ⇒ 00:22:48.150 rob: Yeah.
169 00:22:50.040 ⇒ 00:22:52.498 Robert Tseng: Alright. So we’re actually blocked.
170 00:23:03.950 ⇒ 00:23:10.620 Robert Tseng: oh, you’re good there and then this one.
171 00:23:11.790 ⇒ 00:23:14.179 Robert Tseng: yeah, I think this is just like a
172 00:23:18.320 ⇒ 00:23:23.869 Robert Tseng: this is a little bit more kind of discovery to be done here like I I built this. Wait what?
173 00:23:32.860 ⇒ 00:23:36.860 Robert Tseng: okay, I’ll I think I’ll I’ll probably lump this, and maybe we’ll talk
174 00:23:38.900 ⇒ 00:23:44.060 Robert Tseng: when I when I walk you through the the corral. I’ll also talk about this later, so we don’t have to talk about it now.
175 00:23:46.220 ⇒ 00:23:46.615 Awaish Kumar: Okay.
176 00:23:47.530 ⇒ 00:23:48.060 Robert Tseng: Yeah.
177 00:23:48.230 ⇒ 00:23:51.300 Robert Tseng: Okay. Yeah. I know.
178 00:23:51.520 ⇒ 00:23:59.579 Robert Tseng: Annie, you’re kind of looking at some stuff, any yeah, any questions or things that you want to talk through from things that you were looking into.
179 00:23:59.990 ⇒ 00:24:11.419 Annie Yu: Yeah. So yesterday I had time to kind of go through what’s been done to prove that relationship between spin and daily product revenue. And I
180 00:24:11.840 ⇒ 00:24:12.690 Annie Yu: I’m just
181 00:24:13.980 ⇒ 00:24:33.859 Annie Yu: from the high level. I suspect we could run a multiple regression model to estimate the marginal effect of each channel. But I do have. A question is, if you can point me to a place where I can see what kind of data we would be having access to without those platforms.
182 00:24:34.820 ⇒ 00:24:38.819 Annie Yu: So if we don’t use north beam or incremental, what kind of
183 00:24:38.990 ⇒ 00:24:45.553 Annie Yu: data we will have visibility to tie back to each channel, say, like clicks or
184 00:24:48.320 ⇒ 00:24:53.720 Robert Tseng: Yeah, I think
185 00:24:53.850 ⇒ 00:25:03.290 Robert Tseng: so. I think what would be helpful here is you. So you know, the so product sales. Summary by transaction is the main model that we maintain to
186 00:25:04.075 ⇒ 00:25:15.109 Robert Tseng: like kind of display marketing metrics to but I guess it’s at the it’s modeled to be at the product level. If you log into north theme, you see things at the campaign level.
187 00:25:16.235 ⇒ 00:25:26.309 Robert Tseng: There’s probably a couple of intermediary tables before that. Maybe a wish could help you answer those questions. On what other? Your base like, what data do we get?
188 00:25:27.070 ⇒ 00:25:46.549 Robert Tseng: I guess we don’t get any data directly from sources because we are pulling any ad spend data directly from north Beam. We’re going to cut north beam out soon, and we’re going to go direct with those sources. So if anything, we’ll be able to get more than what North beam is giving us. So I know clicks and impressions and stuff. We don’t get.
189 00:25:46.720 ⇒ 00:25:53.910 Robert Tseng: The only thing that we really take in that matters, I think, is ad spend and campaign, name and product name.
190 00:25:55.550 ⇒ 00:26:10.499 Robert Tseng: So campaign. Yeah, campaign product details. Like ad spend on it like daily ad spend. That’s pretty much all we get from the marketing platforms. Actually, even product name is more because we tie it from our orders. So
191 00:26:10.700 ⇒ 00:26:31.480 Robert Tseng: yeah, so on the marketing platforms, we just get ad spend and campaign names. And then from like our other core data sets, we have, like, you know, obviously, orders and product names there. So that’s pretty much all we’ve been using. I don’t know what you would want to use in your regression model, but it I guess
192 00:26:31.620 ⇒ 00:26:53.300 Robert Tseng: it seems like incremental is only just using those things as well. And you know they have. They’re layering in a few different models to go in to to run like it’s basically, I mean, I think it’s they’re also doing like a lift study as well to to measure incrementality. But that’s kind of my understanding of what what we have and what we can do.
193 00:26:55.690 ⇒ 00:26:58.779 Annie Yu: Got it. Okay? So I yeah, I think where I’m
194 00:27:00.306 ⇒ 00:27:06.420 Annie Yu: I’ll need to educate myself. More is than with that. How do we know
195 00:27:07.763 ⇒ 00:27:12.479 Annie Yu: the marginal marginal effect by each platform
196 00:27:14.600 ⇒ 00:27:21.509 Annie Yu: cause. I think right now, at least, with the efforts we’ve done. It’s more like a overall
197 00:27:23.230 ⇒ 00:27:29.719 Annie Yu: campaign spend and the revenue. But then, if we have to kind of
198 00:27:30.590 ⇒ 00:27:36.239 Annie Yu: split that by channel, I think that’s where I I will need more time to think through.
199 00:27:36.910 ⇒ 00:27:45.749 Robert Tseng: Yeah, so kind of related to what wishes saying, like, we don’t have, we don’t have this data broken out by channel yet. So
200 00:27:45.980 ⇒ 00:27:53.900 Robert Tseng: I actually don’t really know how Sahana pulled that data in. Then, if we don’t have like, she has like a Cac by channel, like
201 00:27:54.300 ⇒ 00:28:00.286 Robert Tseng: calculations, I haven’t looked into that model yet. I need to like better understand it myself. But,
202 00:28:01.530 ⇒ 00:28:12.410 Robert Tseng: It’s definitely not pulling right, I guess. Wish. Can you confirm that that what? What she was using, in fact, add performance. That’s just pulling straight from north beam, right? But we don’t actually calculate it.
203 00:28:13.353 ⇒ 00:28:14.240 Awaish Kumar: Yes, yes.
204 00:28:14.691 ⇒ 00:28:22.409 Awaish Kumar: just getting all the fields from Northeas, and it also have fields like Cac Ros. But we are not sure how they calculate it.
205 00:28:23.940 ⇒ 00:28:31.819 Robert Tseng: Yeah. So I guess, Annie, to answer your question. Yeah, once Northeas out of the picture, we’re not going to even have anything there. But even now.
206 00:28:32.000 ⇒ 00:28:41.659 Robert Tseng: Tac by Channel Roas by channel, like any channel level, like calculations. We don’t do them. We’ve just been taking them out of north Beam, so.
207 00:28:42.310 ⇒ 00:28:47.799 Annie Yu: So we we want to be able to get to that view without North being.
208 00:28:47.800 ⇒ 00:28:52.379 Robert Tseng: Yeah, I guess is, that’s, I think that’s 1 of the the parts to this.
209 00:28:57.430 ⇒ 00:29:05.119 Annie Yu: Got it, cause I think if if that’s the case, we I I would assume that we would have
210 00:29:06.500 ⇒ 00:29:10.180 Annie Yu: like clicks, or very like very like the.
211 00:29:12.820 ⇒ 00:29:19.129 Annie Yu: I think, clicks and impression or sessions from each source, right in the future.
212 00:29:19.130 ⇒ 00:29:30.139 Robert Tseng: Yeah, we will. But maybe you start by looking at the raw tables from north beam, like, I think, maybe like, we’ll just see what they have like. I don’t think they give us that level of granularity. But yeah.
213 00:29:30.540 ⇒ 00:29:33.550 Annie Yu: Okay. So for that table, what’s the name?
214 00:29:35.943 ⇒ 00:29:43.410 Annie Yu: I know you mentioned. I can look at product sales summary. And for the north beam data. What would the
215 00:29:43.550 ⇒ 00:29:46.340 Annie Yu: right data source? I should be looking at.
216 00:29:46.340 ⇒ 00:29:54.660 Robert Tseng: Yeah, which can you point? Kind of, yeah, can you point any towards like the raw tables from north beam that you use to build the product sales summary.
217 00:29:55.787 ⇒ 00:29:58.360 Awaish Kumar: Yeah, I will send you a message.
218 00:29:59.150 ⇒ 00:30:00.709 Awaish Kumar: Is that okay? Right?
219 00:30:00.850 ⇒ 00:30:03.600 Robert Tseng: Yeah, yeah, yeah. You don’t have to do it. Now, I’m just yeah.
220 00:30:06.840 ⇒ 00:30:07.740 Robert Tseng: okay.
221 00:30:08.580 ⇒ 00:30:10.390 Awaish Kumar: I will send any message to you.
222 00:30:10.590 ⇒ 00:30:12.230 Robert Tseng: Yeah, thanks.
223 00:30:13.040 ⇒ 00:30:20.050 Robert Tseng: Okay. So I know there’s a few other things on here. We’re coming up on time. So I just wanna kind of push kind of just give an overview.
224 00:30:20.380 ⇒ 00:30:27.010 Robert Tseng: So yeah, I think there’s a certain, I think.
225 00:30:27.790 ⇒ 00:30:43.619 Robert Tseng: dame, a lot of his hands full kind of with the stuff there, so wish I’ll connect with you. Shortly after this call. We’ll we’ll talk about some of this like this is the the Zanodi and the direct marketing integrations. And then, yeah, there’s a couple
226 00:30:44.360 ⇒ 00:30:57.319 Robert Tseng: channels that they want to add to the to the marketing models. So that’s kind of what Rob was saying earlier, like the offer, Vibe Mntn. So I need to like wrap my head around like
227 00:30:57.630 ⇒ 00:31:07.170 Robert Tseng: how we’re gonna prioritize that. I haven’t finished these tickets of scoping them out. So that’s definitely blocked by me. So
228 00:31:08.600 ⇒ 00:31:13.572 Robert Tseng: yeah, other than that, I think that’s pretty much it.
229 00:31:14.480 ⇒ 00:31:27.359 Robert Tseng: yeah. Mattesh wasn’t on this call. But I wanna try to get Sahana on block like I don’t really know how to help her on her dashboard. So I think that’s those are the areas that I’m gonna try to focus on today.
230 00:31:35.180 ⇒ 00:31:51.570 Robert Tseng: Cool? Any other questions. Just let me know, like, yeah, feel free to just call me whenever I know it’s kinda towards the end of the week we usually get into a scramble by by this time, and would prefer not to to do that tomorrow, especially since
231 00:31:51.700 ⇒ 00:31:54.669 Robert Tseng: the rest of the La team will be here, and we’ll
232 00:31:54.820 ⇒ 00:32:00.404 Robert Tseng: probably not be having a normal Friday. So yeah, I guess
233 00:32:01.150 ⇒ 00:32:04.330 Robert Tseng: I’ll talk to you most of you later.
234 00:32:05.260 ⇒ 00:32:06.360 Annie Yu: Thank you.
235 00:32:06.360 ⇒ 00:32:06.680 Robert Tseng: Yep.
236 00:32:06.680 ⇒ 00:32:08.039 Demilade Agboola: That’s good. Thank you.
237 00:32:08.040 ⇒ 00:32:08.420 Awaish Kumar: Right.
238 00:32:08.420 ⇒ 00:32:09.080 Demilade Agboola: Bye.