Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-05-29 Meeting participants: Annie Yu, Awaish Kumar, Demilade Agboola
WEBVTT
1 00:00:00.000 ⇒ 00:00:03.639 Awaish Kumar: Recently it has been very little
2 00:00:03.900 ⇒ 00:00:08.920 Awaish Kumar: uncategorized as well, because some the work Devin already did on
3 00:00:09.030 ⇒ 00:00:17.299 Awaish Kumar: this product mapping thing like after that, for some time we didn’t. We didn’t have much uncategorized spend, so I’m not sure he might
4 00:00:18.140 ⇒ 00:00:19.629 Awaish Kumar: have some feedback on that.
5 00:00:20.070 ⇒ 00:00:21.760 Annie Yu: Okay, okay, that makes sense.
6 00:00:22.105 ⇒ 00:00:26.600 Demilade Agboola: Checked it. Now there’s no actual ad spend or on categorized right now.
7 00:00:28.320 ⇒ 00:00:31.890 Awaish Kumar: For any like.
8 00:00:32.100 ⇒ 00:00:37.440 Awaish Kumar: Okay, no, no, not here. Like.
9 00:00:37.440 ⇒ 00:00:38.080 Annie Yu: Another, one.
10 00:00:38.080 ⇒ 00:00:43.320 Awaish Kumar: Right. So in the product sales summary by transaction, we just remove the ad spend
11 00:00:43.620 ⇒ 00:00:51.079 Awaish Kumar: where it is uncategorized and spread it to other products. Right? You have to check it in the Int Northweme export.
12 00:00:51.890 ⇒ 00:00:53.570 Demilade Agboola: Oh, okay. Gotcha gotcha.
13 00:00:58.950 ⇒ 00:01:06.130 Awaish Kumar: Okay, let’s move on. What about these internal feedback penny.
14 00:01:10.156 ⇒ 00:01:11.050 Annie Yu: Which one?
15 00:01:11.500 ⇒ 00:01:13.109 Awaish Kumar: Order, journey, dashboard.
16 00:01:14.910 ⇒ 00:01:16.229 Annie Yu: This one.
17 00:01:16.520 ⇒ 00:01:17.799 Annie Yu: Yeah, this one.
18 00:01:18.090 ⇒ 00:01:26.660 Annie Yu: It’s the one that has multiple things to do. Once cross sale, which I will start working on today. And then
19 00:01:26.870 ⇒ 00:01:32.049 Annie Yu: there is still that category and product mapping.
20 00:01:32.210 ⇒ 00:01:36.560 Annie Yu: also the product switching model that needs to be done.
21 00:01:38.140 ⇒ 00:01:39.920 Awaish Kumar: Okay. But you can build something.
22 00:01:40.290 ⇒ 00:01:41.160 Annie Yu: This?
23 00:01:41.850 ⇒ 00:01:43.409 Annie Yu: Is this a different one?
24 00:01:44.160 ⇒ 00:01:44.880 Awaish Kumar: Oh, yeah.
25 00:01:45.310 ⇒ 00:01:46.850 Annie Yu: Order, journey.
26 00:01:47.950 ⇒ 00:01:49.310 Awaish Kumar: Our journey. Dashboard. Yeah.
27 00:01:49.310 ⇒ 00:01:59.619 Annie Yu: Yeah, sorry about that. Yeah. This one was, I do have another very similar one to this, which I put it in block now. Because I’m waiting for the vial size
28 00:02:00.640 ⇒ 00:02:02.090 Annie Yu: and refills.
29 00:02:02.700 ⇒ 00:02:03.950 Awaish Kumar: So this is blocked.
30 00:02:05.710 ⇒ 00:02:06.360 Annie Yu: Yeah.
31 00:02:07.990 ⇒ 00:02:08.850 Awaish Kumar: Okay.
32 00:02:14.500 ⇒ 00:02:16.419 Awaish Kumar: can you unblock for what?
33 00:02:21.030 ⇒ 00:02:29.639 Annie Yu: I do believe Mode has a ticket, but but to my knowledge, jamade, are you still waiting for Eden team to
34 00:02:29.740 ⇒ 00:02:31.529 Annie Yu: to confirm some things.
35 00:02:42.210 ⇒ 00:02:44.440 Demilade Agboola: Sorry. What? What’s the case of this.
36 00:02:44.760 ⇒ 00:02:47.530 Annie Yu: And this is, for I should be so.
37 00:02:51.660 ⇒ 00:02:52.530 Demilade Agboola: Well done.
38 00:02:56.113 ⇒ 00:03:00.589 Demilade Agboola: I’m not sure like. What what do we need here in particular? Because I’m I’m a bit.
39 00:03:00.590 ⇒ 00:03:12.870 Awaish Kumar: But this is a ticket for order. What I’m asking it is in the internal feedback. I’m just asking the update here if it’s done blocked. Whatever those feedback.
40 00:03:12.870 ⇒ 00:03:18.830 Demilade Agboola: I know, if I remember correctly, the only the thing that were blocked by in terms of
41 00:03:19.250 ⇒ 00:03:24.360 Demilade Agboola: the entire flow was, we also needed transactions.
42 00:03:24.650 ⇒ 00:03:31.280 Demilade Agboola: so that if we know what transactions were, and what doctor statuses were
43 00:03:33.170 ⇒ 00:03:37.720 Demilade Agboola: we could then add that as well. But so far we haven’t been able to see that.
44 00:03:39.410 ⇒ 00:03:47.029 Awaish Kumar: Right now, like what we are, exactly what we are waiting here, waiting on. We are waiting on client feedback. Are we waiting on.
45 00:03:51.716 ⇒ 00:03:56.359 Demilade Agboola: Doctor statuses like what status like, how?
46 00:03:56.680 ⇒ 00:04:06.309 Demilade Agboola: Because it’s like Pen, there’s a lot of pending right? So what’s happening when it’s pending like, how long does it take for your doctor to actually give out the prescription? Things like that
47 00:04:06.530 ⇒ 00:04:26.919 Demilade Agboola: as well as we? Also, I know we initially thought of adding, like the transactions, information from the beginning. So it’s just like, Hey, this is when transactions happened. And this is how long it took before the transform transactions to the doctors and from the doctors to you being shipped, and you know that part of it. If
48 00:04:27.496 ⇒ 00:04:28.130 Demilade Agboola: I remember.
49 00:04:29.910 ⇒ 00:04:33.350 Awaish Kumar: So what about active same operation? Lifecycle? Is it the same.
50 00:04:34.872 ⇒ 00:04:39.710 Annie Yu: Yeah, I think that one we deliver a query. Robert did that.
51 00:04:40.090 ⇒ 00:04:43.320 Annie Yu: and I I don’t think we’ve heard back.
52 00:04:43.490 ⇒ 00:04:51.150 Annie Yu: but that could mean like, there’s no problem. But I I think I can’t really confirm that until Robert is.
53 00:04:51.470 ⇒ 00:04:55.069 Annie Yu: But we delivered this a while ago. And
54 00:04:57.210 ⇒ 00:05:00.510 Annie Yu: yeah, I don’t think there’s a next step as of now.
55 00:05:09.930 ⇒ 00:05:18.240 Awaish Kumar: We have. This alright spike is just a thing, restructure data model
56 00:05:18.720 ⇒ 00:05:24.160 Awaish Kumar: like you’re, it’s it’s a student progress, right? No, that we’ll add it.
57 00:05:30.244 ⇒ 00:05:32.015 Demilade Agboola: Oh, sorry that that task.
58 00:05:34.460 ⇒ 00:05:40.660 Demilade Agboola: so it’s still in progress, but it’s taking a back burner based on the other things that have come up.
59 00:05:42.265 ⇒ 00:05:43.090 Demilade Agboola: See?
60 00:05:43.480 ⇒ 00:05:44.360 Demilade Agboola: Yeah.
61 00:05:46.530 ⇒ 00:05:55.059 Awaish Kumar: Okay, this is kind of like, is it blocked, or is it just? You put a backlog because you got something else
62 00:05:55.210 ⇒ 00:05:56.340 Awaish Kumar: to do? Urgent.
63 00:05:59.400 ⇒ 00:06:09.900 Demilade Agboola: it’s a com. It’s largely because it’s in backlog. If if it was active that the blocker wouldn’t be a blocker I still need to sit down with, like Rebecca and
64 00:06:11.330 ⇒ 00:06:16.320 Demilade Agboola: properly like, break down the of how we want to structure out the med kits.
65 00:06:18.260 ⇒ 00:06:21.939 Awaish Kumar: Okay, kind of in progress with client.
66 00:06:26.350 ⇒ 00:06:27.140 Awaish Kumar: Nice.
67 00:06:28.140 ⇒ 00:06:28.850 Demilade Agboola: Yeah, sure.
68 00:06:28.850 ⇒ 00:06:35.960 Awaish Kumar: Helpful like we are still in communication with client. Right? It’s still kind of in progress.
69 00:06:37.440 ⇒ 00:06:42.640 Demilade Agboola: Yes, but it’s very like, I said. It’s very slow, so it’s a back. It’s a back up.
70 00:06:43.160 ⇒ 00:06:43.870 Awaish Kumar: Okay?
71 00:06:45.067 ⇒ 00:06:52.120 Awaish Kumar: What about these tickets? Any for marketing dashboard for? Cmo, have you.
72 00:06:52.120 ⇒ 00:06:59.869 Annie Yu: The marketing dashboard is kind of the same as the Ltv. One have to investigate that before we can get back.
73 00:07:00.844 ⇒ 00:07:04.239 Awaish Kumar: What like Ltv. One the heat map.
74 00:07:05.330 ⇒ 00:07:10.400 Annie Yu: Yeah, the the Ltv heat map. Yeah, just the the one we just went through.
75 00:07:11.590 ⇒ 00:07:20.229 Annie Yu: And then product drill down. Dash. I do have some things to build out. And I’ll build the cross sale table today.
76 00:07:20.938 ⇒ 00:07:24.469 Annie Yu: But yeah, I still have to.
77 00:07:25.170 ⇒ 00:07:31.260 Annie Yu: I wanna follow up on the the category mapping and the product switching for for this ticket.
78 00:07:31.590 ⇒ 00:07:35.300 Annie Yu: I think those are on the Muladi’s plate.
79 00:07:36.485 ⇒ 00:07:37.000 Awaish Kumar: Yes.
80 00:07:41.380 ⇒ 00:07:50.080 Demilade Agboola: Yeah. So I will work on that today. And there have been a lot of like like request and ad hoc things. But that’s what I would. That’s my focus today.
81 00:07:51.450 ⇒ 00:07:51.770 Annie Yu: Okay.
82 00:07:53.952 ⇒ 00:07:59.500 Awaish Kumar: then we have. This is just a sync we can switch.
83 00:08:00.960 ⇒ 00:08:07.490 Awaish Kumar: It’s a list any. This is just a spy guide you on, student. It’s a student promise.
84 00:08:08.070 ⇒ 00:08:12.579 Annie Yu: Yeah, that one. I haven’t really worked on that one but that one is
85 00:08:12.730 ⇒ 00:08:20.589 Annie Yu: gonna come after the Ltv heat map table. So that’s like a longer term solution. But
86 00:08:21.360 ⇒ 00:08:24.330 Annie Yu: we’re gonna figure out the Ott key map for now.
87 00:08:25.350 ⇒ 00:08:30.149 Awaish Kumar: And about like download. You have some other things also in progress.
88 00:08:30.370 ⇒ 00:08:37.020 Awaish Kumar: Add gender to production summary, and this display narrative more granular form.
89 00:08:37.440 ⇒ 00:08:41.320 Awaish Kumar: So are you working on this, these right now, like, are they.
90 00:08:41.320 ⇒ 00:08:47.470 Demilade Agboola: Understood gender to product sales. Summary was done yesterday.
91 00:08:49.250 ⇒ 00:08:53.720 Awaish Kumar: Like. But okay, but how did you find out the address plan by gender.
92 00:08:54.940 ⇒ 00:08:59.249 Demilade Agboola: So I have the. There’s a dim customer table.
93 00:09:00.070 ⇒ 00:09:06.270 Demilade Agboola: So I I tied the the orders. I type it back to orders.
94 00:09:06.830 ⇒ 00:09:07.600 Demilade Agboola: Yes.
95 00:09:07.600 ⇒ 00:09:13.130 Awaish Kumar: You can. Yeah, you can find out the order data, but and spend.
96 00:09:13.750 ⇒ 00:09:15.639 Awaish Kumar: We don’t know right.
97 00:09:17.140 ⇒ 00:09:23.400 Demilade Agboola: I mean, technically, it would attribute the order to like the order that I was computed
98 00:09:23.800 ⇒ 00:09:25.669 Demilade Agboola: to that ad spend.
99 00:09:27.430 ⇒ 00:09:30.310 Demilade Agboola: But it’s not necessarily a thing of like
100 00:09:31.890 ⇒ 00:09:40.000 Demilade Agboola: the ad was like the ad was spent on a on a particular agenda. I don’t know how to explain, but it’s just like about the conversion like.
101 00:09:42.150 ⇒ 00:09:44.995 Awaish Kumar: Yeah. But app was spent
102 00:09:46.180 ⇒ 00:09:52.060 Awaish Kumar: like, how much? Add how much we spent to get this specific order.
103 00:09:52.490 ⇒ 00:09:54.369 Awaish Kumar: How do you get that.
104 00:09:56.790 ⇒ 00:09:58.340 Awaish Kumar: Yeah, I I think it.
105 00:09:58.810 ⇒ 00:09:59.800 Demilade Agboola: Let me see.
106 00:10:05.640 ⇒ 00:10:11.410 Demilade Agboola: because literally, the only like the major change I made was just like going to base order
107 00:10:11.810 ⇒ 00:10:17.320 Demilade Agboola: and including gender in there. So it’s still.
108 00:10:19.580 ⇒ 00:10:27.200 Demilade Agboola: It’s still the same sort of like granularity. Be like it’s just now level of like vendor to it.
109 00:10:29.240 ⇒ 00:10:38.590 Awaish Kumar: Yeah, like when I added, Product, ma’am, this this mapping schedule. So I don’t know. I orders. I know. I know I can
110 00:10:39.130 ⇒ 00:10:46.000 Awaish Kumar: split them for one more category like with the product, like what I can call it membership plan.
111 00:10:46.260 ⇒ 00:10:48.640 Awaish Kumar: But I spent, I don’t know, like
112 00:10:48.780 ⇒ 00:10:59.379 Awaish Kumar: I don’t have much granularity, granularity into like based on membership plan, how we spend that. So basically we use
113 00:10:59.850 ⇒ 00:11:05.820 Awaish Kumar: then to to handle that, we basically use this logic to see, like for the membership plan.
114 00:11:06.000 ⇒ 00:11:13.059 Awaish Kumar: how much orders were placed like similar same logic as percentage of orders. So for Sema.
115 00:11:13.699 ⇒ 00:11:23.540 Awaish Kumar: maybe we had 1,000 orders. And for each membership plan, how many orders. And using the percentage of those orders we multiplied by ad spend to basically get some
116 00:11:24.280 ⇒ 00:11:26.500 Awaish Kumar: and it’s been assigned to each
117 00:11:26.970 ⇒ 00:11:32.320 Awaish Kumar: membership plan so like, what would was that same logic used for gender? Or is it
118 00:11:34.990 ⇒ 00:11:39.180 Awaish Kumar: because from the north we we don’t really get the general level information.
119 00:11:40.110 ⇒ 00:11:48.129 Demilade Agboola: Oh, no, it’s not from nothing. It was. It’s kind of based off the actual orders and then using it with Northum information.
120 00:11:48.940 ⇒ 00:11:56.679 Awaish Kumar: No, no, like when the product sells somebody by transaction. Basically it come, it is combining order data. And the north stream data.
121 00:11:57.010 ⇒ 00:11:57.800 Demilade Agboola: Yes.
122 00:11:57.940 ⇒ 00:12:03.250 Demilade Agboola: So what I’m saying is, it was added to the orders data before
123 00:12:03.510 ⇒ 00:12:07.209 Demilade Agboola: we combined it with nothing data to start attributing
124 00:12:08.050 ⇒ 00:12:14.619 Demilade Agboola: So it adds a new level of granularity to the other data. As we are trying to map the ad spend to it.
125 00:12:14.620 ⇒ 00:12:18.970 Awaish Kumar: But then admin gets duplicated for for the rows.
126 00:12:22.480 ⇒ 00:12:23.430 Demilade Agboola: No.
127 00:12:23.690 ⇒ 00:12:24.130 Awaish Kumar: So like.
128 00:12:24.660 ⇒ 00:12:35.479 Awaish Kumar: like when I join both data, right? So the data is coming from add orders. And then data, some. I join it, based on like the date and the extended product name
129 00:12:35.920 ⇒ 00:12:51.220 Awaish Kumar: so, and the membership plan right now, like, you must have 2 rows for the orders like date membership plan, standardized product, name, and the gender male and the same for gender female. Now you have 2 rows
130 00:12:51.890 ⇒ 00:12:56.820 Awaish Kumar: for the same joining key, so it gets duplicated.
131 00:13:02.900 ⇒ 00:13:04.444 Demilade Agboola: Let me see
132 00:13:09.560 ⇒ 00:13:12.770 Awaish Kumar: Yeah, like you can look at it. I don’t know like I have not
133 00:13:13.290 ⇒ 00:13:15.984 Awaish Kumar: seen what you have done. I’m just
134 00:13:17.430 ⇒ 00:13:19.150 Demilade Agboola: Yeah.
135 00:13:23.130 ⇒ 00:13:29.310 Awaish Kumar: So we can move forward for, like, I just have to get into another meeting after after this.
136 00:13:31.530 ⇒ 00:13:32.430 Awaish Kumar: Hello!
137 00:13:32.550 ⇒ 00:13:38.260 Awaish Kumar: So what about this is blogged. Any update auto journey dashboard. We we discussed that one right.
138 00:13:38.720 ⇒ 00:13:39.330 Annie Yu: Yeah.
139 00:13:40.250 ⇒ 00:13:48.819 Awaish Kumar: Treatment. Id. These are blocked. So any feedback on these ones from anyone like anything got unblocked or anything.
140 00:13:50.830 ⇒ 00:13:54.640 Awaish Kumar: Them, Lara, you have. We have 2 tickets from your side here.
141 00:13:57.150 ⇒ 00:13:58.319 Demilade Agboola: Yeah. So the
142 00:14:00.230 ⇒ 00:14:10.740 Demilade Agboola: those ones are dependent on the treatment. Id, so because we’re trying to use treatment, Id from bask, we’re waiting for bask to get like. Give us an update on that
143 00:14:12.060 ⇒ 00:14:12.560 Demilade Agboola: so.
144 00:14:13.650 ⇒ 00:14:22.900 Awaish Kumar: Okay. And this one actually still waiting on how you pronounce the name Sebastine, or
145 00:14:24.658 ⇒ 00:14:32.020 Awaish Kumar: We are actually blocked on his team to add some events in Gtm. I’m in communication with them, and let’s
146 00:14:33.684 ⇒ 00:14:36.439 Awaish Kumar: let’s see how quickly we can do that.
147 00:14:39.570 ⇒ 00:14:52.459 Awaish Kumar: same like these events tasks which are assigned to me. They are similarly same, basically for the events data which is going to come from Gf, Google analytics, basically
148 00:14:54.063 ⇒ 00:14:56.236 Awaish Kumar: and then we have
149 00:14:57.140 ⇒ 00:15:00.179 Awaish Kumar: this one again. This is kind of
150 00:15:00.740 ⇒ 00:15:07.680 Awaish Kumar: we just gotten. I just got informed by Ayush that they have added some new sources into segment.
151 00:15:08.060 ⇒ 00:15:11.589 Awaish Kumar: The data is coming from for the new
152 00:15:11.700 ⇒ 00:15:17.720 Awaish Kumar: Emr. So I’m just going to work and investigate like how the data is coming there.
153 00:15:20.180 ⇒ 00:15:22.030 Awaish Kumar: And we have
154 00:15:26.180 ⇒ 00:15:29.373 Awaish Kumar: other ones here, I don’t know. Like there’s a lot of tickets.
155 00:15:30.070 ⇒ 00:15:30.950 Awaish Kumar: God!
156 00:15:31.500 ⇒ 00:15:40.130 Awaish Kumar: Anything like something to discuss here anything like blocking? Are we blocking each other.
157 00:15:41.040 ⇒ 00:15:46.269 Awaish Kumar: or any any clarity required on the tickets in this, which are in this cycle.
158 00:15:54.850 ⇒ 00:15:59.609 Annie Yu: For that product, drill down dash I should be able to deliver.
159 00:16:00.260 ⇒ 00:16:11.469 Annie Yu: They requested to add aspen and roas on the top, which should be. I’m pretty much done. I just wanna validate the numbers.
160 00:16:11.890 ⇒ 00:16:17.070 Awaish Kumar: Okay. So like, I updated the model yesterday. So it is done. And basically, you can
161 00:16:17.220 ⇒ 00:16:20.740 Awaish Kumar: now work on the modeling part right?
162 00:16:21.150 ⇒ 00:16:21.470 Annie Yu: Yeah.
163 00:16:22.700 ⇒ 00:16:25.510 Awaish Kumar: Okay, that’s that’s all. I think.
164 00:16:27.710 ⇒ 00:16:29.310 Awaish Kumar: So. Yeah.
165 00:16:30.121 ⇒ 00:16:43.120 Annie Yu: So I do have one more thing. A wish. I know that I am creating a ticket to investigate the new customer account between those 2 models for you, and I actually found another small thing
166 00:16:43.340 ⇒ 00:16:46.940 Annie Yu: from that cohort revenue retention summary that
167 00:16:47.600 ⇒ 00:16:53.240 Annie Yu: should be corrected. So can I also put that in the.
168 00:16:53.640 ⇒ 00:16:59.189 Awaish Kumar: Yeah, yeah, put in the ticket. I will just review that. And I then I can ask you if I need anything.
169 00:16:59.830 ⇒ 00:17:00.480 Annie Yu: Okay.
170 00:17:01.040 ⇒ 00:17:06.290 Awaish Kumar: Okay, sounds good. Okay.
171 00:17:08.020 ⇒ 00:17:12.619 Awaish Kumar: There’s nothing else from my side, so we can drop off right.
172 00:17:12.980 ⇒ 00:17:14.780 Annie Yu: Yeah, yeah, sounds good.
173 00:17:15.150 ⇒ 00:17:15.990 Annie Yu: Thank you. Guys.
174 00:17:16.099 ⇒ 00:17:17.930 Awaish Kumar: Thank you. Never had any any.
175 00:17:18.619 ⇒ 00:17:19.359 Annie Yu: Hey!
176 00:17:19.829 ⇒ 00:17:20.490 Awaish Kumar: Bye.
177 00:17:20.900 ⇒ 00:17:22.240 Demilade Agboola: Thank you. Bye.