Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-05-29 Meeting participants: Annie Yu, Awaish Kumar, Demilade Agboola, Rob
WEBVTT
1 00:00:43.050 ⇒ 00:00:44.789 Demilade Agboola: Hey? Wish! Can you hear me?
2 00:02:41.620 ⇒ 00:02:42.290 Awaish Kumar: Hello!
3 00:02:46.950 ⇒ 00:02:47.660 Demilade Agboola: Hello!
4 00:02:53.550 ⇒ 00:03:00.110 rob: Hey, guys, hey? Dem de Mulatte, do you? Are you clear now on what they want for the
5 00:03:00.590 ⇒ 00:03:02.430 rob: Regex Mapping?
6 00:03:02.830 ⇒ 00:03:04.310 rob: I just want to make sure.
7 00:03:04.790 ⇒ 00:03:19.550 Demilade Agboola: Yeah, I I did the the task yesterday and I pushed it. So it reflected in the dashboards today, I should actually just type, carter, and ask him if what what they see is much closer to what they desire.
8 00:03:20.240 ⇒ 00:03:21.100 Demilade Agboola: But yeah.
9 00:03:21.100 ⇒ 00:03:23.269 rob: Yeah, yeah, that’s all I wanted to make sure.
10 00:03:24.100 ⇒ 00:03:24.880 rob: Okay.
11 00:03:31.480 ⇒ 00:03:36.660 rob: alright. In that case, you guys probably don’t need me here. I just want to make sure you didn’t need anything else for me.
12 00:03:41.980 ⇒ 00:03:43.239 Demilade Agboola: Thanks for dropping by.
13 00:03:43.790 ⇒ 00:03:45.130 rob: Yeah, we’ll see it.
14 00:03:53.700 ⇒ 00:04:00.300 Demilade Agboola: So I’m I just moved houses, and my new house doesn’t have Wi-fi set up. So my network is very weird.
15 00:04:01.240 ⇒ 00:04:02.990 Demilade Agboola: Sometimes I hear things.
16 00:04:02.990 ⇒ 00:04:04.149 Awaish Kumar: Please relax.
17 00:04:04.580 ⇒ 00:04:06.570 Awaish Kumar: I don’t know if any is joining.
18 00:04:07.110 ⇒ 00:04:08.500 Demilade Agboola: Yeah, I don’t know if I need.
19 00:04:09.650 ⇒ 00:04:11.160 Awaish Kumar: Let me ping her.
20 00:05:12.530 ⇒ 00:05:13.530 Awaish Kumar: Hello!
21 00:05:14.840 ⇒ 00:05:15.680 Annie Yu: Hello!
22 00:05:16.720 ⇒ 00:05:19.290 Awaish Kumar: How do you guys put?
23 00:05:26.570 ⇒ 00:05:34.640 Awaish Kumar: Okay, let’s go buy the tickets. So this model is good. Right? Crossing.
24 00:05:36.283 ⇒ 00:05:44.370 Annie Yu: Yes, I will use that model to build out the visual today. So I’ll
25 00:05:45.950 ⇒ 00:05:53.100 Annie Yu: It looks good. Now, I think I just have to do some fix function to get that percentage moving.
26 00:05:56.210 ⇒ 00:06:00.090 Awaish Kumar: What do you mean by fixed function like? Is it modeling change, or.
27 00:06:01.095 ⇒ 00:06:03.350 Annie Yu: No, no, no! Just within tableau.
28 00:06:04.250 ⇒ 00:06:11.159 Awaish Kumar: Okay, that’s okay for the client feedback like you are waiting, still waiting on this one.
29 00:06:11.864 ⇒ 00:06:19.300 Annie Yu: Yeah, for this one. I do have one question. I know that yesterday you guys push some change, and that
30 00:06:19.440 ⇒ 00:06:26.536 Annie Yu: ad spend and and cac are closer between this table
31 00:06:28.960 ⇒ 00:06:46.830 Annie Yu: between the cohort revenue, retention, summary and product sales summary by transaction. But I think my question then, is even for the same product and same month. The Aspen and Ncac. Now are still different. Between these 2 models is that expected.
32 00:06:47.730 ⇒ 00:06:48.159 Awaish Kumar: So.
33 00:06:51.400 ⇒ 00:06:51.720 Annie Yu: Okay.
34 00:06:52.180 ⇒ 00:06:57.810 Awaish Kumar: Yeah, having little bit higher ad spend is expected.
35 00:06:58.260 ⇒ 00:06:59.020 Annie Yu: Okay.
36 00:06:59.480 ⇒ 00:07:01.240 Annie Yu: And you mean the.
37 00:07:01.240 ⇒ 00:07:13.540 Awaish Kumar: Like there’s there should not be much difference on that adjustment side. But we are, say, we say, like, if there is some level of uncategorized spend it is still can just
38 00:07:13.930 ⇒ 00:07:17.739 Awaish Kumar: make make little bit higher for some some products.
39 00:07:18.420 ⇒ 00:07:22.170 Annie Yu: Then how about the new customer account? Is that.
40 00:07:22.736 ⇒ 00:07:26.699 Awaish Kumar: No, it should not be different. So
41 00:07:27.760 ⇒ 00:07:32.629 Awaish Kumar: yeah, we we can look at that like, I just mentioned you that create a ticket for that one.
42 00:07:34.070 ⇒ 00:07:37.129 Annie Yu: Oh, okay, can you click into this.
43 00:07:37.620 ⇒ 00:07:38.360 Awaish Kumar: Sorry.
44 00:07:38.360 ⇒ 00:07:46.830 Demilade Agboola: What’s the level of disparity like between the ad spend? Like, how far apart is it? Is it like 5%? 10%.
45 00:07:47.497 ⇒ 00:07:57.219 Annie Yu: Wish if you can click into the build out cohort based key map for Ltv, I. I did put like an example there to see the difference?
46 00:07:57.530 ⇒ 00:07:58.430 Annie Yu: Yeah.
47 00:07:59.290 ⇒ 00:08:00.600 Awaish Kumar: Me too.
48 00:08:01.960 ⇒ 00:08:06.690 Annie Yu: So I was filtering on injectable Sema for April.
49 00:08:09.270 ⇒ 00:08:18.280 Awaish Kumar: Yes, but damilady, after your fix, like we still have uncategorized spend or not.
50 00:08:21.010 ⇒ 00:08:22.510 Annie Yu: This was after the fix.
51 00:08:24.180 ⇒ 00:08:30.770 Demilade Agboola: Yes, I’ll I’m not sure how much on categorize when we still have, but I will check that and let you know.
52 00:08:30.770 ⇒ 00:08:36.490 Awaish Kumar: Okay, so number one is ad spend is little bit different. And it
53 00:08:36.770 ⇒ 00:08:39.770 Awaish Kumar: it can be because of uncategorized spam. So there’s
54 00:08:40.049 ⇒ 00:08:47.460 Awaish Kumar: there’s like nothing much here. I I don’t think so. And on on the distinct customer side.
55 00:08:48.090 ⇒ 00:08:57.399 Awaish Kumar: I think we there’s little bit of more investigation required to have a proper answer, and I think
56 00:08:58.730 ⇒ 00:09:04.499 Awaish Kumar: we should have a ticket for that one. So we just give you a separate investigation ticket for this
57 00:09:06.750 ⇒ 00:09:11.289 Awaish Kumar: to to figure out the difference of new customers between these 2 3 months.
58 00:09:13.350 ⇒ 00:09:13.970 Annie Yu: Hi.
59 00:09:13.970 ⇒ 00:09:23.559 Annie Yu: so you’re saying that what needs to be investigate is the new customer account. And for the impact, it’s expected that they will be different.
60 00:09:25.550 ⇒ 00:09:26.420 Awaish Kumar: Yes.
61 00:09:26.710 ⇒ 00:09:27.030 Annie Yu: Okay.
62 00:09:27.530 ⇒ 00:09:33.789 Annie Yu: yeah, and do I? Okay, I’ll I’ll create a ticket. And should I assign that to a ratio.
63 00:09:33.790 ⇒ 00:09:35.150 Awaish Kumar: Yeah, sign it to me.
64 00:09:35.720 ⇒ 00:09:36.110 Annie Yu: Okay.
65 00:09:36.110 ⇒ 00:09:40.590 Awaish Kumar: Let’s see how I can see any
66 00:09:44.580 ⇒ 00:09:50.770 Awaish Kumar: sorry what the noise
67 00:09:54.350 ⇒ 00:10:02.010 Awaish Kumar: this clarity between 2 tables.
68 00:10:10.680 ⇒ 00:10:11.550 Awaish Kumar: Okay.
69 00:10:15.660 ⇒ 00:10:16.240 Annie Yu: Okay.
70 00:10:16.780 ⇒ 00:10:21.820 Awaish Kumar: And yeah, also the medium ladder. To confirm.
71 00:10:22.720 ⇒ 00:10:25.640 Awaish Kumar: add respect from categorizer just and.
72 00:10:32.540 ⇒ 00:10:37.650 Annie Yu: So one more question. So for the table that directly pulled from
73 00:10:38.100 ⇒ 00:10:42.870 Annie Yu: North Spain, the Aspen and Ncac. Are
74 00:10:44.530 ⇒ 00:10:50.430 Annie Yu: lower. So where did those on categorized data go here.
75 00:10:54.850 ⇒ 00:10:58.702 Awaish Kumar: So the table which gets the data from
76 00:11:00.149 ⇒ 00:11:13.779 Awaish Kumar: north be right? So it it will have a uncategorized spend right? If if we don’t, we could not figure out the product right? Then what happens is that in the product sales summary logic.
77 00:11:14.120 ⇒ 00:11:16.040 Awaish Kumar: We don’t want to.
78 00:11:16.725 ⇒ 00:11:26.509 Awaish Kumar: Just why would you say ignore that address plan? So instead, we want to assign that uncategorized
79 00:11:26.690 ⇒ 00:11:29.880 Awaish Kumar: to our products.
80 00:11:30.080 ⇒ 00:11:34.619 Awaish Kumar: And we do that by the total number of orders.
81 00:11:34.740 ⇒ 00:11:38.569 Awaish Kumar: So, for example, in a month, in a
82 00:11:39.810 ⇒ 00:11:48.379 Awaish Kumar: like, maybe yesterday, for example, there was a uncategorized plan of like, maybe $1,000
83 00:11:48.600 ⇒ 00:11:52.560 Awaish Kumar: and injectable Samar had, like
84 00:11:53.320 ⇒ 00:12:04.820 Awaish Kumar: like total products, for 18 were the total orders for 18 were like, maybe 5,000 out of those 1,000 are are for injectable Sema. So we just find the
85 00:12:05.050 ⇒ 00:12:14.399 Awaish Kumar: percentage of orders for injectable Sema, like 1,000 divided by 5,000 multiplied with a total expand. So we get the
86 00:12:15.687 ⇒ 00:12:23.770 Awaish Kumar: some value for a dispen for this product, and we’ll just assign it there and and remove it from the uncategorized side.
87 00:12:25.060 ⇒ 00:12:27.539 Annie Yu: Okay, got it. So if.
88 00:12:28.130 ⇒ 00:12:31.543 Awaish Kumar: Based on the percentage of orders. We just
89 00:12:32.220 ⇒ 00:12:35.840 Awaish Kumar: what to say, spread of our advertisement across products.
90 00:12:37.190 ⇒ 00:12:39.789 Awaish Kumar: Yeah, that is, one is uncategorized. Yeah.
91 00:12:40.840 ⇒ 00:12:41.220 Annie Yu: Then.
92 00:12:41.220 ⇒ 00:12:49.179 Annie Yu: Well, the stakeholder wants this cohort. Summary to be, this kind of the same logic is that doable.
93 00:12:56.700 ⇒ 00:12:59.949 Awaish Kumar: Like. If the customer wants that.
94 00:13:00.250 ⇒ 00:13:05.209 Awaish Kumar: it’s it’s obviously we. We can implement. It’s not something we cannot.