Meeting Title: [Eden] Daily Standup Date: 2025-04-09 Meeting participants: Aakash Tandel, Annie Yu, Mitesh Patel, Demilade Agboola, Robert Tseng, Josh, Rob, Sahana Asokan, Awaish Kumar
WEBVTT
1 00:02:19.430 ⇒ 00:02:20.320 Aakash Tandel: You are.
2 00:02:20.770 ⇒ 00:02:21.600 Robert Tseng: Hey! Gosh!
3 00:02:29.030 ⇒ 00:02:31.369 Aakash Tandel: Do you want me to run kind of through the standard stuff.
4 00:02:32.330 ⇒ 00:02:38.860 Robert Tseng: Yeah, let’s let’s just do the standard stuff unless I mean, Mattesh will probably be here, and he may like interrupt with some stuff. But.
5 00:02:38.860 ⇒ 00:02:39.380 Aakash Tandel: Yep.
6 00:02:40.110 ⇒ 00:02:40.790 Robert Tseng: Yeah.
7 00:02:41.240 ⇒ 00:02:45.879 Aakash Tandel: Sounds good, Rob, were you able to get into linear? I know you had issues yesterday.
8 00:02:47.917 ⇒ 00:02:52.660 rob: let me check again. I wasn’t last time I checked, but that was yesterday.
9 00:02:56.200 ⇒ 00:03:01.269 Aakash Tandel: I can. I can try removing you, and then re adding you, if that’s.
10 00:03:01.710 ⇒ 00:03:06.379 rob: Let me try again. I I don’t think I did, since you added access.
11 00:03:06.380 ⇒ 00:03:06.960 Aakash Tandel: Okay.
12 00:03:10.090 ⇒ 00:03:15.086 Aakash Tandel: yeah, it looks like there’s never a login. So we’ll see your user should pop up with
13 00:03:16.740 ⇒ 00:03:18.810 Aakash Tandel: Caller Jane Icon. Once it comes through.
14 00:03:23.380 ⇒ 00:03:27.829 rob: I’m just looking for the Channel man. I got so many. It’s ridiculous.
15 00:03:31.100 ⇒ 00:03:32.349 rob: Here we go.
16 00:03:46.620 ⇒ 00:03:47.820 rob: Yeah, there we go.
17 00:03:48.590 ⇒ 00:03:49.210 Aakash Tandel: Awesome.
18 00:03:51.610 ⇒ 00:03:55.590 Aakash Tandel: I think I tagged you in something yesterday. I don’t remember exactly what it was, but
19 00:04:10.340 ⇒ 00:04:14.270 Aakash Tandel: alright, folks, I’m sorry you’re saying something wrong.
20 00:04:14.780 ⇒ 00:04:16.440 rob: I was saying, this looks cool.
21 00:04:18.442 ⇒ 00:04:25.709 Aakash Tandel: Yeah, I mean, it’s nothing too crazy. It’s kind of your standard ticketing system. But yeah, it’s worked well for us so far.
22 00:04:28.100 ⇒ 00:04:34.699 Aakash Tandel: If new one, we’ll give folks another like 30 seconds, and then otherwise we can start with them. A lot of.
23 00:04:35.440 ⇒ 00:04:36.000 Robert Tseng: Yeah.
24 00:04:45.860 ⇒ 00:04:46.810 Aakash Tandel: Any.
25 00:04:49.777 ⇒ 00:04:53.852 Demilade Agboola: So this is basically done. I believe I mentioned this.
26 00:04:55.430 ⇒ 00:05:02.910 Demilade Agboola: I think. I sent Rob Robert the model that contains the data.
27 00:05:04.120 ⇒ 00:05:04.770 Aakash Tandel: Okay.
28 00:05:08.690 ⇒ 00:05:13.380 Aakash Tandel: Awesome. Alright. Which one of these Chippo basketors.
29 00:05:15.046 ⇒ 00:05:23.320 Demilade Agboola: So for the missing records to ship all this has like we’ve well, we’ve gotten the data loaded into the warehouse.
30 00:05:23.660 ⇒ 00:05:27.119 Demilade Agboola: It’s now part of our team shipments.
31 00:05:28.214 ⇒ 00:05:31.529 Demilade Agboola: The time I proved the Pr this morning.
32 00:05:32.120 ⇒ 00:05:39.560 Demilade Agboola: So I have matched it. And it’s part of our Pr, it’s part of our data available.
33 00:05:40.690 ⇒ 00:05:44.100 Demilade Agboola: So yeah, this is also basically done.
34 00:05:45.300 ⇒ 00:05:46.110 Robert Tseng: Great.
35 00:05:50.680 ⇒ 00:05:53.540 Aakash Tandel: That’s awesome. Thanks for getting that in there. That’s awesome.
36 00:05:54.830 ⇒ 00:05:57.020 Aakash Tandel: Cool for.
37 00:05:57.020 ⇒ 00:06:01.763 Robert Tseng: One there’s a i’ve added. A lot of ways is the one that I’ve been doing.
38 00:06:04.540 ⇒ 00:06:08.670 Robert Tseng: Hmm, I yeah.
39 00:06:08.670 ⇒ 00:06:11.840 Aakash Tandel: This was the thread with the person at Basque right.
40 00:06:12.340 ⇒ 00:06:13.310 Demilade Agboola: Yeah, Zack.
41 00:06:14.000 ⇒ 00:06:14.550 Aakash Tandel: Okay.
42 00:06:14.770 ⇒ 00:06:16.770 Robert Tseng: Actually, if you click into the sub issue.
43 00:06:19.960 ⇒ 00:06:27.060 Robert Tseng: Okay, you scroll down. So I wrote a lot of stuff here. But basically just kind of queries. And just got to context here.
44 00:06:29.020 ⇒ 00:06:37.913 Robert Tseng: yeah, I think there’s there’s a couple of outcomes that we need to get to a few more tech. I thought I created sub issues from the sub issue. But
45 00:06:41.810 ⇒ 00:06:48.429 Robert Tseng: Okay, yeah. So those are the 3 things that I’m gonna run through today to try to close that out. But basically, like.
46 00:06:52.620 ⇒ 00:06:58.840 Robert Tseng: yeah, I think. The order statuses. I guess if you
47 00:07:02.913 ⇒ 00:07:06.299 Robert Tseng: I’m trying to context switch. Okay? So
48 00:07:08.210 ⇒ 00:07:20.818 Robert Tseng: yeah, I know, Rob, you sent me some transaction ids from before. The these web hooks. And so I think you’re basically saying like, we won’t really be able to match these to subsequent orders anyways,
49 00:07:21.910 ⇒ 00:07:25.859 rob: It sounds like Zack is pretty confident that they’re
50 00:07:26.510 ⇒ 00:07:33.360 rob: that those pis are just being replaced by other pi’s. Yeah.
51 00:07:33.360 ⇒ 00:07:35.360 Robert Tseng: To make sure that that was the case.
52 00:07:35.360 ⇒ 00:07:46.229 rob: Yeah, what I was thinking is, I could join those I could look at the user ids and join and make sure there was like a completed order. Shortly thereafter, with a different
53 00:07:46.950 ⇒ 00:07:47.930 rob: pi.
54 00:07:48.390 ⇒ 00:07:55.999 Robert Tseng: Yeah, so yeah, I mean, dev, a lot of this would basically clear up like, why.
55 00:07:56.110 ⇒ 00:08:03.390 Robert Tseng: like, why, they’re missing created at dates. And what you were seeing. Yeah. And then, I think.
56 00:08:03.760 ⇒ 00:08:12.960 Robert Tseng: just anywhere, we’ve been really just measuring conversions via transaction is just not really reliable, because there’s yeah, there’s
57 00:08:13.230 ⇒ 00:08:16.283 Robert Tseng: at least 10% that are duplicate.
58 00:08:19.510 ⇒ 00:08:40.359 Robert Tseng: that have these replacement pis is just like replacement transactions. So it’s just like multiple transactions that may end up just leading to a single order. So there are multiple reasons for why transactions fail. I think, Zach, I just. It’s basically hypothesizing that maybe it’s payment failure. Maybe it’s
59 00:08:41.080 ⇒ 00:08:48.749 Robert Tseng: yeah, maybe just something on the front end doesn’t go through. And they have to submit a second time. So there’s like a 5% seems like
60 00:08:49.060 ⇒ 00:08:54.320 Robert Tseng: failure rate before success before success, which is
61 00:08:54.750 ⇒ 00:08:58.979 Robert Tseng: whatever that’s the margin of error that he thinks is normal. So I think.
62 00:08:59.280 ⇒ 00:09:01.700 Robert Tseng: Yeah, we’re just really trying to figure out
63 00:09:01.950 ⇒ 00:09:06.239 Robert Tseng: for the transactions that do not have create payments, because
64 00:09:06.720 ⇒ 00:09:23.170 Robert Tseng: that if if there’s a created payment, then that actually triggers the creation of an order for those, for those transactions that don’t have that are they actually being replaced by another transaction that ties to an order or not. That’s basically the last piece to investigating this.
65 00:09:23.800 ⇒ 00:09:28.169 Aakash Tandel: Okay, cool. And these tickets kinda outline all that stuff.
66 00:09:28.550 ⇒ 00:09:29.100 Robert Tseng: Yep.
67 00:09:29.440 ⇒ 00:09:31.460 Aakash Tandel: Okay. And these are all on you.
68 00:09:31.940 ⇒ 00:09:33.500 Robert Tseng: Yeah, I’ll I’m gonna do it.
69 00:09:33.500 ⇒ 00:09:39.090 Aakash Tandel: Okay? And then will there be follow up for Damalade to do stuff or unsure. Yet.
70 00:09:41.070 ⇒ 00:09:50.660 Demilade Agboola: It’s basically the outcome is will determine how I model the data to just accommodate all these like facts of how the data flows.
71 00:09:51.320 ⇒ 00:09:54.719 Demilade Agboola: And if there are any like data quality issues, I need to mitigate.
72 00:09:55.940 ⇒ 00:10:04.240 Robert Tseng: Yeah, yeah, I mean, I, I should be able to close this out within 30 min. So, yeah, it should be. I should be able to hand that to them. A lot of soon.
73 00:10:04.800 ⇒ 00:10:05.450 Aakash Tandel: Awesome.
74 00:10:05.640 ⇒ 00:10:14.610 Aakash Tandel: Okay, cool. That sounds good. Yeah. I was trying to follow the thread with Zach. I followed most of it, but it sounds like you were. You’re on top of it. So that’s good. Okay.
75 00:10:15.897 ⇒ 00:10:24.039 Aakash Tandel: back. Now here, Demo La day updating models for Exec dash bar chart. I think this is a bar chart stacked.
76 00:10:27.200 ⇒ 00:10:29.860 Sahana Asokan: I think that was the datum issue.
77 00:10:31.930 ⇒ 00:10:33.740 Aakash Tandel: So so what’s the issue here?
78 00:10:34.190 ⇒ 00:10:39.829 Sahana Asokan: I think we needed to make the data model a certain way to our chart stacked.
79 00:10:40.330 ⇒ 00:10:43.069 Aakash Tandel: Okay? Yeah. And Demo is is in progress.
80 00:10:44.321 ⇒ 00:10:51.559 Demilade Agboola: It will be today. I’ve been closing out the other tickets and so this is something I will
81 00:10:51.830 ⇒ 00:10:55.660 Demilade Agboola: put on my plate today, if they’re not the higher priorities.
82 00:10:56.910 ⇒ 00:11:03.480 Aakash Tandel: Okay, and then the wait. These are like the same thing. Right? No, these are different.
83 00:11:04.030 ⇒ 00:11:06.229 Demilade Agboola: Different dashboards. So retention.
84 00:11:06.230 ⇒ 00:11:16.201 Aakash Tandel: Same idea. Yep, same idea. Okay, yeah. That sounds good. Once you get through those kind of higher priority items definitely, these are next?
85 00:11:17.100 ⇒ 00:11:17.990 Aakash Tandel: okay?
86 00:11:18.300 ⇒ 00:11:19.810 Aakash Tandel: And then
87 00:11:20.170 ⇒ 00:11:35.899 Aakash Tandel: this alert for anything that impacts. I know you. And we’re talking about this adding an alert to any change that would impact Cac or Ltv more than 10%. Do you have a good idea of how to move forward on this one once you get there? Or is this still to be.
88 00:11:35.900 ⇒ 00:11:36.530 Demilade Agboola: Sure.
89 00:11:37.335 ⇒ 00:11:48.080 Demilade Agboola: So not that we have a pr already in prod. Oh, not not. We have a Pr which needs to be merch into prod. So the issue here is that
90 00:11:48.530 ⇒ 00:11:57.419 Demilade Agboola: there seems to be a disparity between staging data and broad data. So the test already failing before even hitting production.
91 00:11:57.680 ⇒ 00:12:08.720 Demilade Agboola: So that is what I will be looking at and just trying to fix. But the test has been created, it worked. It’s it’s able to figure out every difference between every single product.
92 00:12:09.214 ⇒ 00:12:12.050 Demilade Agboola: The Cac and Ltv between stitching and production. So
93 00:12:12.582 ⇒ 00:12:19.430 Demilade Agboola: any Pr going forward would automatically. If there’s a change. It would automatically flag it. That is what we want.
94 00:12:21.400 ⇒ 00:12:24.980 Aakash Tandel: Yep, that sounds like what we want. Okay, cool.
95 00:12:24.980 ⇒ 00:12:31.910 Robert Tseng: So like, if attack or Ltv changes by more than 10% on any product it’s gonna get, we’re gonna get the alert.
96 00:12:32.750 ⇒ 00:12:34.689 Demilade Agboola: Yeah, any product on any day. Yeah.
97 00:12:34.840 ⇒ 00:12:41.489 Robert Tseng: Okay, is that also base? Is that just based off our Pr changes or like, I don’t know. I imagine
98 00:12:41.650 ⇒ 00:12:51.559 Robert Tseng: some of their products that they don’t sell often like there’s they probably fluctuate more week to week. So like, even without touching anything we should, we could get alerts.
99 00:12:53.525 ⇒ 00:12:57.974 Demilade Agboola: No, so this only this is only an alert to
100 00:13:00.010 ⇒ 00:13:03.409 Demilade Agboola: to a change about to come into the system.
101 00:13:03.410 ⇒ 00:13:06.930 Robert Tseng: Okay, yeah, I think I prefer that. Yeah.
102 00:13:10.240 ⇒ 00:13:20.066 Aakash Tandel: Okay, that sounds good. Let’s go to a wish. You kind of sticking with the analytics engineers.
103 00:13:21.700 ⇒ 00:13:25.119 Aakash Tandel: this is an internal feedback. I think this is waiting on.
104 00:13:27.490 ⇒ 00:13:27.969 Robert Tseng: This is just
105 00:13:28.420 ⇒ 00:13:40.046 Robert Tseng: opted to use polytomic instead of segment, because it’s more reliable for this. We’re triggering manual syncs. But eventually we’re gonna have to switch over and pay for this connector. But
106 00:13:40.840 ⇒ 00:13:45.770 Aakash Tandel: Yeah, that makes sense. And I wrote the ticket for a recurring.
107 00:13:45.770 ⇒ 00:13:52.110 Awaish Kumar: Yeah, I I did that yesterday. So we can like the manual sync
108 00:13:53.020 ⇒ 00:13:57.920 Awaish Kumar: for this week. We can close this like, I need to recreate this ticket for the next week.
109 00:13:59.850 ⇒ 00:14:09.659 Aakash Tandel: Yup, I have it on a recurring, so this will pop up every week for you. But yeah, it sounds like the long term solution is to get that oops that connector
110 00:14:09.850 ⇒ 00:14:12.690 Aakash Tandel: built out. Okay, that sounds good.
111 00:14:14.220 ⇒ 00:14:16.791 Aakash Tandel: So should we leave this as
112 00:14:17.670 ⇒ 00:14:21.209 Aakash Tandel: I guess this is, I guess we need to send that to
113 00:14:21.320 ⇒ 00:14:25.500 Aakash Tandel: whoever needs to make a decision on that connector. But for now we can manually sync it.
114 00:14:25.810 ⇒ 00:14:26.380 Robert Tseng: Yeah.
115 00:14:26.690 ⇒ 00:14:32.560 Aakash Tandel: Cool. Okay, anything on here run query on campaigns are showing up for spend on gummies.
116 00:14:33.560 ⇒ 00:14:34.369 Robert Tseng: Yeah, that’s.
117 00:14:34.370 ⇒ 00:14:36.670 Awaish Kumar: Done. I managed the Pr. Yesterday
118 00:14:37.240 ⇒ 00:14:40.880 Awaish Kumar: for that campaign filter and share the data with.
119 00:14:42.170 ⇒ 00:14:49.810 Robert Tseng: Yeah. So basically, there’s still like, this is just like their marketing team. Their ad seem still.
120 00:14:50.280 ⇒ 00:15:03.401 Robert Tseng: Well, there’s 2 things I think one is making sure that. Yeah, the product names are actually showing up in the ad ad name and campaign name. So it’s the same thing we’ve been saying, and then the second is
121 00:15:04.190 ⇒ 00:15:06.050 Robert Tseng: We were.
122 00:15:06.230 ⇒ 00:15:12.570 Robert Tseng: I guess, which the pr that you that you that you put you know you, that you that you sent out was for
123 00:15:12.740 ⇒ 00:15:20.750 Robert Tseng: adjusting like the legacy logic of like I mean. So we’re only filtering off product names in those 3 categories like.
124 00:15:21.040 ⇒ 00:15:24.829 Robert Tseng: so like that’s. That’s the change that we need to make right.
125 00:15:26.945 ⇒ 00:15:41.579 Awaish Kumar: Yeah, like, so basically, we were, we already have this logic in place that we are looking for 1st from ad name. Then we look for product name from asset name, and then from the campaign name.
126 00:15:41.990 ⇒ 00:15:49.820 Awaish Kumar: And but like, there were some of the yeah. But some of the.
127 00:15:49.820 ⇒ 00:15:54.839 Robert Tseng: Oral summa was like being like weirdly categorized right? So.
128 00:15:55.080 ⇒ 00:16:04.399 Awaish Kumar: Yeah, yeah, that that because of the the old, we had a campaign filter on a campaign level. So I just removed it yesterday.
129 00:16:04.570 ⇒ 00:16:05.420 Robert Tseng: Okay. Yeah.
130 00:16:05.420 ⇒ 00:16:16.181 Mitesh Patel: So so, Robert. And is that the reason we were seeing spend for gummies, even though we thought there was. You know, we weren’t spending any money on gummies. Right?
131 00:16:16.540 ⇒ 00:16:24.959 Robert Tseng: Yeah, I mean, that’s part of it. But the the main part is is that like with uncategorized ad, spend, we just spread it out across like our total.
132 00:16:25.340 ⇒ 00:16:26.860 Robert Tseng: So, okay, that’s Michael.
133 00:16:26.860 ⇒ 00:16:33.499 Robert Tseng: People are still buying gummies. And so even if they’re not new customers, some of the ad spend is going to that.
134 00:16:33.860 ⇒ 00:16:35.130 Mitesh Patel: Got it? Okay? Alright.
135 00:16:35.130 ⇒ 00:16:50.090 Robert Tseng: So I mean, I kind of put the question on Stuart. It’s like, well, maybe we should be adjusting that logic so that we spread it off of new orders. But I don’t know. I think that’s that’s why it’s gonna show up on as ad spend for products that are that have no new customers.
136 00:16:50.690 ⇒ 00:16:54.032 Mitesh Patel: Okay, got it. Okay, that makes sense.
137 00:16:54.740 ⇒ 00:17:16.889 Mitesh Patel: also, I don’t know. So so these kinds of you know, they’re mostly small minor issues that we’re seeing. But that, you know, I just asked the team to start getting them to you, so you can help just do any tweaks that might be necessary, like, for example, on Saturday, April 5.th So after the the adjustments that you made on Friday.
138 00:17:17.089 ⇒ 00:17:25.430 Mitesh Patel: you know, we had that one error after that. The Ncaax. Look reasonable. Right? About 500 ish for a summer
139 00:17:25.989 ⇒ 00:17:33.579 Mitesh Patel: but if you look at the data just for Saturday, April 5, th you know, Ncac was at 800,
140 00:17:33.920 ⇒ 00:18:03.359 Mitesh Patel: and we know there’s gonna be day to day fluctuations. But that’s just seems to be out of balance. Just seems to be an extreme condition. So I I don’t. You know. I’ve asked the team that we’ll start sending these to you, but only to sort of validate, you know, if there’s anything missing that you know, that that we can add to, to make sure, because, you know, I’ve told the team that this is it. This is the data. This is reliable, and if there are small
141 00:18:03.370 ⇒ 00:18:10.830 Mitesh Patel: sort of discrepancies we might see I want them. I want to point them out to you in case it it helps us to uncover something
142 00:18:10.880 ⇒ 00:18:11.490 Mitesh Patel: or.
143 00:18:11.490 ⇒ 00:18:11.960 Robert Tseng: Yeah.
144 00:18:11.960 ⇒ 00:18:15.150 Mitesh Patel: Just better understand us. Better understand the data
145 00:18:15.510 ⇒ 00:18:20.200 Mitesh Patel: like you said this. You know the the uncategorized spend being distributed.
146 00:18:20.700 ⇒ 00:18:21.320 Robert Tseng: Yeah.
147 00:18:21.550 ⇒ 00:18:22.150 Mitesh Patel: Yeah.
148 00:18:24.100 ⇒ 00:18:33.380 Robert Tseng: Yeah, no, definitely keep flagging any like weird kind of signals. Feel like, yeah. Obviously, you guys would see some of these spikes that we don’t so.
149 00:18:33.380 ⇒ 00:18:34.409 Mitesh Patel: Yeah, yeah.
150 00:18:34.960 ⇒ 00:18:40.569 Robert Tseng: Yeah, I mean, on the weekend thing like I I don’t know. I mean, I don’t. Wanna
151 00:18:40.970 ⇒ 00:18:48.830 Robert Tseng: I think it would just be a a completely like complete hypothesis. But like, maybe like I mean, I don’t know if you can’t.
152 00:18:49.360 ⇒ 00:18:58.010 Robert Tseng: I don’t know if our ad spend is consistent day to day across the weeks, and then on weekends like does purchase? Or does purchase behavior change like I don’t. I don’t think we
153 00:18:58.250 ⇒ 00:19:01.330 Robert Tseng: like kind of have like a checklist of like what are some of the
154 00:19:01.440 ⇒ 00:19:07.259 Robert Tseng: like? The the week to week seasonalities that we would expect from the from a customer purchase behavior.
155 00:19:07.260 ⇒ 00:19:19.010 Mitesh Patel: Sure. Sure. So we’ll look into those those kinds of sort of trends and and what what the normal expectation would be, and only point out, you know what I think would be outliers.
156 00:19:19.010 ⇒ 00:19:19.590 Robert Tseng: Yeah.
157 00:19:19.950 ⇒ 00:19:20.610 Mitesh Patel: Alright!
158 00:19:20.870 ⇒ 00:19:21.520 Robert Tseng: Bye.
159 00:19:26.010 ⇒ 00:19:26.600 Aakash Tandel: Cool.
160 00:19:28.280 ⇒ 00:19:52.429 Aakash Tandel: Pulling this back to oasis stuff, I pulled some stuff that was in ready for development to pull into this cycle. So seems like you are free to pick these up. So. It looks like the highest priority ones is an audio api integration. So you know. Let us know if you have any questions on this, and Robert and I can hopefully help you move this guy into the Progress Board.
161 00:19:54.170 ⇒ 00:19:57.130 Awaish Kumar: Okay, sure, I will start looking at it today.
162 00:19:57.130 ⇒ 00:20:04.789 Robert Tseng: Yeah, I’ll connect with the wish on the zoonoty thing. I already kind of sent a list of questions to Danny, who’s the owner of the zoonoty on like.
163 00:20:05.040 ⇒ 00:20:14.069 Robert Tseng: yeah, just to make sure that I know what shape it needs to look like. I’ll I’ll post the questions I sent them in the ticket. But yeah.
164 00:20:16.440 ⇒ 00:20:20.259 Aakash Tandel: All right, let’s go to Sahana marketing dashboard.
165 00:20:20.260 ⇒ 00:20:30.180 Sahana Asokan: Yeah. So I sent the update in the slack channel with the updates. I the link to the published version is there
166 00:20:30.330 ⇒ 00:20:41.709 Sahana Asokan: as well, and I also posted some of the data discrepancies I was seeing between that excel sheet that Robert had sent me for ad spend and the
167 00:20:41.840 ⇒ 00:20:48.759 Sahana Asokan: ad spend the data that I was seeing in tableau from ad performance, and I attached the screenshots.
168 00:20:48.760 ⇒ 00:20:58.909 Robert Tseng: Yeah. So since Mattesh is on the call, I just, I’m just sharing the screen just so we can flash it. So I think the Ltv and Cac ratios are not crazy anymore. Obviously, Mer is still gonna change a bit as.
169 00:20:58.910 ⇒ 00:21:00.760 Mitesh Patel: Yeah, yeah, we add those other expenses.
170 00:21:01.108 ⇒ 00:21:19.600 Robert Tseng: Yeah, I think I I sent the spreadsheet to you. Basically, vitash like the takeaway is, I know that you already basically had to send something similar to incremental. I already watched on all the threads, watched the video whatever. So, yeah, for the 4 offline sources. Slash unpaid sources like.
171 00:21:20.910 ⇒ 00:21:23.600 Robert Tseng: I’d like to use the same sort of format. Yeah.
172 00:21:23.600 ⇒ 00:21:29.070 Robert Tseng: exactly. Just. I don’t need you to create it something new. So I tried to make it so you could just send the same thing.
173 00:21:29.070 ⇒ 00:21:32.840 Robert Tseng: Yeah, can we do product? Name filter? Here.
174 00:21:33.290 ⇒ 00:21:38.909 Mitesh Patel: So this. Ltv, that’s a yeah. Just use, Sema. Only highlight, Sema, only.
175 00:21:39.120 ⇒ 00:21:39.750 Robert Tseng: Okay.
176 00:21:42.200 ⇒ 00:21:43.410 Mitesh Patel: Injectable Sema.
177 00:21:44.080 ⇒ 00:21:45.710 Robert Tseng: Why am I not seeing it? Oh, there we go!
178 00:21:51.370 ⇒ 00:21:55.279 Mitesh Patel: Alright! That revenue. Ltv.
179 00:21:57.420 ⇒ 00:22:01.540 Mitesh Patel: Alright! Where’s the Ltv. Itself on this.
180 00:22:04.210 ⇒ 00:22:05.640 Robert Tseng: Bilva’s not on here. Right?
181 00:22:05.780 ⇒ 00:22:08.740 Robert Tseng: Yeah, it’s not. We didn’t add that to this.
182 00:22:08.920 ⇒ 00:22:09.260 Robert Tseng: Yeah.
183 00:22:09.890 ⇒ 00:22:10.440 Mitesh Patel: I mean.
184 00:22:10.440 ⇒ 00:22:12.840 Robert Tseng: If you want, we can bring it in otherwise.
185 00:22:12.840 ⇒ 00:22:33.220 Mitesh Patel: We should, because it helps us to. Just think about it. You know all in 1 1 chart, even. I know that wasn’t 1 of the the the kpis that I had provided. Alright, yeah, because I wanted to see it by product and the dashboard I was looking at. You, you know it works for Sema, but not for, like Sir Marlon.
186 00:22:34.800 ⇒ 00:22:44.379 Mitesh Patel: the one that’s live on tableau. So I just figured this. This is the one I’d go to going forward anyway. Alright, yeah, if you can pull Ltv by product. That’d be fantastic here.
187 00:22:44.680 ⇒ 00:22:56.160 Robert Tseng: Yeah, I mean this Moreland issue that you mentioned, I mean, Joanna called that out to me like a couple weeks ago. I thought we figured patched it already. So if you’re still seeing that error, we have to go back and look at it. But I thought we.
188 00:22:57.040 ⇒ 00:23:00.312 Mitesh Patel: Alright. I just hope I’m not looking at an old report.
189 00:23:01.250 ⇒ 00:23:05.230 Robert Tseng: Yeah, no. We tried to eliminate any duplicate links and stuff. So yeah.
190 00:23:05.680 ⇒ 00:23:08.319 Robert Tseng: if you, if you saw that recently, we’ll go and look at again.
191 00:23:08.530 ⇒ 00:23:10.350 Mitesh Patel: Yeah, it was just yesterday. So okay.
192 00:23:10.350 ⇒ 00:23:11.100 Robert Tseng: Okay?
193 00:23:11.560 ⇒ 00:23:30.370 Robert Tseng: But yeah, other than that, the breakout by channels is here. So we have the paid. Obviously, these numbers will tighten up once we have right. There’s a lot of channels here that are not in that list that you had sent me, so I don’t know if we want to do some sort of consolidation here. But yeah, I think that’s something we can.
194 00:23:31.450 ⇒ 00:23:38.007 Robert Tseng: Maybe it’s I mean, you’re only really looking at the biggest ones, anyway. So we can probably talk about that.
195 00:23:39.150 ⇒ 00:23:45.629 Robert Tseng: So yeah, I mean, there’s all the State is coming from North Beam, for now I think there’s kind of like a yeah, once we
196 00:23:46.990 ⇒ 00:23:53.613 Robert Tseng: I kind of explained to you like the order of the so still, still, by the end of the week, trying to get you the demo of the
197 00:23:54.000 ⇒ 00:23:55.504 Robert Tseng: kind of the the.
198 00:23:55.990 ⇒ 00:24:05.179 Robert Tseng: the attribution, but basically our own, our own attribution reporting with direct connectors. We’ll do it for all the big ones. First, st we’re not gonna get like
199 00:24:05.450 ⇒ 00:24:07.810 Robert Tseng: I don’t know, like pinterest or like
200 00:24:08.120 ⇒ 00:24:17.600 Robert Tseng: app. I mean app app loving. I don’t think is on on the list. So there’s there’s a way that we’ve broken it out for what we think we can get by end of this week, and then I think we’re gonna have to keep.
201 00:24:18.320 ⇒ 00:24:33.540 Mitesh Patel: Keep adding to it, okay, yeah, we’ll get what we can from the platform, some of them. Some of the expenses like, you know the like the offer. We’re gonna have to continue to get from the spreadsheet for a bit, and then the others will just add to them as we go. I think that’s great good plan.
202 00:24:33.930 ⇒ 00:24:43.619 Robert Tseng: Great. Yeah. So I guess question on the offer versus like other influencer, slash affiliate. So like Rob’s already doing some kind of like. It’s kind of
203 00:24:43.830 ⇒ 00:24:49.729 Robert Tseng: cobble together some spreadsheet that like gets the influencers
204 00:24:50.410 ⇒ 00:24:54.350 Robert Tseng: spend. And then we’re just gonna pull that directly into the data warehouse
205 00:24:54.560 ⇒ 00:25:03.363 Robert Tseng: on the offer like it feels like it’s structured. Similarly to the influencer and affiliate stuff. So it’s to me it’s just like a subset of that.
206 00:25:03.690 ⇒ 00:25:07.530 Robert Tseng: yeah, that’ll be fine. But yeah, the the we we.
207 00:25:07.670 ⇒ 00:25:09.210 Mitesh Patel: Yeah, we need to
208 00:25:10.820 ⇒ 00:25:21.890 Mitesh Patel: to have the offer separate from the list. You know, all the other influencers can just be grouped into one bucket. The offer is going to be a much larger, so would like that to be separate.
209 00:25:22.910 ⇒ 00:25:25.480 Robert Tseng: Okay, yeah, noted.
210 00:25:26.084 ⇒ 00:25:33.385 Robert Tseng: Okay, so yeah, I mean, we’re gonna just add the Ltv here. And then I think, as there’s some Qa that needs to happen.
211 00:25:34.120 ⇒ 00:25:42.950 Robert Tseng: yeah, as soon as we can get the mer, like kind of the budget ingested from you that we can update the Mer calculations here, but otherwise it’ll look something like this.
212 00:25:43.190 ⇒ 00:25:44.460 Mitesh Patel: Alright, fantastic.
213 00:25:44.810 ⇒ 00:25:45.440 Robert Tseng: Great
214 00:25:50.588 ⇒ 00:25:52.919 Robert Tseng: Akash, turn it back to you.
215 00:25:53.380 ⇒ 00:25:56.039 Aakash Tandel: Yep, pulling it up as we speak.
216 00:25:58.060 ⇒ 00:26:00.310 Aakash Tandel: And, Sahan, I just added the
217 00:26:00.520 ⇒ 00:26:03.020 Aakash Tandel: just what we talked about into this ticket, so.
218 00:26:03.502 ⇒ 00:26:06.690 Sahana Asokan: It was the Ltv correct?
219 00:26:06.690 ⇒ 00:26:08.900 Aakash Tandel: Yes, yep.
220 00:26:09.920 ⇒ 00:26:15.970 Sahana Asokan: Then we need to. Just can we just do some Qc. Please on the ad spend and some of the fields I included.
221 00:26:16.990 ⇒ 00:26:17.420 Aakash Tandel: Sure.
222 00:26:17.420 ⇒ 00:26:17.980 Robert Tseng: Yeah.
223 00:26:19.760 ⇒ 00:26:21.393 Sahana Asokan: I added all the examples.
224 00:26:21.720 ⇒ 00:26:23.699 Aakash Tandel: Oh, you! You saw that in slack right.
225 00:26:23.700 ⇒ 00:26:24.470 Robert Tseng: And slack.
226 00:26:24.470 ⇒ 00:26:27.029 Aakash Tandel: Okay, Robert, do you wanna quality control.
227 00:26:27.030 ⇒ 00:26:28.359 Robert Tseng: I’m I’m gonna look at it!
228 00:26:28.910 ⇒ 00:26:29.640 Aakash Tandel: Awesome.
229 00:26:30.620 ⇒ 00:26:38.549 Aakash Tandel: That sounds good, and I know we’re kind of pulling back from these until this marketing dash is finished. So no updates there, right, son.
230 00:26:40.755 ⇒ 00:26:41.290 Sahana Asokan: No.
231 00:26:43.940 ⇒ 00:26:45.055 Aakash Tandel: Alright
232 00:26:46.170 ⇒ 00:27:10.290 Robert Tseng: Although I will say that, like the since we’re now, we’re coming to the end of that order journey investigation. I think we can kind of go back into the customer journey. So nothing to do today as of today. But like, I’m as I’m gonna hand that off to finish the modeling there. I think we can update that journey dashboard. Probably I I don’t know if we’ll start it by tomorrow, but I think that’s that’s gonna get unblocked.
233 00:27:10.450 ⇒ 00:27:11.300 Sahana Asokan: Sounds good.
234 00:27:13.157 ⇒ 00:27:17.870 Aakash Tandel: Let me just click into this. Okay? So.
235 00:27:21.040 ⇒ 00:27:23.131 Aakash Tandel: okay, yeah, Robert, you and I can
236 00:27:23.990 ⇒ 00:27:31.699 Aakash Tandel: maybe flush this ticket out a little bit more with kind of what we need from this. Because I think, yeah, okay, that sounds good.
237 00:27:32.100 ⇒ 00:27:40.529 Josh : So so we are going to be done with the marketing dashboard the next day. There’s the
238 00:27:41.550 ⇒ 00:27:45.829 Josh : Robert you asked me to to escalate with Zack.
239 00:27:46.180 ⇒ 00:27:46.750 Robert Tseng: Yep.
240 00:27:47.870 ⇒ 00:27:53.580 Josh : And we have are pharmed ops dash.
241 00:27:55.630 ⇒ 00:27:59.460 Josh : what are the other core data sets that we’re still working on.
242 00:28:03.030 ⇒ 00:28:04.149 Robert Tseng: I think.
243 00:28:05.510 ⇒ 00:28:12.350 Robert Tseng: that’s pretty much it. I guess the retention dash hasn’t been widely used yet, so I think we’ll probably just have to, I mean, I’ll I can.
244 00:28:12.700 ⇒ 00:28:21.489 Robert Tseng: Maybe we’ll do it. We’ll walk through that on the next the next time, or on a separate call, so you can. I mean it’s it’s published it should be ready to use.
245 00:28:22.061 ⇒ 00:28:31.709 Robert Tseng: I know, Jim. A lot of you were like looking at a refresh thing, so I’ll make, I’ll make sure that that’s that’s good. But the the build there is done. So
246 00:28:33.450 ⇒ 00:28:43.680 Robert Tseng: yeah, I think those are the what we’re what we’ve considered to be quote unquote core. That’s that was what was has been talked about since you started joining these calls.
247 00:28:47.750 ⇒ 00:28:48.630 Josh : Okay.
248 00:28:48.630 ⇒ 00:28:52.130 Robert Tseng: We have a bunch of other stuff in the backlog that you know we wanna
249 00:28:52.290 ⇒ 00:28:55.489 Robert Tseng: kickoff. But you know we haven’t pulled that into cycle.
250 00:29:00.840 ⇒ 00:29:05.760 Josh : Okay? And then, what about the incremental.
251 00:29:07.990 ⇒ 00:29:24.819 Robert Tseng: Yeah. So incremental, I’m not really touching right now. Like, seem, they’re still in like a calibration phase, which is basically like they’re running into the same discrepancies that we do. I have created a ticket. I assigned this to Annie, but
252 00:29:24.950 ⇒ 00:29:36.669 Robert Tseng: I think what increment I just kind of projecting what I think incremental is gonna the same issues they’re gonna run into. And since we’re doing I mean, we just have to have like a next step for.
253 00:29:36.670 ⇒ 00:29:58.289 Robert Tseng: Okay? Assuming we already have all these direct integrations set up with these channels, what does that actually allow us to do? So I think we need to update our uncategorized ad spend like distribution methodology. And then there’s like a couple of things there. So I think that’s that’s where my head is at. It’s more work in parallel to incremental like I’m not.
254 00:29:58.400 ⇒ 00:30:04.639 Robert Tseng: I’m not like in the weeds jumping in there, but I’ve I’ve been keeping a pulse on where where the progress that they’re at.
255 00:30:09.710 ⇒ 00:30:10.939 Mitesh Patel: Yeah, I think.
256 00:30:11.840 ⇒ 00:30:17.570 Mitesh Patel: you know, incremental. If you can try to keep up with and surpass what they’re gonna be providing
257 00:30:17.730 ⇒ 00:30:23.299 Mitesh Patel: it would be fantastic. I think we also need to replace North beam, though.
258 00:30:24.120 ⇒ 00:30:26.010 Robert Tseng: Yeah, yeah, that’s that’s the goal.
259 00:30:26.210 ⇒ 00:30:32.120 Mitesh Patel: Yeah, I would. I I think that attribution, the Channel attribution and spend by channel is
260 00:30:33.930 ⇒ 00:30:39.719 Mitesh Patel: and this and how much we’re spending on North beam versus incremental. I would prioritize north beam.
261 00:30:40.400 ⇒ 00:30:41.190 Josh : Yeah.
262 00:30:41.700 ⇒ 00:30:46.951 Josh : if we can cause. Like, if we’re adding a new piece of software, I’m telling you guys, I want another one taken away.
263 00:30:48.020 ⇒ 00:30:51.317 Josh : Cause, like I’m done with the bills for
264 00:30:51.880 ⇒ 00:30:59.880 Josh : 10 pieces of software, the same thing. And then, Robert, I know that you’re gonna send me the emails attached to all the stuff today, right?
265 00:30:59.880 ⇒ 00:31:08.740 Robert Tseng: Yeah, I mean, I ran to a bit of a blocker I was pinging through a pass for stuff. But it’s just yeah. Anyway, that’s that’s something. I already started again yesterday.
266 00:31:09.160 ⇒ 00:31:15.019 Josh : Cool, because I’m still trying to be Doge, and I can’t be Doge unless I know that.
267 00:31:16.490 ⇒ 00:31:18.650 Robert Tseng: Yeah, I mean, there are just like.
268 00:31:18.960 ⇒ 00:31:21.180 Robert Tseng: I think, well, anyway, we can talk about offline.
269 00:31:21.180 ⇒ 00:31:22.740 Josh : Yeah, yeah.
270 00:31:24.910 ⇒ 00:31:30.939 Aakash Tandel: Yeah, that makes sense. You don’t wanna no point in having redundant softwares and paying for all that stuff. So that makes sense.
271 00:31:32.150 ⇒ 00:31:36.540 Aakash Tandel: Yeah, I know, I gotta justify your guys cost. So you know, you guys gotta help me.
272 00:31:36.850 ⇒ 00:31:38.850 Aakash Tandel: Yeah, make sense.
273 00:31:41.220 ⇒ 00:31:44.680 Aakash Tandel: Cool. Let me see.
274 00:31:45.720 ⇒ 00:31:51.739 Aakash Tandel: I know actually, if anyone has anything else to say, we can hop over to Annie.
275 00:31:55.650 ⇒ 00:32:01.670 Aakash Tandel: No, okay, yeah. And you want to give us an update. I don’t know what this blocker.
276 00:32:01.670 ⇒ 00:32:08.269 Robert Tseng: Same modeling that Dame a lot of needs to do to give her the ability to do stack bars.
277 00:32:08.270 ⇒ 00:32:11.572 Aakash Tandel: Yep. Okay, cool. So stack bars across the board.
278 00:32:13.340 ⇒ 00:32:16.990 Robert Tseng: I had that one yesterday we could have a more of a call. This is kind of the
279 00:32:17.200 ⇒ 00:32:21.900 Robert Tseng: my follow up on what I think we need to do in order to like out
280 00:32:22.550 ⇒ 00:32:25.390 Robert Tseng: like in parallel to what incremental is doing.
281 00:32:25.690 ⇒ 00:32:26.700 Aakash Tandel: Gotcha. Okay?
282 00:32:27.806 ⇒ 00:32:37.223 Aakash Tandel: Okay, that sounds good. And then I initially created this. This was the outcome from Danny’s mock up that we need to turn into a dash. So
283 00:32:37.670 ⇒ 00:32:47.840 Aakash Tandel: I was gonna add a color to that. So maybe this will be maybe Robert and Annie can sync up about this one sounds like this is like net new for Annie. So that’ll be good.
284 00:32:47.840 ⇒ 00:32:53.490 Annie Yu: Yeah, I will need time. With you, Robert, to get more understanding of it.
285 00:32:53.840 ⇒ 00:32:54.540 Robert Tseng: Yeah.
286 00:32:55.260 ⇒ 00:32:58.376 Aakash Tandel: Okay, yeah. And then I, yeah, if if you guys meet
287 00:32:58.920 ⇒ 00:33:06.000 Aakash Tandel: just fill this ticket out with whatever you you learn or I can also meet. If if I have time.
288 00:33:07.850 ⇒ 00:33:08.470 Robert Tseng: Okay.
289 00:33:09.838 ⇒ 00:33:15.701 Aakash Tandel: And actually, Robert, down there, you usually have a lot of stuff. Let’s see
290 00:33:16.820 ⇒ 00:33:19.969 Robert Tseng: No, I just went on a tear last yesterday, and just.
291 00:33:19.970 ⇒ 00:33:20.590 Aakash Tandel: Yeah.
292 00:33:21.910 ⇒ 00:33:22.710 Robert Tseng: Yeah.
293 00:33:22.710 ⇒ 00:33:25.579 Aakash Tandel: Looks like a lot of stuff is done. Okay, alright. And then these are.
294 00:33:25.580 ⇒ 00:33:31.615 Robert Tseng: Progress stuff is just the follow ups from that we talked about with Dave from the
295 00:33:32.390 ⇒ 00:33:36.780 Robert Tseng: like. The announce the further analysis that needs to be done from on the transactions.
296 00:33:36.780 ⇒ 00:33:42.650 Aakash Tandel: Panels. Yeah, okay, yep, that makes sense. Okay, does anyone have
297 00:33:42.790 ⇒ 00:33:45.502 Aakash Tandel: anything else? I can stop sharing for
298 00:33:46.960 ⇒ 00:33:49.960 Josh : No, just what else is needed from Zack.
299 00:33:50.170 ⇒ 00:33:51.910 Josh : Did you guys get what you need?
300 00:33:52.230 ⇒ 00:33:52.960 Josh : Yeah.
301 00:33:52.960 ⇒ 00:33:59.198 Robert Tseng: No, I mean he has his claims. I have to validate them. I think he thinks that there’s no issue.
302 00:34:00.120 ⇒ 00:34:07.790 Robert Tseng: I just you know. I I think I would just operate with a okay, we can trust what they say or people say, but we gotta verify it as well.
303 00:34:07.790 ⇒ 00:34:12.080 Josh : Verify. Account. Okay, okay, cool. Thanks. Guys.
304 00:34:12.380 ⇒ 00:34:13.210 Robert Tseng: Cool. Thank you.
305 00:34:13.219 ⇒ 00:34:15.669 Aakash Tandel: Alright, thank you. All. Have a good day bye.