Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-06-11 Meeting participants: Awaish Kumar, Robert Tseng, Annie Yu, Mitesh Patel, Demilade Agboola
WEBVTT
1 00:01:15.820 ⇒ 00:01:16.960 Mitesh Patel: Hello!
2 00:01:20.350 ⇒ 00:01:21.070 Robert Tseng: Hey? Mitesh.
3 00:01:21.070 ⇒ 00:01:21.840 Mitesh Patel: How are you?
4 00:01:22.440 ⇒ 00:01:23.255 Robert Tseng: Good! How are you?
5 00:01:23.460 ⇒ 00:01:24.260 Mitesh Patel: Weird.
6 00:01:27.570 ⇒ 00:01:29.000 Mitesh Patel: Let me get my list.
7 00:01:33.450 ⇒ 00:01:38.129 Robert Tseng: Yeah, well, let’s talk about. I mean, I was expecting you tomorrow. But like today’s great, too. Let’s let’s let’s.
8 00:01:38.455 ⇒ 00:01:54.429 Mitesh Patel: Right after I said, I’ll join tomorrow. I’ll get back to you by tomorrow. But then I’m like, actually, I’ll just join today’s call because I was the the dashboard and have some initial thoughts and questions. So it’s might as well just get it going.
9 00:01:54.790 ⇒ 00:01:58.280 Robert Tseng: Great. Yeah, let’s let’s let’s talk about your stuff first.st
10 00:01:58.280 ⇒ 00:02:00.340 Mitesh Patel: Yeah. One. Sec. Let me.
11 00:02:04.010 ⇒ 00:02:24.520 Mitesh Patel: Yeah. I think when you were out, you know, I connected with Annie directly on this growth dashboard that she set up cause. I needed certain, you know, a custom date ranges, basically in the all of that data she was. She was really good and just did it super quick, and I had it within a few hours after discussing with her so
12 00:02:24.740 ⇒ 00:02:27.069 Mitesh Patel: oh, there’s Annie, thank you, Annie, for that.
13 00:02:27.340 ⇒ 00:02:37.630 Robert Tseng: Yeah, I mean, we’re kind of at this point now, where I feel like the foundations are there. It’s just kind of getting into a place where it meets your preferences. We can make these iterations super fast. We just wanna yeah. So.
14 00:02:37.630 ⇒ 00:02:38.620 Mitesh Patel: Yeah. Alright. Cool.
15 00:02:38.620 ⇒ 00:02:39.100 Robert Tseng: Yeah.
16 00:02:39.100 ⇒ 00:02:53.989 Mitesh Patel: So in that chat right after, I said, I’ll get back to you tomorrow by tomorrow. On it. I made a list of things that we can actually discuss today. So on this marketing, can I share my screen? Just some pointing at things? No, not yet. Can you make me let me present.
17 00:02:53.990 ⇒ 00:02:54.910 Robert Tseng: Yeah.
18 00:02:56.120 ⇒ 00:02:57.419 Mitesh Patel: I think this should work.
19 00:02:57.440 ⇒ 00:02:58.190 Robert Tseng: Yeah.
20 00:02:58.960 ⇒ 00:02:59.620 Mitesh Patel: Somehow.
21 00:03:02.490 ⇒ 00:03:04.220 Mitesh Patel: Alright. You’re seeing it right.
22 00:03:04.220 ⇒ 00:03:04.540 Robert Tseng: Yep.
23 00:03:04.860 ⇒ 00:03:11.422 Mitesh Patel: Okay. So a few questions comments, I’m gonna kinda go back and forth here.
24 00:03:12.520 ⇒ 00:03:14.395 Robert Tseng: Oh, you got a lot of slack windows.
25 00:03:15.400 ⇒ 00:03:16.809 Mitesh Patel: What are you seeing.
26 00:03:19.140 ⇒ 00:03:21.969 Mitesh Patel: Yeah, I see whole screen. Yeah.
27 00:03:21.970 ⇒ 00:03:27.337 Robert Tseng: No, you’re I wanted to just share the one dashboard. Yeah, I got a lot of stuff going on.
28 00:03:28.505 ⇒ 00:03:28.940 Robert Tseng: sir.
29 00:03:29.410 ⇒ 00:03:34.269 Mitesh Patel: I thought I picked a specific window. That’s really weird.
30 00:03:36.230 ⇒ 00:03:38.390 Robert Tseng: Yeah, sometimes that doesn’t work for me, either.
31 00:03:39.600 ⇒ 00:03:42.129 Mitesh Patel: Is that cool? You’re just seeing the dashboard now, right.
32 00:03:42.130 ⇒ 00:03:43.690 Robert Tseng: Yeah. Now, I just see the dashboard.
33 00:03:43.690 ⇒ 00:03:50.559 Mitesh Patel: Alright cool. Thanks. This should not change what you’re seeing.
34 00:03:53.050 ⇒ 00:03:53.960 Mitesh Patel: Alright
35 00:03:54.437 ⇒ 00:04:02.529 Mitesh Patel: this dashboard one of one of the change. What I need to do is report on these things like certain date ranges.
36 00:04:03.010 ⇒ 00:04:06.349 Mitesh Patel: Can we add a custom date range to this.
37 00:04:09.050 ⇒ 00:04:09.849 Robert Tseng: Yeah, we can.
38 00:04:09.960 ⇒ 00:04:13.909 Robert Tseng: Okay, so like, what give me an example of like what that would look like.
39 00:04:14.090 ⇒ 00:04:20.530 Mitesh Patel: So what to fill out that dashboard, or or that Google Sheet that you were seeing
40 00:04:20.870 ⇒ 00:04:23.839 Robert Tseng: I’m assuming. This is what you use to run your your team now, so I.
41 00:04:23.840 ⇒ 00:04:43.639 Mitesh Patel: Yeah, something like this. So I can see week over and share week over week trends. Right? I want what? I actually did. Sorry this this is the one you were you referred to. Right so. And he created that dashboard for us so I could go into set these custom date ranges and grab the data
42 00:04:43.940 ⇒ 00:04:51.389 Mitesh Patel: right? Because I want to see. I want to share with the Elt and with my internal team week over week trends.
43 00:04:51.960 ⇒ 00:04:52.680 Robert Tseng: Yeah.
44 00:04:52.680 ⇒ 00:04:56.429 Mitesh Patel: Right? That’s great, yeah. And and and so the record
45 00:04:56.570 ⇒ 00:05:09.639 Mitesh Patel: that was my 1st request to Annie, which, like I said she did. She she did really quick is but what I would really like to get to is, you know, can you just give me a week over week?
46 00:05:10.180 ⇒ 00:05:25.490 Mitesh Patel: Trend right? How? How like these groups? So so the same data that we have in the one dashboard for the custom date range, just give me week over week and and and the week, and that I’m reporting in this report is Sunday. Through Saturday.
47 00:05:25.930 ⇒ 00:05:26.590 Robert Tseng: Okay.
48 00:05:26.590 ⇒ 00:05:28.659 Mitesh Patel: So instead of me collecting the data.
49 00:05:28.830 ⇒ 00:05:43.199 Mitesh Patel: I think the custom date range is still helpful for me, when, if I want to look at something, you know. Compare different time periods, or whatever that’ll still be helpful. But if I can just get a week over week view of all of this data that would be fantastic.
50 00:05:43.740 ⇒ 00:05:52.249 Robert Tseng: Yeah, so that growth. Kpi dash, we just get, we pretty much just use this sheet as inspiration. We, we can create that weekly view like a weekly table pretty much. Yeah.
51 00:05:52.250 ⇒ 00:05:53.789 Mitesh Patel: Yeah, that was it on that.
52 00:05:54.060 ⇒ 00:05:54.550 Robert Tseng: Okay.
53 00:05:57.450 ⇒ 00:06:05.080 Mitesh Patel: Okay, down here, what source are we using for channel data?
54 00:06:05.290 ⇒ 00:06:11.010 Robert Tseng: So the paid stuff is north beam unpaid is Google sheets. And then customer I/O is just customer. I/O, yeah.
55 00:06:11.800 ⇒ 00:06:13.919 Mitesh Patel: Google sheets for unpaid.
56 00:06:13.920 ⇒ 00:06:18.255 Robert Tseng: Yeah, there’s like a Google sheet that you were using with.
57 00:06:20.260 ⇒ 00:06:24.379 Robert Tseng: I guess by incremental, yeah, we kind of.
58 00:06:24.380 ⇒ 00:06:24.829 Mitesh Patel: Also the same.
59 00:06:24.830 ⇒ 00:06:25.220 Robert Tseng: Source, yeah.
60 00:06:25.220 ⇒ 00:06:26.989 Mitesh Patel: That is not
61 00:06:27.150 ⇒ 00:06:37.920 Mitesh Patel: okay. So there’s there’s, there’s a bit of you said you’re using that sheet for unpaid. That sheet is paid. But offline spend, though, that that which we don’t have integrated.
62 00:06:37.920 ⇒ 00:06:40.179 Robert Tseng: Sorry. That’s what I meant. Offline. Yeah.
63 00:06:40.180 ⇒ 00:06:40.610 Mitesh Patel: Okay.
64 00:06:40.610 ⇒ 00:06:41.030 Robert Tseng: Offline.
65 00:06:41.030 ⇒ 00:06:44.640 Robert Tseng: Yeah, there’s a lot that’s unattributed.
66 00:06:46.760 ⇒ 00:06:50.209 Robert Tseng: Yeah, okay, yeah. I mean, we, let’s.
67 00:06:50.460 ⇒ 00:06:52.720 Robert Tseng: I guess that finds you. And yeah.
68 00:06:52.720 ⇒ 00:06:58.869 Mitesh Patel: Yeah, we gotta figure that out. The other thing that I noticed is paid versus unpaid.
69 00:06:59.372 ⇒ 00:07:05.109 Mitesh Patel: So if I look at unpaid channels like the offer is absolutely a paid channel.
70 00:07:06.120 ⇒ 00:07:07.010 Robert Tseng: Yep.
71 00:07:07.290 ⇒ 00:07:11.300 Mitesh Patel: So that needs to be shifted. Question about that I know.
72 00:07:11.650 ⇒ 00:07:21.700 Mitesh Patel: Couple of months ago we had this conversation about filtering out the offer revenue and spend.
73 00:07:22.010 ⇒ 00:07:22.345 Robert Tseng: Yep.
74 00:07:22.680 ⇒ 00:07:27.996 Mitesh Patel: I I wanna make sure we’re I don’t know. At some point we should have changed it if we haven’t already.
75 00:07:28.250 ⇒ 00:07:28.700 Robert Tseng: Okay.
76 00:07:28.700 ⇒ 00:07:35.799 Mitesh Patel: Since we’re getting the offer, spend through that offline, spend sheet.
77 00:07:36.430 ⇒ 00:07:39.000 Mitesh Patel: We should also include that revenue.
78 00:07:40.700 ⇒ 00:07:41.320 Robert Tseng: Yeah.
79 00:07:41.800 ⇒ 00:07:52.130 Mitesh Patel: Right. So in the total revenue, the offer, revenue and the offer spend should now be included in the paid channels.
80 00:07:52.720 ⇒ 00:07:53.480 Robert Tseng: Okay.
81 00:07:53.480 ⇒ 00:07:53.815 Mitesh Patel: Alright.
82 00:07:54.750 ⇒ 00:08:05.970 Mitesh Patel: is there? So so you know, we got this other. I guess it’s tiny. So it really doesn’t matter. But this we need to break down further into.
83 00:08:06.410 ⇒ 00:08:12.950 Mitesh Patel: You know, other organic. I guess this organic is SEO, like, email, is this customer I/O.
84 00:08:14.890 ⇒ 00:08:20.570 Robert Tseng: Yeah, I would probably assume that most of it is I I guess, oasis on a fall. So I mean, we have this on.
85 00:08:21.060 ⇒ 00:08:23.440 Robert Tseng: We. We have like offline.
86 00:08:24.000 ⇒ 00:08:36.149 Robert Tseng: We have okay, we have. We have paid unpaid. And then within pay, we have online, we have offline and then unpaid. I feel like we haven’t updated that model in some time. So like, yeah, moving the offer like anything like that, I just think that
87 00:08:36.640 ⇒ 00:08:50.410 Robert Tseng: we yeah, this, this is probably an area we need to update. So I don’t know if you have that tracked anywhere but we we should. We should connect, I mean, I think a way should connect with whoever on your team would would know would know this.
88 00:08:50.410 ⇒ 00:08:53.179 Mitesh Patel: So here’s the thing. I yeah.
89 00:08:53.480 ⇒ 00:08:53.950 Mitesh Patel: What?
90 00:08:54.720 ⇒ 00:08:59.869 Mitesh Patel: This channel attribution? Right? What we get from
91 00:09:00.500 ⇒ 00:09:17.019 Mitesh Patel: North Beam. And when I look in north beam. And again, the whole thing is when I want to. And I know we’re using north beam data so we can’t really get rid of North beam. But you know, in North Beam there are like 33 channels that we get data by.
92 00:09:17.120 ⇒ 00:09:30.859 Mitesh Patel: and we can do 1st touch or last touch. I am right now, so you don’t. You didn’t see it in in in these metrics, right? But in another.
93 00:09:31.860 ⇒ 00:09:37.349 Mitesh Patel: This is, I don’t know. We have 2 different ways, and the weeks are different. These are Friday through Thursday.
94 00:09:37.600 ⇒ 00:09:43.910 Mitesh Patel: It’s not because of my choice, but it’s a reporting sort of frequency. Why, we’re defining week
95 00:09:44.020 ⇒ 00:09:45.860 Mitesh Patel: weeks differently. Okay?
96 00:09:46.160 ⇒ 00:09:46.460 Mitesh Patel: Oh.
97 00:09:46.460 ⇒ 00:09:58.770 Mitesh Patel: but what I need to track and report on that is the free to paid ratio. So what that means is what is all the revenue that we’re getting from free channels
98 00:09:59.490 ⇒ 00:10:02.649 Mitesh Patel: versus the revenue we’re getting from paid channels.
99 00:10:02.970 ⇒ 00:10:03.530 Robert Tseng: Yeah.
100 00:10:03.530 ⇒ 00:10:08.820 Mitesh Patel: Okay? And that’s all I I did is, I export the data
101 00:10:09.210 ⇒ 00:10:12.459 Mitesh Patel: for total revenue right? This is from North Beam.
102 00:10:12.680 ⇒ 00:10:13.240 Robert Tseng: Yep.
103 00:10:14.007 ⇒ 00:10:23.820 Mitesh Patel: And I’m using the first.st Although I exported last touch. I’m I am using the 1st touch data in north beam. These 7 channels are free.
104 00:10:25.350 ⇒ 00:10:30.790 Mitesh Patel: the other 30, the the other whatever 26 are paid.
105 00:10:30.790 ⇒ 00:10:32.400 Robert Tseng: 6, yeah, yeah, okay.
106 00:10:33.182 ⇒ 00:10:37.890 Mitesh Patel: And and then what I’m just doing in here is just the ratio.
107 00:10:38.290 ⇒ 00:10:44.939 Mitesh Patel: and it it makes sense, because, you know, in the beginning of the month. This should be around 0 point 7 to point
108 00:10:45.060 ⇒ 00:11:11.720 Mitesh Patel: 7 5 in the beginning of the month, when we cut spend. But we’re still getting revenue. It made it makes sense, right that the data is directionally correct. And I just need one source of data, I don’t, you know, never going to be accurate. But that that attribution is really important. So and and the reason I I mentioned that is, when you you know, we can’t just use customer, the customer I/O report.
109 00:11:11.720 ⇒ 00:11:27.860 Mitesh Patel: We need that separately. But you know, we’ll call that platform data just like I look at platform data for Meta and platform data for adwords. But the total revenue has to come. It has to be unique, and either 1st touch or last touch attribution.
110 00:11:29.030 ⇒ 00:11:29.669 Robert Tseng: I see?
111 00:11:34.290 ⇒ 00:11:45.890 Robert Tseng: okay. So I mean, I just think I’m just gonna talk out loud here. So in this section. Yeah, we have the. We have a custom date range. We have paid versus unpaid sounds like we also need kind of just the
112 00:11:49.170 ⇒ 00:11:56.852 Robert Tseng: Well, we have to make sure that the the up, the 33 channels are allocated correctly. And then when we’re looking at the revenue
113 00:11:57.640 ⇒ 00:12:11.110 Robert Tseng: I mean, if you’re able to pull 1st touch versus last touch like in. We could probably add that filter here as well. So we could add that if you want to. But yeah, I think a couple of things that we need to clean up the unattributed spend. Gotta make sure that.
114 00:12:11.340 ⇒ 00:12:15.070 Robert Tseng: I mean, you will see you want to see Customer I/O, as like.
115 00:12:15.360 ⇒ 00:12:16.519 Mitesh Patel: That’s a separate dashboard.
116 00:12:16.520 ⇒ 00:12:24.049 Robert Tseng: That should be a separate. So with anything that’s customer, I/O should be taken out. Is that what you want? Is that what you’re saying it should just move into like a separate chart. Maybe we just.
117 00:12:24.050 ⇒ 00:12:25.589 Mitesh Patel: Yeah, custom. I/O.
118 00:12:25.590 ⇒ 00:12:26.010 Robert Tseng: And bypas.
119 00:12:26.010 ⇒ 00:12:32.180 Mitesh Patel: All of that should be completely separate. Because that is plat customer plat platform data customer, I/O platform data.
120 00:12:32.750 ⇒ 00:12:34.369 Robert Tseng: Got it. So yeah.
121 00:12:34.786 ⇒ 00:12:40.610 Mitesh Patel: If we add CIO data to here, we’re gonna get. We’re gonna count dupes.
122 00:12:40.610 ⇒ 00:12:44.679 Robert Tseng: Okay, anything else that’s considered. Platform.
123 00:12:45.990 ⇒ 00:12:49.159 Mitesh Patel: Not out of those as long as we use north beam.
124 00:12:49.730 ⇒ 00:12:50.450 Robert Tseng: We’re using Northeas.
125 00:12:50.450 ⇒ 00:12:54.359 Mitesh Patel: Set of data. It’s not it’s not platform data.
126 00:12:54.700 ⇒ 00:12:55.370 Robert Tseng: Okay?
127 00:12:55.510 ⇒ 00:13:02.329 Robert Tseng: And then with, like the incremental Google sheet, the one you know, we’re referencing. You’re saying that’s not the right source for.
128 00:13:02.330 ⇒ 00:13:04.099 Mitesh Patel: No, that is the right source.
129 00:13:04.100 ⇒ 00:13:04.420 Robert Tseng: Okay.
130 00:13:04.420 ⇒ 00:13:05.609 Mitesh Patel: For paid channels.
131 00:13:05.610 ⇒ 00:13:07.850 Robert Tseng: For? Yeah, for offline paid. Okay?
132 00:13:08.661 ⇒ 00:13:13.699 Robert Tseng: But yeah, we just seem to have gotten like the the unpaid side is kind of messy right now. So.
133 00:13:13.700 ⇒ 00:13:14.500 Mitesh Patel: Yeah, yeah.
134 00:13:14.500 ⇒ 00:13:15.110 Robert Tseng: Okay.
135 00:13:15.560 ⇒ 00:13:42.250 Mitesh Patel: And then let’s see, once we have that, you know, week over week, free to paid ratio is by whatever attribution will be just, you know, will be easy. And just double confirm that. I know we kind of went back and forth on it that the offer revenue and cause, you know, we were backing out the revenue from the offer out of paid channels, but now that we’re including its cost, it shouldn’t be backed out.
136 00:13:42.610 ⇒ 00:13:46.630 Robert Tseng: Yeah, it should be part of the rep. It should be shown in the revenue and in the in the.
137 00:13:46.630 ⇒ 00:13:47.520 Mitesh Patel: Costs. Yeah.
138 00:13:48.210 ⇒ 00:13:49.000 Robert Tseng: Okay.
139 00:13:50.115 ⇒ 00:13:56.329 Robert Tseng: okay. So I think that gives me enough clarity on, like, how we can clean up this section.
140 00:13:57.170 ⇒ 00:14:13.509 Robert Tseng: yeah, I’ll break it on to some like kind of some some tasks with the team like later on. Anything else, I guess, in terms, in terms of the new Ltv. The way that we’re viewing. Ltv, we want to kind of go, if you like that. If this is good, we want to replace the other one. Yeah.
141 00:14:13.510 ⇒ 00:14:28.350 Mitesh Patel: I like it, and it’s more meaningful. But really, I’m looking at. You know the the totals right? This is really helpful if I’m like, okay. But then it’s like, All right. Well, what is my, what can I rely on as my Ltv, right? Just because
142 00:14:28.780 ⇒ 00:14:41.760 Mitesh Patel: we’re different. And and I understand right, because based on the cohorts, but so so to me. You know that that what we had in the old, the other dashboard, which is just a Ltv.
143 00:14:41.760 ⇒ 00:14:42.609 Robert Tseng: That line. Yeah.
144 00:14:42.610 ⇒ 00:14:47.279 Mitesh Patel: Yeah, here’s the Ltv, here’s the Ncac. And here’s the ratio.
145 00:14:48.950 ⇒ 00:14:51.309 Robert Tseng: Yeah, you, you still want that.
146 00:14:51.310 ⇒ 00:15:11.129 Mitesh Patel: I? I think so, because how do I use this? Right? What the let’s go back to, how I use it, and and how I need to use it is I need to manage the the paid teams to say, Hey, we! Here’s our Ltv, so here’s what the blended Ncat can be.
147 00:15:11.210 ⇒ 00:15:25.719 Mitesh Patel: And and are you guys on target? Or are you, you know, above target for that like? I said, this is very useful for me to look at by cohort. But it doesn’t give me a number to use. Does that make sense.
148 00:15:25.720 ⇒ 00:15:27.210 Robert Tseng: Yeah, yeah.
149 00:15:28.820 ⇒ 00:15:40.459 Robert Tseng: yeah. Well, I mean, even that number that blended is blended across all time. I I don’t know. I think there’s different ways to blend it. You could blend it by waiting each month equally. You can. You know you could. I mean, like, it’s kind of like.
150 00:15:42.020 ⇒ 00:15:50.600 Robert Tseng: I mean, that’s where I see like having it broken out this way. I mean, we could always just we could roll up into whatever like number you want. But I guess, with the other one.
151 00:15:50.600 ⇒ 00:15:54.110 Mitesh Patel: That’s where that’s where I need your help. I don’t know how to roll it up.
152 00:15:54.466 ⇒ 00:15:59.220 Robert Tseng: I don’t know the best way to roll it up. So maybe you can just kind of think through it and discuss it.
153 00:15:59.220 ⇒ 00:15:59.970 Robert Tseng: Okay.
154 00:15:59.970 ⇒ 00:16:00.590 Mitesh Patel: Yeah.
155 00:16:01.150 ⇒ 00:16:05.850 Robert Tseng: Yeah, I think
156 00:16:06.190 ⇒ 00:16:10.589 Robert Tseng: I mean, I I have. I’ll have more thoughts later. But I think just given the
157 00:16:11.600 ⇒ 00:16:20.429 Robert Tseng: seasonality of the customer purchase cycle. I think we should factor that into how we do the roll up, because right like, if people are only making purchases.
158 00:16:20.885 ⇒ 00:16:29.719 Robert Tseng: I mean, I don’t know if there’s still the case. But last we looked into it right like twice a month, really around like the paycheck times. Then, obviously like your
159 00:16:30.160 ⇒ 00:16:41.530 Robert Tseng: I don’t know. This is a simple over over simplification, but your spend is gonna be less efficient right after they’ve are like, you know, the week after their their paycheck versus like the week of their paycheck, or something, or if you, if you know what I’m trying to say, so.
160 00:16:41.530 ⇒ 00:16:42.080 Mitesh Patel: Yeah, yeah.
161 00:16:42.080 ⇒ 00:16:51.690 Robert Tseng: I think, like the timing of that kind of should influence like how we do the roll up. So we just gotta like break down like, how we want. Yeah, kind of think about those.
162 00:16:52.090 ⇒ 00:16:58.435 Robert Tseng: our understanding of a customer purchase behavior. And then how we should reflect that in what we’re calling blended
163 00:16:58.770 ⇒ 00:17:26.149 Mitesh Patel: That would be that. That’s perfect. I’m I’m again. We don’t. You don’t have the answer, but you’re thinking through the right things right? And then, even with the seasonality when we had, you know, one quarter a year ago that worked for glp ones, but not so much for the newer products. Right? So it’s kind of like. Then I’m kind of just eyeballing it, saying, oh, we launched some Rollin injections like 9 months ago. So let me, you know. Let me just use that ltv, yeah, right? So
164 00:17:26.450 ⇒ 00:17:30.579 Mitesh Patel: anyway, that would we just help me figure that out.
165 00:17:31.010 ⇒ 00:17:31.570 Robert Tseng: Okay.
166 00:17:31.570 ⇒ 00:17:44.930 Mitesh Patel: Yeah, yeah, okay. I think we I gotta jump because I’m late to another call. But this is good by tomorrow I’ll have other thoughts as well in terms of you know the the dashboards.
167 00:17:45.430 ⇒ 00:17:51.369 Robert Tseng: Great. Yeah, no, thank you for this. This gives us clarity. We can. Yeah, we we can. We can make it work with this.
168 00:17:51.520 ⇒ 00:17:53.580 Mitesh Patel: Cool. Thank you. Yeah. Awesome.
169 00:17:53.960 ⇒ 00:17:54.520 Robert Tseng: Wow!
170 00:17:55.719 ⇒ 00:18:02.360 Robert Tseng: I think everyone else will stay on for a bit longer. I want to just go through the regular
171 00:18:05.040 ⇒ 00:18:06.300 Robert Tseng: so
172 00:18:07.590 ⇒ 00:18:18.530 Robert Tseng: yeah, couple of things. If you could go over this in your own time, I think I shared it in the slack channel. Just make sure everyone has looked through it. I’ll just blow through this real quick. But this is.
173 00:18:18.610 ⇒ 00:18:39.780 Robert Tseng: I’m going to be maintaining this on a bi-weekly basis. I push for 30% increase on our contract. I’m trying to get it signed by Friday. So I think there’s a bit more skin in the game, like, I know that our team has kind of adjusted to like having a bit more work on. And so we’re kind of reaching capacity if we need to negotiate hours and like how long you’re spending on the client, or whatever.
174 00:18:40.158 ⇒ 00:18:47.939 Robert Tseng: You know, we can talk about it. You can just DM me, and we can discuss it. I think right now, from what I’ve seen from the hours that you guys have logged back.
175 00:18:48.100 ⇒ 00:18:55.989 Robert Tseng: we’re still like where we were expecting to be. So I’m not worried about that so far. But you know, if there’s anything I I’m
176 00:18:56.110 ⇒ 00:19:01.069 Robert Tseng: I’m seeing it like a week or 2 weeks later than what we’re actually going through because of
177 00:19:01.250 ⇒ 00:19:06.569 Robert Tseng: like when I review allocations and stuff. So just just want to keep that in mind.
178 00:19:07.069 ⇒ 00:19:20.949 Robert Tseng: Yeah, I think like the 2 objectives that we’re kind of be focusing on one is like, Yeah, the Emr migration. And so I know that we really just got started with this a wish you’re kind of. I’m like, you’re kind of the lead on this.
179 00:19:21.373 ⇒ 00:19:26.370 Robert Tseng: I’m trying to get you in the in the right conversations, and I’ll help you kind of.
180 00:19:27.350 ⇒ 00:19:38.270 Robert Tseng: I mean, I can build the strategy, Doc, and do do all that stuff to kind of push, push it, push it along but then the other piece is, we want to do more marketing activation. So
181 00:19:38.703 ⇒ 00:20:02.510 Robert Tseng: yeah, that’s like, you know, I I gave any a walkthrough of the mixed panel stuff. Yeah, with all of the we did a lot with transactional data, with like behavioral data. We want to make that we want to get that into a good place. And so yeah, I know that beyond that we we have a bunch of maintenance of existing reports and stuff like that where there’s small tweaks here and there.
182 00:20:02.660 ⇒ 00:20:08.721 Robert Tseng: I, personally don’t have a good pulse on how long that’s taking us to do so.
183 00:20:09.280 ⇒ 00:20:14.200 Robert Tseng: I have like an estimate, but I think I probably need like another week or 2 to kind of
184 00:20:14.390 ⇒ 00:20:19.209 Robert Tseng: see like what what that looks like for for us.
185 00:20:19.696 ⇒ 00:20:24.279 Robert Tseng: I think how that’s gonna be reflected here, I’m gonna do a better job of like
186 00:20:24.350 ⇒ 00:20:51.480 Robert Tseng: creating recurring tickets. I know that a wish I did that, for, like the Zendesk weekly refresh. But I’m trying to think through like all the different things that you guys do and then creating recurring tickets for it. And yeah, if I miss anything like I should have that done today. But if I miss anything, then please create your own ticket, and you can tag me. Or if you want to just create your own recurring ticket and tag yourself that way, we can just make sure that everything is ticketed.
187 00:20:51.914 ⇒ 00:21:00.749 Robert Tseng: I think up to this point we’ve been doing a lot of maintenance work without having any tickets. And so that’s my bad on not being as tight on there.
188 00:21:01.485 ⇒ 00:21:16.280 Robert Tseng: But yeah, I groom through everything this morning took me a while. And there’s a lot of stuff that’s in progress. I feel like we’ve been kind of stuck on a lot of things. So yeah, this looks full. A lot of is recurring maintenance stuff that’s not necessarily urgent. So I won’t talk about it.
189 00:21:16.360 ⇒ 00:21:42.749 Robert Tseng: But yeah, I think just like general themes. Yeah, I mean not to call out. You know, Dave, lie too much. I. But I mean, I think there’s I know you weren’t in stand up a couple of days, or whatever. So yeah, if you could just update wherever you can on the block blocked and in progress. I’ve tagged you in comments and things, but I think the general theme is product. Categorization is still kind of like this ongoing ambient, like thing that’s
190 00:21:43.370 ⇒ 00:22:10.089 Robert Tseng: continuously refining. But people are asking me different questions around the same thing all the time. So I think, basically from like categories to subcategories, to products, to variance to vial sizes, like making sure we have that really clear lineage mapped out. I think that’ll unblock a lot of these things that we’re working on. So I know you’re kind of working on it in bits and pieces already. But if you could just
191 00:22:10.290 ⇒ 00:22:23.200 Robert Tseng: yeah, just kind of help me to understand where we’re at from that overall. I think that would. I think that’s like at least 5 tickets here. So I I think that’s that’s 1 that’s 1 big thing we’re we’re held up on.
192 00:22:24.730 ⇒ 00:22:30.290 Robert Tseng: Yeah, maybe I’ll just pause there, see if you kind of have any thoughts on that, so far.
193 00:22:31.243 ⇒ 00:22:39.139 Demilade Agboola: Quick question. Who will be the best person to come up with the categorizations of like the different product categories and subcategories? Rebecca.
194 00:22:40.000 ⇒ 00:22:43.510 Robert Tseng: Yeah, well, so I think Rebecca and Cutter together. So
195 00:22:44.260 ⇒ 00:22:50.009 Robert Tseng: yeah, we need, if we need to get them on a call together, we can do that. I know that Annie has asked Joanna.
196 00:22:50.140 ⇒ 00:23:01.689 Robert Tseng: you know, like Adam and Josh, like our message me separately, and they have their own ideas. But the the owners of that should be, you know, Cutter and and Rebecca. So
197 00:23:01.850 ⇒ 00:23:24.759 Robert Tseng: I would say cutter cares more about like categories and subcategories, and then Rebecca cares more about like, you know, breaking things down into variance and like product level like stuff. And then and the vial sizes specifically, because that’s what’s blocking me on the forecast. I haven’t been able to do that. So yeah, I think that’s kind of why I think we both. We need to have both of them there.
198 00:23:25.122 ⇒ 00:23:38.769 Robert Tseng: Yeah, if you need help, kind of setting up that conversation, I think it’s really just mapping out. This is what we have so far, you know, like these are how we assign different categories and make sure we just get the list from them fairly, so that we can go and finish that.
199 00:23:39.530 ⇒ 00:23:47.839 Demilade Agboola: Yeah. So basically, we’re stuck. The the reason why we’ve not made a lot of progress on it is because it doesn’t. Necessarily.
200 00:23:47.960 ⇒ 00:24:02.548 Demilade Agboola: it’s not like the data is available. And you know, we just are not using it. The data that is available has like data quality issues. But that being said. A quick fix is just. We know the standard product names like.
201 00:24:03.210 ⇒ 00:24:31.839 Demilade Agboola: if we know what those product names are, and we know that, like med kits. One can be categorized this way or not. Patches or any depaches can be categorized that way. We can just do that at the high level, and then apply that across every order that comes in. Like, we just distribute those product categories. And then we can sum up revenue that way. We can sum up ad spend that way based off our knowledge of these high level products. Names.
202 00:24:31.930 ⇒ 00:24:35.229 Demilade Agboola: But that obviously, is a workaround and
203 00:24:35.626 ⇒ 00:24:43.370 Demilade Agboola: the issue. The main issue is obviously from Basque, the fact that, like we don’t have that data coming in. So we’re just creating this workaround for that.
204 00:24:43.610 ⇒ 00:24:44.270 Robert Tseng: Yeah.
205 00:24:44.930 ⇒ 00:24:56.380 Robert Tseng: I I don’t know if we’re still using this. I don’t. I think some I see people using this. I think this is still, where this is, how the team thinks about categories and things. So
206 00:24:56.960 ⇒ 00:25:06.360 Robert Tseng: yeah, it kind of has, like all the different sections we need. Maybe quantity is kind of like ambiguous at this point. It’s not necessarily file size, like, I don’t really know, like, I think
207 00:25:06.610 ⇒ 00:25:14.190 Robert Tseng: so. Maybe this is, this would be the starting point, like I I mean, if you need me to assist with this like I would pretty much just put this in front of
208 00:25:15.770 ⇒ 00:25:28.609 Robert Tseng: Cutter and Rebecca, I’ll be like, Wow, we’re just we’re just gonna use these everything that we’re gonna just map everything to this. We’ll use the product name as like the you know, foundation. But then we’ll roll up to all of these categories.
209 00:25:28.730 ⇒ 00:25:34.300 Robert Tseng: But then we but it sounds like we still need like I think there was a ticket where you were trying to figure out like.
210 00:25:36.630 ⇒ 00:25:52.429 Robert Tseng: dose dosage like order, because you know whether it’s the second or 3rd dose sometimes like that changes the number of vials as well. So like I I don’t know if you were if you were still hung up on that on that piece. But like, yeah, I guess I’m just trying to get us to
211 00:25:53.020 ⇒ 00:25:54.152 Robert Tseng: have it all.
212 00:25:54.530 ⇒ 00:26:01.429 Demilade Agboola: Yeah, yeah, sure. So I I think we can close out like, we’ll probably want to close out this categorization before the weekend.
213 00:26:03.260 ⇒ 00:26:12.380 Demilade Agboola: I think that plus the I just sent a pr for the fix to
214 00:26:14.040 ⇒ 00:26:24.900 Demilade Agboola: the deduping. So those are like 2 easy wins. I think the the long, the one that might still like spill into next week will be the file size, and like being able to like
215 00:26:25.170 ⇒ 00:26:35.579 Demilade Agboola: map that like model that properly but those 2 ones, we should be able to do that. And I’m also trying to finish up the sequence sequence model this week as well. So those 3 ones
216 00:26:36.080 ⇒ 00:26:38.490 Demilade Agboola: we can churn them up, churn them out this week.
217 00:26:38.870 ⇒ 00:26:51.240 Robert Tseng: Okay, great and then, yeah, I mean, everything in progress is so, or for most part. So I think this is that pretty much covers everything there. Yeah. Wish
218 00:26:52.720 ⇒ 00:27:03.350 Robert Tseng: I owe you a bunch of tickets. I think it’s really just ticketing out the the Emr work. I I tag Utam to try to help work with you on like planning out what that looks like.
219 00:27:03.802 ⇒ 00:27:09.660 Robert Tseng: If that helps, I think just just cause. It feels like a massive project of things that are coming. And
220 00:27:09.890 ⇒ 00:27:14.319 Robert Tseng: I wanna be able to tell the team like what we planned out in terms of work there
221 00:27:14.830 ⇒ 00:27:26.420 Robert Tseng: and then everything that Mattesh just mentioned about making marketing, model changes, or whatever like. I think that’ll end up going to you. So I think that’s pretty much what you have coming.
222 00:27:26.420 ⇒ 00:27:27.180 Awaish Kumar: Your way.
223 00:27:28.490 ⇒ 00:27:31.679 Awaish Kumar: I just have an update on this type form data.
224 00:27:31.910 ⇒ 00:27:39.660 Awaish Kumar: So we actually, it was modeled by autumn. But I don’t like.
225 00:27:39.820 ⇒ 00:27:48.850 Awaish Kumar: I can’t find it anywhere like how we ingested it. So like, I tried checking segment and the polytomic.
226 00:27:48.950 ⇒ 00:27:54.240 Awaish Kumar: But yeah, that I don’t know. Cool like how we ingested this data to victory.
227 00:27:54.240 ⇒ 00:27:54.830 Robert Tseng: Data, sorry.
228 00:27:54.830 ⇒ 00:27:58.009 Awaish Kumar: It was a platform data.
229 00:27:58.840 ⇒ 00:28:00.170 Robert Tseng: Platform data.
230 00:28:00.610 ⇒ 00:28:01.680 Awaish Kumar: Type, phone type.
231 00:28:01.680 ⇒ 00:28:03.989 Robert Tseng: Oh, type, form. Type. 4. 0, okay. Okay.
232 00:28:04.200 ⇒ 00:28:07.840 Annie Yu: Yeah, that should be a ticket for me. If you’re trying to find that.
233 00:28:08.200 ⇒ 00:28:19.309 Robert Tseng: Oh, yeah. Well, yeah, I I saw that. Well, yes, because the type form data seems stale from February, like, I guess no one can find. Oh, yeah, it seems like we can’t find where that that data is coming from.
234 00:28:20.010 ⇒ 00:28:31.929 Annie Yu: But I actually checked earlier in bigquery. I see that we have the latest data the latest, the latest data we have. It’s actually today. So I’m not sure it’s actually up to date.
235 00:28:32.570 ⇒ 00:28:37.590 Robert Tseng: Okay, yeah, I mean, can we make sure?
236 00:28:38.060 ⇒ 00:28:38.690 Robert Tseng: Yeah.
237 00:28:38.690 ⇒ 00:28:42.520 Awaish Kumar: Actually any. Maybe you are looking at the fact tables?
238 00:28:42.950 ⇒ 00:28:43.330 Awaish Kumar: Yeah.
239 00:28:43.330 ⇒ 00:28:55.280 Awaish Kumar: Basically, we are running the Dbt models, and it creates the table every day. So it seems to you that it was created today. But underlying data which is coming from source has not updated since February.
240 00:28:55.790 ⇒ 00:28:57.579 Annie Yu: Okay, got it?
241 00:28:57.980 ⇒ 00:29:10.269 Robert Tseng: Well, somehow, it’s I guess, any your screenshot from this, like it’s from April 18.th But I guess, are you suggesting that this is not actually April 18, th and this is actually still. February 20, second.
242 00:29:11.510 ⇒ 00:29:15.508 Awaish Kumar: Yeah, like, if I see the forms data
243 00:29:16.170 ⇒ 00:29:25.320 Awaish Kumar: in the bigquery, we have a data set called type form inside of it. We have table if we just see the details and the last modified.
244 00:29:25.540 ⇒ 00:29:27.410 Awaish Kumar: Okay, it was
245 00:29:33.110 ⇒ 00:29:36.409 Awaish Kumar: okay. Now I see it is updated. I don’t know
246 00:29:37.180 ⇒ 00:29:41.699 Awaish Kumar: from where, but it. It seems that we, it data got updated.
247 00:29:42.810 ⇒ 00:29:45.700 Awaish Kumar: Okay, so I mean, this is the original look.
248 00:29:46.340 ⇒ 00:29:50.860 Robert Tseng: This is the last looker studio report. I think, that people are still using
249 00:29:53.410 ⇒ 00:29:59.820 Robert Tseng: So we’re just trying to bring this over to tableau. And if we don’t know if this data is fresh, then we gotta go figure out what that is. And if you
250 00:29:59.990 ⇒ 00:30:01.740 Robert Tseng: if yeah, if we could, just
251 00:30:01.890 ⇒ 00:30:14.499 Robert Tseng: I would. Yeah, I it seems like we’re not clear whether it is. But if you need me to like, figure it out, just let me know. But yeah, we’re we. We need to get off of off of Booker studio here. So
252 00:30:14.941 ⇒ 00:30:21.449 Robert Tseng: I mean, sounds like Annie, you. You understand the requirements on the visualization side. So we’re just making sure that we’re using the right data source here.
253 00:30:25.800 ⇒ 00:30:26.560 Robert Tseng: Yeah, so.
254 00:30:26.560 ⇒ 00:30:26.910 Awaish Kumar: Yeah, because.
255 00:30:26.910 ⇒ 00:30:31.290 Robert Tseng: If you could help unblock any there she can. She can finish this.
256 00:30:31.290 ⇒ 00:30:37.709 Awaish Kumar: Data seems to be fresh. I will just talk with them to figure out where it is coming from. Actually.
257 00:30:38.260 ⇒ 00:30:39.710 Robert Tseng: Okay, thanks.
258 00:30:41.809 ⇒ 00:30:46.930 Robert Tseng: Yeah. I know we’re at time. That was really kind of Pratesh kind of had some stuff there.
259 00:30:48.660 ⇒ 00:30:54.079 Robert Tseng: yeah. Any other questions. Yeah, I don’t think I have anything else that I will mention here for now.
260 00:30:55.244 ⇒ 00:31:07.889 Annie Yu: One question, Robert, about the gross Kpi marketing Kpi. I think I missed just like the very beginning part of it. So is the request like, we want the column. We want to have weekly column, and then
261 00:31:08.130 ⇒ 00:31:09.870 Annie Yu: Kps on the rows.
262 00:31:10.899 ⇒ 00:31:17.019 Robert Tseng: Yeah. So I’ll I’m gonna ticket it out like, right install. So there, there is like a.
263 00:31:17.130 ⇒ 00:31:30.799 Robert Tseng: he’s basically using your data and the custom data filters, date ranges you you gave him. And he’s manually getting weekly data and putting it into a summarized table and Google sheets. And so I think the ask is just to
264 00:31:30.960 ⇒ 00:31:35.066 Robert Tseng: do the weekly summarization for him, and just put it as like a tableau table.
265 00:31:35.600 ⇒ 00:31:42.110 Annie Yu: Got it, got it. But then, for it’s gonna be Sunday to Saturday, and for marketing, it’s gonna be Friday to Thursday.
266 00:31:42.110 ⇒ 00:31:47.344 Robert Tseng: Yeah, which is like so weird to me. But yeah, I think if you knew that already, then, okay.
267 00:31:47.910 ⇒ 00:31:54.380 Annie Yu: Okay, yeah, I, it’s it’s yeah, also help to have a ticket. Just so I don’t like, okay.
268 00:31:55.490 ⇒ 00:31:56.110 Robert Tseng: Yeah.
269 00:31:56.520 ⇒ 00:32:08.440 Robert Tseng: okay, cool. So that yeah, I think we have. Just we’re just trying to push through on some things here. So I’m not gonna add any more to this to this week. But yeah, I think everything should be up to date at this point.
270 00:32:08.560 ⇒ 00:32:15.774 Demilade Agboola: Good question. And yeah, can you stay on for like 3 more, 3 more minutes.
271 00:32:16.180 ⇒ 00:32:17.169 Annie Yu: Is that to me.
272 00:32:17.370 ⇒ 00:32:18.230 Demilade Agboola: Yes, yes.
273 00:32:18.230 ⇒ 00:32:19.309 Annie Yu: Yes, for sure.
274 00:32:19.310 ⇒ 00:32:20.650 Demilade Agboola: Alright, that’s all.
275 00:32:23.010 ⇒ 00:32:31.270 Robert Tseng: Okay, cool. Yeah. So that’s that’s that. I think I’ll I’ll just leave it there for now I’ll I’ll see you guys later.
276 00:32:31.840 ⇒ 00:32:34.779 Annie Yu: I’ll just leave the meeting, and I think it should stay on.
277 00:32:37.240 ⇒ 00:32:38.740 Robert Tseng: We’ll sign it to.
278 00:32:41.760 ⇒ 00:32:42.650 Annie Yu: Okay.
279 00:32:43.490 ⇒ 00:32:44.780 Demilade Agboola: Hey, Annie, how are you?
280 00:32:45.600 ⇒ 00:32:46.155 Annie Yu: Hi!
281 00:32:46.890 ⇒ 00:32:47.947 Annie Yu: How’s it going.
282 00:32:48.650 ⇒ 00:32:49.490 Demilade Agboola: Pretty good.
283 00:32:50.960 ⇒ 00:33:03.010 Demilade Agboola: So I wanted to know for the product category. Is there any particular model you want? Okay, you just actually responded, is the product, transaction and order. Summary. Alright, I will.
284 00:33:05.130 ⇒ 00:33:05.930 Demilade Agboola: I will.
285 00:33:05.930 ⇒ 00:33:13.940 Annie Yu: Mainly the products the first.st The product sales summary. That one is what we use for product drill down. And I just try to
286 00:33:14.651 ⇒ 00:33:19.740 Annie Yu: keep the numbers consistent. So, for, like that, one will definitely need it.
287 00:33:20.870 ⇒ 00:33:27.840 Demilade Agboola: Okay? Alright. Another question is for the what’s it called?
288 00:33:28.430 ⇒ 00:33:32.399 Demilade Agboola: The tableau reports that we made for Kota.
289 00:33:33.880 ⇒ 00:33:35.809 Annie Yu: The selected one.
290 00:33:35.810 ⇒ 00:33:39.460 Demilade Agboola: Get selected ones. Were there any changes to the filters.
291 00:33:42.648 ⇒ 00:33:46.559 Annie Yu: Let me double check to the filters.
292 00:33:46.820 ⇒ 00:33:53.500 Demilade Agboola: Because it appears like. For instance, there’s summer in the new product sales. And obviously summer is not a new product.
293 00:33:55.700 ⇒ 00:34:01.749 Demilade Agboola: so I was just wondering if cause I know josh just tagged me about that, and I was wondering if anything had changed.
294 00:34:04.010 ⇒ 00:34:10.669 Annie Yu: Don’t believe I did. But let me double check, because I want.
295 00:34:12.559 ⇒ 00:34:14.039 Demilade Agboola: Yeah.
296 00:34:14.040 ⇒ 00:34:19.440 Annie Yu: I think Sema is in the new product, though.
297 00:34:20.420 ⇒ 00:34:28.329 Demilade Agboola: Let me see, because I know I had to do a fix for the selected new products.
298 00:34:29.684 ⇒ 00:34:35.380 Demilade Agboola: Because I know I got tagged in it today for non-glp sales
299 00:34:35.969 ⇒ 00:34:43.960 Demilade Agboola: if we could remove Sema. So I did that, and I refreshed the like. I changed the filters to not include Sema.
300 00:34:44.880 ⇒ 00:34:48.989 Demilade Agboola: but I was just wondering if we if that was a theme.
301 00:34:54.860 ⇒ 00:34:58.730 Annie Yu: Should I share my screen, or do you want to share it?
302 00:34:59.010 ⇒ 00:35:01.510 Annie Yu: Is it we can? We can check it.
303 00:35:02.110 ⇒ 00:35:03.430 Demilade Agboola: Yeah.
304 00:35:03.430 ⇒ 00:35:04.390 Annie Yu: That’s better.
305 00:35:05.460 ⇒ 00:35:06.510 Demilade Agboola: Okay.
306 00:35:07.000 ⇒ 00:35:11.549 Demilade Agboola: Actually, I think Josh is mistaken. I can’t see Sema in the the new product
307 00:35:11.880 ⇒ 00:35:16.170 Demilade Agboola: it’s in selected, but it’s there, though, that that one I had to to fix.
308 00:35:18.060 ⇒ 00:35:20.030 Annie Yu: The? Was it the gummy.
309 00:35:21.407 ⇒ 00:35:26.680 Demilade Agboola: No, actually was different types of Sema. I just had to remove them
310 00:35:28.400 ⇒ 00:35:32.630 Demilade Agboola: on the filters if you check the the selected one. Now you see that they aren’t there anymore?
311 00:35:33.400 ⇒ 00:35:34.030 Demilade Agboola: Yeah.
312 00:35:34.030 ⇒ 00:35:35.030 Annie Yu: Top one
313 00:35:35.530 ⇒ 00:35:37.940 Demilade Agboola: Yeah, I had to just remove them.
314 00:35:38.810 ⇒ 00:35:40.460 Annie Yu: Okay. Thank you.
315 00:35:40.460 ⇒ 00:35:42.920 Demilade Agboola: That’s fine. It’s no problem. Just wanted to be sure.
316 00:35:43.120 ⇒ 00:35:50.150 Annie Yu: Yeah, well, we’re on this. I think one more question is the the gummy, the total
317 00:35:50.340 ⇒ 00:35:58.089 Annie Yu: non glp products, I think. At least in Mattesh dashboard, he said. Gummy should be glp.
318 00:35:58.330 ⇒ 00:36:03.310 Annie Yu: But then here, non-glp, we we have gummies here, and
319 00:36:03.430 ⇒ 00:36:08.189 Annie Yu: I don’t know if that’s like intended, or we should remove it.
320 00:36:09.150 ⇒ 00:36:10.690 Demilade Agboola: Got me as a glp.
321 00:36:12.290 ⇒ 00:36:13.410 Annie Yu: I think so.
322 00:36:15.240 ⇒ 00:36:20.819 Annie Yu: Yeah, based on the Mattesh, the the Gross Kpi marketing. Kpi Mattesh is using.
323 00:36:21.910 ⇒ 00:36:23.190 Demilade Agboola: Interesting.
324 00:36:24.920 ⇒ 00:36:27.929 Annie Yu: I think we got this list from Qatar, too. So.
325 00:36:28.630 ⇒ 00:36:32.030 Demilade Agboola: Yeah. So we just use Carter’s definition
326 00:36:32.717 ⇒ 00:36:35.999 Demilade Agboola: hasn’t necessarily said anything about that.
327 00:36:37.588 ⇒ 00:36:40.219 Demilade Agboola: So unless we’re gonna use, like.
328 00:36:41.070 ⇒ 00:36:46.640 Demilade Agboola: I dislike using different definitions for 2 different people, because then it starts to create confusion over time.
329 00:36:46.780 ⇒ 00:36:47.160 Annie Yu: Yeah.
330 00:36:47.770 ⇒ 00:36:48.380 Demilade Agboola: Nice.
331 00:36:49.120 ⇒ 00:36:53.160 Demilade Agboola: So you know, what can we can we ask Kota if sema.
332 00:36:53.690 ⇒ 00:36:56.499 Demilade Agboola: if ingest, if certain gummies are
333 00:36:56.760 ⇒ 00:37:05.839 Demilade Agboola: glp, if he says yes, then we we can make those changes as well. If he says No, I mean, I think Sama should be.
334 00:37:06.350 ⇒ 00:37:08.590 Demilade Agboola: But Samuel side itself is glp right.
335 00:37:08.800 ⇒ 00:37:12.300 Annie Yu: Yeah, yeah, like, other other sema, products.
336 00:37:12.440 ⇒ 00:37:16.650 Demilade Agboola: Yeah, so Sam, are should be glp as well.
337 00:37:17.110 ⇒ 00:37:17.890 Annie Yu: Yeah.
338 00:37:18.050 ⇒ 00:37:25.160 Annie Yu: okay, yeah, I I’m gonna ask him and tag you there. But whatever he answers, I can adjust.
339 00:37:26.880 ⇒ 00:37:28.109 Demilade Agboola: Alright, sounds good.
340 00:37:30.570 ⇒ 00:37:33.559 Annie Yu: Okay, is that it? For now.
341 00:37:33.820 ⇒ 00:37:34.970 Demilade Agboola: Alright, that’s all. That’s.
342 00:37:34.970 ⇒ 00:37:42.589 Annie Yu: So. Yeah. And I just approved that refund the dupe. But, like I I don’t like, I’ll I’ll let you merge it if
343 00:37:42.960 ⇒ 00:37:44.040 Annie Yu: if that works.
344 00:37:44.460 ⇒ 00:37:45.410 Demilade Agboola: Okay, no, no, that’s fine.
345 00:37:45.410 ⇒ 00:37:51.310 Annie Yu: I saw it says, like some Dbt test failed. But I don’t really know what that means.
346 00:37:51.480 ⇒ 00:37:54.619 Demilade Agboola: Yeah, that’s Dora. I’ll I’ll handle it.
347 00:37:54.760 ⇒ 00:37:55.520 Annie Yu: Okay.