Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-05-27 Meeting participants: Annie Yu, Demilade Agboola, Josh, Rob, Awaish Kumar
WEBVTT
1 00:03:44.870 ⇒ 00:03:47.710 rob: Hey, Demo Lade, I have a question for you.
2 00:03:49.370 ⇒ 00:03:50.529 Demilade Agboola: Hi rob, shoot.
3 00:03:51.540 ⇒ 00:04:00.559 rob: I’m just wondering jonah was just asking me a question. How often does your tableau model refresh? I I assume it’s like every hour.
4 00:04:01.640 ⇒ 00:04:06.809 Demilade Agboola: Oh, no! The models refresh at the beginning of the work day.
5 00:04:07.490 ⇒ 00:04:09.300 rob: Oh, okay, just once a day.
6 00:04:10.070 ⇒ 00:04:15.300 Demilade Agboola: Yeah, do we need do we need a certain report at a faster cadence?
7 00:04:16.700 ⇒ 00:04:22.310 rob: maybe not. I just told him the wrong thing, so I’ll tell him it’s it’s just once a day.
8 00:04:23.430 ⇒ 00:04:31.600 Demilade Agboola: Okay. Alright. So they’re just if there’s any report that needs a faster cadence, please let us know we’ll definitely work on that.
9 00:04:33.510 ⇒ 00:04:33.930 rob: Okay.
10 00:04:51.770 ⇒ 00:05:01.119 Josh : There’s a bunch of questions flying around about accuracy of vast data versus tableau data, you know.
11 00:05:01.640 ⇒ 00:05:02.969 Josh : which I saw.
12 00:05:03.510 ⇒ 00:05:08.150 Josh : So is Robert on. Well, let me look at Jim.
13 00:05:13.250 ⇒ 00:05:16.150 Josh : A brain forge seems enough. Robert’s joining.
14 00:05:18.850 ⇒ 00:05:20.649 Demilade Agboola: Pardon I need to.
15 00:05:23.031 ⇒ 00:05:28.999 Demilade Agboola: Unfortunately, Rob is out of office this week. So we’ll be, you know, holding the port.
16 00:05:31.880 ⇒ 00:05:38.030 Josh : Okay, that’s scheduled good question for for Rob.
17 00:05:38.490 ⇒ 00:05:39.532 Josh : How do we?
18 00:05:40.260 ⇒ 00:05:44.100 Josh : how do we verify what’s going on with this? Because I got one report telling me
19 00:05:44.370 ⇒ 00:05:47.629 Josh : 4.3, and I have another report telling 6.1.
20 00:05:48.730 ⇒ 00:05:58.279 rob: Yeah, that’s what I was asking. Let me look in big query. I’m a doctor’s office, but I’ll be home in half an hour, so let me run it in
21 00:05:58.530 ⇒ 00:06:00.440 rob: bigquery and
22 00:06:00.780 ⇒ 00:06:08.329 rob: just see are you when you’re comparing them? Is he looking in bass because their reporting has always been bad?
23 00:06:09.900 ⇒ 00:06:11.680 Josh : Yeah, I think he’s looking at best.
24 00:06:12.160 ⇒ 00:06:20.400 rob: Yeah, I I mean, I suspect that it’s on their end. But yeah, I’ll just make sure that my numbers match
25 00:06:20.690 ⇒ 00:06:22.060 rob: brain forges.
26 00:06:22.940 ⇒ 00:06:23.300 Josh : No.
27 00:06:23.300 ⇒ 00:06:25.990 Demilade Agboola: Is it one of it possible to get like a
28 00:06:26.250 ⇒ 00:06:31.499 Demilade Agboola: CC. In the like? What’s going on? Because I’m flying blind here. But.
29 00:06:33.800 ⇒ 00:06:39.180 rob: Yeah, I I don’t think I’ve seen all of it. I just Jonah just came to me this morning and was asking.
30 00:06:39.810 ⇒ 00:06:44.020 Josh : Jonah started ringing the alarm, saying, Hey.
31 00:06:44.490 ⇒ 00:06:48.509 Josh : there’s this thing that sets up, and I think his numbers might be wrong.
32 00:06:48.790 ⇒ 00:06:54.450 Josh : And again it’s just back to this root cause issue that like, I don’t have a single source of truth that everyone just crossed.
33 00:06:58.830 ⇒ 00:07:01.680 Demilade Agboola: Okay. I mean, it’ll like
34 00:07:02.930 ⇒ 00:07:10.319 Demilade Agboola: Rob, if you need me to triage on this, I’m definitely available cause I’m just trying to figure out like what exactly the disparity is.
35 00:07:10.820 ⇒ 00:07:24.629 rob: What I’m gonna do, Demo. I’m gonna do a full export of Basques numbers just to make sure they’re not dropping web hooks. That’s the only thing I could see that are making our numbers off is if they’re not pushing some web hooks.
36 00:07:24.920 ⇒ 00:07:31.630 rob: But historically, it’s been really low. It’s been like point oh, 9%,
37 00:07:31.790 ⇒ 00:07:36.730 rob: something like that that they drop. So let me make sure that hasn’t increased.
38 00:07:39.510 ⇒ 00:07:49.199 Demilade Agboola: Yeah, I think when we were looking at the other like previous task, we saw, we discovered that there was an increase in it, but it was still like relatively low, but there was an increase.
39 00:07:50.430 ⇒ 00:07:51.330 rob: Okay.
40 00:07:52.020 ⇒ 00:07:59.650 rob: I mean to be off that much. So they’d have to be dropping like 10% right? And that that doesn’t sound right. But.
41 00:08:02.190 ⇒ 00:08:02.905 Demilade Agboola: Okay,
42 00:08:03.730 ⇒ 00:08:09.309 Demilade Agboola: again. I don’t know. Like, I said, would it be possible to just tag me like the source of these messages? So I can just kind of
43 00:08:09.430 ⇒ 00:08:11.660 Demilade Agboola: and get some context on these. Thank you.
44 00:08:11.660 ⇒ 00:08:22.539 rob: I’ll I’ll add you to the group also. Well, yeah, let’s do it in the more open one. Cause he just DM’d me and Robert, but we’ll do it in analytics.
45 00:08:23.360 ⇒ 00:08:25.059 Demilade Agboola: Okay. Sounds good. Thank you.
46 00:08:25.060 ⇒ 00:08:26.990 rob: You’re you’re in there right, Demo Lade.
47 00:08:28.670 ⇒ 00:08:30.290 Demilade Agboola: The Xml analytics.
48 00:08:30.570 ⇒ 00:08:31.130 Demilade Agboola: Yes.
49 00:08:31.130 ⇒ 00:08:34.400 rob: Analytics channel. Yeah, yeah, we’ll do it in there.
50 00:08:40.570 ⇒ 00:08:45.560 Demilade Agboola: Okay. Josh, do we have any other like.
51 00:08:46.680 ⇒ 00:08:53.449 Josh : Another bigger question is that I keep hearing about like systems getting deprecated or not getting deprecated.
52 00:08:53.830 ⇒ 00:08:55.050 Josh : Oh, God!
53 00:08:55.650 ⇒ 00:08:58.470 Josh : Trying to get an understanding of like, how are we going to?
54 00:08:58.850 ⇒ 00:09:01.720 Josh : But for us we capture a lot of anonymized data.
55 00:09:02.450 ⇒ 00:09:09.019 Josh : And then there’s like a process that has to happen to stitch data back together so we can retarget people.
56 00:09:09.770 ⇒ 00:09:12.869 Josh : So I’m just trying to understand what that workflow looks like.
57 00:09:13.710 ⇒ 00:09:16.740 Josh : And if you guys don’t know it, just add it to the list.
58 00:09:21.440 ⇒ 00:09:27.619 Demilade Agboola: Okay? Let’s see
59 00:09:43.350 ⇒ 00:09:50.290 Demilade Agboola: trying to. Is there? Is there like a timeline or frequency like, is there like a address into that?
60 00:09:50.600 ⇒ 00:09:52.720 Demilade Agboola: Or is it more open? Ended.
61 00:09:53.360 ⇒ 00:09:59.769 Josh : Just kind of open ended like, I just wanna know what your process looks like. So if you guys don’t have it today, that’s fine. Just put it together.
62 00:09:59.910 ⇒ 00:10:05.389 Josh : And then what does the sprint look like for the rest of this week, like, what are we working on? What are we gonna get done by the end of the week.
63 00:10:06.840 ⇒ 00:10:14.739 Demilade Agboola: Okay, I think we can just like, go around table and just kind of talk about like what we, what we’re going to get done this week individually.
64 00:10:16.670 ⇒ 00:10:17.999 Demilade Agboola: Okay, let’s do it.
65 00:10:19.090 ⇒ 00:10:23.359 Demilade Agboola: Alright. Annie, would you like to start?
66 00:10:23.980 ⇒ 00:10:25.400 Demilade Agboola: And then you can just go round.
67 00:10:26.380 ⇒ 00:10:32.999 Annie Yu: Yeah, sure. Are you? Should I screen share, or are you sharing.
68 00:10:34.430 ⇒ 00:10:38.790 Demilade Agboola: Oh, okay, I could screen share whichever is more convenient for you.
69 00:10:39.800 ⇒ 00:10:40.460 Annie Yu: Yeah.
70 00:10:41.870 ⇒ 00:10:53.689 Annie Yu: for me. One thing is the cohort based heat map for Ltv, so I added that Ltv. And always help with some small fixes.
71 00:10:53.990 ⇒ 00:10:56.649 Awaish Kumar: Any. Can you please share the screen or.
72 00:10:57.550 ⇒ 00:11:01.110 Demilade Agboola: Yeah, I’m about own politicians.
73 00:11:01.110 ⇒ 00:11:01.810 Annie Yu: Okay. Okay.
74 00:11:03.290 ⇒ 00:11:05.050 Demilade Agboola: Give me one second.
75 00:11:08.910 ⇒ 00:11:11.780 Demilade Agboola: Oh, right! Can you see my screen.
76 00:11:12.410 ⇒ 00:11:13.020 Annie Yu: Yeah.
77 00:11:13.880 ⇒ 00:11:15.150 Demilade Agboola: Okay.
78 00:11:19.120 ⇒ 00:11:26.390 Annie Yu: Yeah, I’m talking about that building where I don’t see that. Build out cohort base heat map.
79 00:11:28.110 ⇒ 00:11:32.460 Annie Yu: probably. Or you can click into the marketing dashboard.
80 00:11:33.150 ⇒ 00:11:33.790 Demilade Agboola: Oh!
81 00:11:34.230 ⇒ 00:11:39.380 Annie Yu: And now scroll down to the bottom. So this one is also pending
82 00:11:39.690 ⇒ 00:11:47.329 Annie Yu: the other ticket. I was talking about this build out cohort based. This one is built, and then I share.
83 00:11:47.810 ⇒ 00:11:51.959 Annie Yu: I add, Mattesh. I think Robert said that
84 00:11:53.252 ⇒ 00:11:59.310 Annie Yu: what he was trying to do. I add him with some message and
85 00:12:00.030 ⇒ 00:12:05.420 Annie Yu: gonna see if he has any feedback. And if it helps.
86 00:12:05.720 ⇒ 00:12:13.640 Annie Yu: Yeah, we were, Robert was was saying. It will probably help to grab some time with him. So I send this message.
87 00:12:16.380 ⇒ 00:12:18.249 Annie Yu: And we’ll look to
88 00:12:20.120 ⇒ 00:12:27.019 Annie Yu: set up some time with him to answer questions and walk through the entire marketing dashboard this week.
89 00:12:29.400 ⇒ 00:12:32.849 Annie Yu: But yeah, I sent this out last Friday.
90 00:12:36.340 ⇒ 00:12:44.040 Annie Yu: I’ll probably give it another half day before following up, or if Josh wants to.
91 00:12:44.800 ⇒ 00:12:49.310 Josh : Just add natash. Just add natash and be really direct like, Hey, I need to get time with you.
92 00:12:49.870 ⇒ 00:12:52.629 Josh : hey? I want to review this with you this morning.
93 00:12:53.070 ⇒ 00:12:55.350 Josh : Please let me know a good time. We can cover this.
94 00:12:55.770 ⇒ 00:13:00.540 Annie Yu: Okay, yeah. So there’s that. And then
95 00:13:02.050 ⇒ 00:13:06.170 Annie Yu: another big one is the product drill down. Dash?
96 00:13:08.640 ⇒ 00:13:21.699 Annie Yu: I think it’s in. Yeah, that one. This one is quite like a big one. But there are 3 tickets for modeling work that I added last Friday that will need your support.
97 00:13:24.530 ⇒ 00:13:31.720 Demilade Agboola: Yeah, we’ll definitely like, sync myself, oasisioned you
98 00:13:31.990 ⇒ 00:13:37.490 Demilade Agboola: where we’ll just basically figure out like the context of the tickets as well as the
99 00:13:38.561 ⇒ 00:13:43.219 Demilade Agboola: like. Who’s handling? What? Because the 3, the 3 tickets and we can split among ourselves.
100 00:13:44.471 ⇒ 00:13:49.449 Demilade Agboola: But yeah, like that, that will definitely be something we look at today as well.
101 00:13:49.890 ⇒ 00:13:59.029 Annie Yu: Okay? And can you scroll down a little bit? I have one quick question for Aish. Yeah, just a little bit more.
102 00:14:02.030 ⇒ 00:14:05.549 Annie Yu: Oh, yeah, and then I would.
103 00:14:05.800 ⇒ 00:14:07.840 Annie Yu: Can you go a little bit more?
104 00:14:09.860 ⇒ 00:14:14.529 Annie Yu: Yeah, this one. I did add a wish last
105 00:14:14.780 ⇒ 00:14:19.639 Annie Yu: week as well. I was looking at this summary table. And I
106 00:14:20.080 ⇒ 00:14:25.960 Annie Yu: saw that 1st product purchase here are different than that’s
107 00:14:26.110 ⇒ 00:14:30.009 Annie Yu: standardized product name that that we usually use. So
108 00:14:30.340 ⇒ 00:14:34.180 Annie Yu: I’m just trying to figure out, yeah.
109 00:14:34.180 ⇒ 00:14:39.340 Awaish Kumar: Yeah, I’m not sure about this user summary table, how
110 00:14:39.510 ⇒ 00:14:41.700 Awaish Kumar: it was created. But yeah, I
111 00:14:42.730 ⇒ 00:14:46.299 Awaish Kumar: basically, it’s a legacy table. So I don’t know like.
112 00:14:46.450 ⇒ 00:14:52.549 Awaish Kumar: are you using it for? Something like we might migrate it to our March tables.
113 00:14:54.300 ⇒ 00:15:02.259 Annie Yu: Oh, I tried to build that table just a little bit up the model, please.
114 00:15:04.370 ⇒ 00:15:08.550 Annie Yu: Yeah. This one. I’m trying to add that Ltv metrics.
115 00:15:15.830 ⇒ 00:15:18.249 Annie Yu: so I was using this one.
116 00:15:18.660 ⇒ 00:15:20.259 Annie Yu: Should I look?
117 00:15:20.430 ⇒ 00:15:23.389 Annie Yu: Add another table for this.
118 00:15:26.890 ⇒ 00:15:36.520 Awaish Kumar: Basically like, we created a model which has the cohort month and 1st product purchase and the
119 00:15:38.390 ⇒ 00:15:43.520 Awaish Kumar: revenue and the customers. What exactly do we need this for.
120 00:15:46.950 ⇒ 00:15:54.799 Annie Yu: Tracking by cohort the order number the order, count, customer, count orders per customer.
121 00:15:55.920 ⇒ 00:16:00.759 Awaish Kumar: Yeah. The the table which I shared with you like that has the
122 00:16:01.120 ⇒ 00:16:04.529 Awaish Kumar: that can have exactly the same things right?
123 00:16:05.160 ⇒ 00:16:06.190 Annie Yu: And is it?
124 00:16:07.110 ⇒ 00:16:10.209 Awaish Kumar: So it has the 1st product purchase. It has the month.
125 00:16:10.430 ⇒ 00:16:13.240 Awaish Kumar: and then we have all the
126 00:16:15.190 ⇒ 00:16:23.850 Awaish Kumar: like. Then it it sums up the all the revenue right even from other products, for this customer, if that.
127 00:16:23.850 ⇒ 00:16:24.500 Annie Yu: Is the requirement?
128 00:16:25.100 ⇒ 00:16:33.789 Annie Yu: No, no, yeah. So that’s a good question. But for this one the stakeholder wants to see only
129 00:16:35.510 ⇒ 00:16:40.000 Annie Yu: only metrics tied to that 1st product.
130 00:16:40.610 ⇒ 00:16:48.094 Awaish Kumar: So, for example, in this month of for the May 25 for Products summer Lane.
131 00:16:48.910 ⇒ 00:16:54.910 Awaish Kumar: 100 customers bought this product from.
132 00:16:55.400 ⇒ 00:16:59.219 Awaish Kumar: and we only want to track them. Orders which are placed for this order
133 00:17:00.278 ⇒ 00:17:01.849 Awaish Kumar: in future, for those customers.
134 00:17:01.850 ⇒ 00:17:05.770 Annie Yu: This specific? Yes, for this specific use case. Yes.
135 00:17:06.839 ⇒ 00:17:09.530 Awaish Kumar: Okay, so like, it needs to be a separate
136 00:17:10.149 ⇒ 00:17:13.659 Awaish Kumar: table. I think we don’t have anything like that before.
137 00:17:13.979 ⇒ 00:17:18.319 Awaish Kumar: So I don’t know, like user summary table should not be used. In that case.
138 00:17:19.369 ⇒ 00:17:24.249 Annie Yu: Okay, then. So the summary table is tracking all the.
139 00:17:24.809 ⇒ 00:17:25.359 Awaish Kumar: So there
140 00:17:25.359 ⇒ 00:17:36.169 Awaish Kumar: table is basically a legacy table, and I’m not sure what it is exactly it’s doing. But it it should be. It must be summing all the revenue, because historically, we have been doing that.
141 00:17:36.820 ⇒ 00:17:42.090 Annie Yu: Okay, okay, okay, so that I guess that means another ticket.
142 00:17:42.190 ⇒ 00:17:48.420 Annie Yu: Okay, I’ll I can create a ticket for that.
143 00:17:49.330 ⇒ 00:17:53.949 Demilade Agboola: Okay, sounds good. So that means we’ll have 4 tickets, and then we can split it amongst us.
144 00:17:53.950 ⇒ 00:17:56.369 Awaish Kumar: Were, were you able to create this heat map?
145 00:17:57.430 ⇒ 00:18:04.910 Awaish Kumar: Or is this is that? Yeah, that one. The yeah. Just scroll down this one.
146 00:18:07.040 ⇒ 00:18:11.970 Annie Yu: Yeah. Yeah, for this one. I have a ticket that will
147 00:18:13.370 ⇒ 00:18:27.330 Annie Yu: require modeling work. So right now, I can see I can show the absolute count using hemap. But the idea is to have another table that we can directly show the percentage.
148 00:18:27.520 ⇒ 00:18:32.550 Annie Yu: And with that all.
149 00:18:33.290 ⇒ 00:18:35.180 Awaish Kumar: Which ticket is that cross? Sell.
150 00:18:35.660 ⇒ 00:18:38.039 Annie Yu: Yeah, in the process from 3. Okay.
151 00:18:41.150 ⇒ 00:18:48.929 Awaish Kumar: We can scroll down. Okay, that cross cell model is is for that heat map.
152 00:18:52.300 ⇒ 00:18:52.890 Annie Yu: Yes.
153 00:18:52.890 ⇒ 00:18:58.542 Demilade Agboola: Yeah, okay, so I think we just need to like
154 00:18:59.110 ⇒ 00:19:03.139 Demilade Agboola: maybe not necessarily now. But we just need to like work.
155 00:19:03.370 ⇒ 00:19:08.840 Demilade Agboola: assign a ticket and just have that move to the like. Different models move to the
156 00:19:09.660 ⇒ 00:19:11.879 Demilade Agboola: whoever’s gonna cause. They’re like right now, unassigned.
157 00:19:13.331 ⇒ 00:19:18.040 Demilade Agboola: And so once we do that, at least that can keep the ball rolling on this overall project.
158 00:19:19.235 ⇒ 00:19:19.750 Awaish Kumar: Okay.
159 00:19:24.100 ⇒ 00:19:25.679 Awaish Kumar: I think it resolved.
160 00:19:29.540 ⇒ 00:19:36.709 Demilade Agboola: Okay. So I think we could just go to waste and run through.
161 00:19:39.210 ⇒ 00:19:41.529 Awaish Kumar: So this model is done.
162 00:19:42.470 ⇒ 00:19:43.780 Awaish Kumar: 3, 1, 6.
163 00:19:44.890 ⇒ 00:19:47.360 Demilade Agboola: 3, 1, 6. Yeah, that’s done.
164 00:19:50.340 ⇒ 00:19:53.040 Awaish Kumar: We can move it to done from testing.
165 00:19:57.710 ⇒ 00:19:59.246 Awaish Kumar: And the other one.
166 00:20:00.470 ⇒ 00:20:05.330 Awaish Kumar: This Zendesk syncs they are. They can be moved to done as well to it, to.
167 00:20:07.900 ⇒ 00:20:08.590 Demilade Agboola: Okay.
168 00:20:11.050 ⇒ 00:20:21.240 Awaish Kumar: And if we go back we have this spike. I have answered that internal feedback and no brainer scream.
169 00:20:21.510 ⇒ 00:20:26.340 Awaish Kumar: So yeah, we just keep it there. I don’t know how we want to
170 00:20:29.560 ⇒ 00:20:34.509 Awaish Kumar: like Robert wants to deal with it, but that’s basically kind of we have answered this question
171 00:20:36.250 ⇒ 00:20:37.540 Awaish Kumar: kind of done.
172 00:20:39.230 ⇒ 00:20:43.570 Awaish Kumar: We have answered this question to client like, why that was happening, but
173 00:20:44.030 ⇒ 00:20:48.179 Awaish Kumar: I think we can move it to done for this one as well.
174 00:20:49.600 ⇒ 00:20:50.360 Demilade Agboola: Okay.
175 00:20:51.760 ⇒ 00:20:53.859 Awaish Kumar: Was an investigation which is done
176 00:21:01.190 ⇒ 00:21:01.900 Demilade Agboola: Okay.
177 00:21:03.250 ⇒ 00:21:10.240 Awaish Kumar: Yeah, these are kind of bring in for channel. Yeah, these are done because we have marketing data. Now.
178 00:21:11.430 ⇒ 00:21:20.360 Awaish Kumar: offline and online, both models are created for that as well. So these, both and I think they are in the dashboard, and any you have
179 00:21:20.460 ⇒ 00:21:24.630 Awaish Kumar: looked at those models, and I haven’t received any
180 00:21:26.890 ⇒ 00:21:30.440 Awaish Kumar: feedback on that. So these 2 are, we can move them to done as well.
181 00:21:32.630 ⇒ 00:21:33.320 Demilade Agboola: Okay.
182 00:21:42.440 ⇒ 00:21:46.120 Demilade Agboola: yeah, it’s I can upgrade this one.
183 00:21:46.120 ⇒ 00:21:55.459 Awaish Kumar: We have both my own. We are not using coral where? But we have that general data from north team, and we have a fine data from marketing span tracker.
184 00:21:56.680 ⇒ 00:21:57.105 Demilade Agboola: Okay.
185 00:22:01.710 ⇒ 00:22:03.560 Awaish Kumar: I got this one.
186 00:22:05.340 ⇒ 00:22:07.000 Awaish Kumar: It’s kind of same thing.
187 00:22:18.020 ⇒ 00:22:18.779 Demilade Agboola: I think we have to try.
188 00:22:20.460 ⇒ 00:22:32.040 Awaish Kumar: For event models. We basically feeding to meet Sebastian’s team
189 00:22:33.950 ⇒ 00:22:40.820 Awaish Kumar: because on the Gtm, like, we are not getting the required events
190 00:22:41.820 ⇒ 00:22:47.550 Awaish Kumar: data coming to Gfr, so it’s kind of a blog. But yeah, we can just keep it there.
191 00:22:50.060 ⇒ 00:22:52.310 Demilade Agboola: I’m just okay. Put it there.
192 00:22:52.310 ⇒ 00:22:56.059 Awaish Kumar: Yeah, I have already rating. Yeah, I already commented on that.
193 00:22:56.380 ⇒ 00:22:58.850 Awaish Kumar: Yeah, this one is just a sink.
194 00:22:59.837 ⇒ 00:23:04.480 Demilade Agboola: Not sure if there’s, I think there’s a thing. Okay, continue to blocked.
195 00:23:08.960 ⇒ 00:23:10.430 Awaish Kumar: Okay, hang on.
196 00:23:11.280 ⇒ 00:23:12.500 Awaish Kumar: Yeah, that’s it.
197 00:23:14.510 ⇒ 00:23:15.720 Demilade Agboola: That is fine.
198 00:23:16.350 ⇒ 00:23:18.880 Demilade Agboola: Let me quickly go through my things.
199 00:23:21.100 ⇒ 00:23:24.100 Demilade Agboola: Oh, sorry I did. 2 assignees.
200 00:23:27.281 ⇒ 00:23:31.040 Demilade Agboola: Alright. So a couple of, I think a couple of things
201 00:23:31.400 ⇒ 00:23:38.489 Demilade Agboola: are being blocked here by Zach bask with Robert out of
202 00:23:38.620 ⇒ 00:23:43.659 Demilade Agboola: office this week. I will try and like. Just tag him, and just see like
203 00:23:44.180 ⇒ 00:23:47.289 Demilade Agboola: I believe. Robert asked him if there’s a timeline for
204 00:23:47.880 ⇒ 00:23:55.400 Demilade Agboola: the fix to the like treatment, Id, and adding it into the orders.
205 00:23:56.066 ⇒ 00:23:58.770 Demilade Agboola: I’m not sure if he has responded.
206 00:23:59.190 ⇒ 00:24:03.969 Demilade Agboola: but I would just just generally just, you know, tag him. Josh, would he help to like?
207 00:24:04.330 ⇒ 00:24:07.159 Demilade Agboola: Would he help if you helped push that.
208 00:24:07.160 ⇒ 00:24:10.590 Josh : Tag me where you need it, and just tag me at Josh
209 00:24:11.320 ⇒ 00:24:13.769 Josh : from that area. Wherever this is. Okay.
210 00:24:14.560 ⇒ 00:24:20.619 Demilade Agboola: Sounds good. So that kind of tackles this
211 00:24:23.650 ⇒ 00:24:28.930 Demilade Agboola: then to split some by odt and injections that I have done
212 00:24:29.581 ⇒ 00:24:44.770 Demilade Agboola: in terms of the I’m asking, like Josh, in terms of the overall, like you’re for Josh revenue reports in terms of like the product splits, and how you see, the products is that is that level of granularity useful to you.
213 00:24:46.330 ⇒ 00:24:49.100 Josh : Yeah, basically, what I’m looking for.
214 00:24:49.930 ⇒ 00:24:50.700 Demilade Agboola: Project.
215 00:24:51.050 ⇒ 00:25:00.410 Demilade Agboola: Alright. Yeah. The request over the last couple of weeks have been helpful to help split it into like more useful chunks, so that that has been helpful.
216 00:25:04.260 ⇒ 00:25:08.070 Demilade Agboola: Yeah, this is still like work in progress where I’m trying to.
217 00:25:10.720 ⇒ 00:25:14.917 Demilade Agboola: So I have been able to do the splits with the med kits.
218 00:25:15.750 ⇒ 00:25:18.476 Demilade Agboola: I know. So this is a question to josh
219 00:25:19.740 ⇒ 00:25:29.340 Demilade Agboola: long term. The the med kits are made up of multiple things. Do we want to be able to over time? Split the med kits into
220 00:25:29.660 ⇒ 00:25:31.080 Demilade Agboola: the individual.
221 00:25:33.730 ⇒ 00:25:38.269 Demilade Agboola: Do like like I know, for instance, the start, the start variance might be different from like the
222 00:25:38.500 ⇒ 00:25:54.299 Demilade Agboola: maintenance variance. And so like being able to split it. And it’s like, okay. So the first, st the 1st is this the second one is this, do we want to get to that level of granularity? For, like the med kits, and be able to know that, like the different orders that come in on a monthly basis or new, you know.
223 00:25:54.590 ⇒ 00:25:57.639 Demilade Agboola: quarterly basis. This is what they are getting.
224 00:25:58.140 ⇒ 00:26:01.329 Demilade Agboola: or are we just fine going. Medkit. One. Medkit. 2.
225 00:26:04.760 ⇒ 00:26:16.190 Josh : I mean, if you’re asking if there’s a way for us to demonstrate the journey, and then we should try to share the journey as much as possible. Right now. It’s good to just know that we’re making sales, and things are going through.
226 00:26:17.420 ⇒ 00:26:31.010 Demilade Agboola: Okay, no. What I’m saying is like, you know how like the medcase is made up of multiple things. Right? So potentially, we might have and just this is just some spitballing. So you might have like, maybe semaglutide.
227 00:26:32.180 ⇒ 00:26:53.589 Demilade Agboola: Do we want to be able to get to the point where, like over time, we’re able to wrap like the Med kit sales are able to like. We can put the individual orders down to the variance that was in that order. So we can say, Hey, this was actually, it’s not just the med kits order in the month of January. It was, you know, semaglutide specifically.
228 00:26:55.660 ⇒ 00:27:00.680 Demilade Agboola: and that counts as semetocyte sales like, do we? Do? We want to be able to break it down to that level of granularity.
229 00:27:01.520 ⇒ 00:27:04.870 Josh : Yeah, I don’t, really. I’m not following what you’re saying. I’m sorry.
230 00:27:05.740 ⇒ 00:27:15.250 Demilade Agboola: Oh, okay, so markets are made up of multiple variants, and those like those variants. Are sold.
231 00:27:15.520 ⇒ 00:27:18.429 Demilade Agboola: But we just call him med kids overall just for the.
232 00:27:18.430 ⇒ 00:27:20.829 Josh : Each one has different products inside of it.
233 00:27:21.360 ⇒ 00:27:21.920 Josh : Yes.
234 00:27:21.920 ⇒ 00:27:24.529 Demilade Agboola: Oh, yeah. So what I’m saying.
235 00:27:24.530 ⇒ 00:27:26.740 Josh : None of them have semoglutash.
236 00:27:27.400 ⇒ 00:27:41.999 Demilade Agboola: Yes, that was just me giving like a a just, a random. I just took a random like drug. But my point, my point with that is just basically, do we want to be able to say, Hey, for the 1st starter dose? This is the what was sold.
237 00:27:42.180 ⇒ 00:27:44.629 Demilade Agboola: and be able to rank and say for Medicaid.
238 00:27:44.630 ⇒ 00:27:50.060 Josh : If you can do account. If you can do a count of all the drugs, it’s just do a total
239 00:27:50.510 ⇒ 00:27:57.570 Josh : like number of polls of each or number of you know, whatever sold by
240 00:27:57.700 ⇒ 00:28:01.969 Josh : drug type 2 I brought on to a separate report.
241 00:28:03.060 ⇒ 00:28:10.649 Demilade Agboola: Okay, alright. So that’s kind of what we’re thinking of doing here, just being able to space it by the drug type in each of the med kits, basically
242 00:28:10.920 ⇒ 00:28:18.029 Demilade Agboola: by order. So instead of having just like the med kits order 2, we actually know what order 2 was
243 00:28:18.130 ⇒ 00:28:19.770 Demilade Agboola: and actually assign you to that?
244 00:28:23.532 ⇒ 00:28:25.940 Demilade Agboola: Okay, so right now.
245 00:28:25.940 ⇒ 00:28:30.510 Josh : Still, I don’t understand. What do you mean by order? 2 versus order, one like, what are you trying to know?
246 00:28:31.570 ⇒ 00:28:33.750 Josh : Like? What is it. You’re trying to understand.
247 00:28:34.670 ⇒ 00:28:37.219 Demilade Agboola: So med kits are. Med kits are
248 00:28:37.830 ⇒ 00:28:43.179 Demilade Agboola: a subscription like people buy them consistently. So it’s like the starts and the maintenance.
249 00:28:43.180 ⇒ 00:28:54.089 Josh : Yeah, what do you mean? Starts and normal. So there’s like, there’s a titration protocol. Yes, they start on a lower dose, and they work their way up to the
250 00:28:54.380 ⇒ 00:28:56.690 Josh : the Maxed out.
251 00:28:57.520 ⇒ 00:28:58.140 Demilade Agboola: Okay.
252 00:28:58.140 ⇒ 00:29:08.509 Josh : So like, what is the data point you’re trying to understand like for the bi, is it? The number of payrolls is the number of people that can’t titrate. What are you trying to? I’m trying to understand. What are you trying to get.
253 00:29:09.430 ⇒ 00:29:17.300 Demilade Agboola: So we’re trying to space it down so that when we look at like the revenue, or we look at the sale of the drugs. It’s not just Medicaid.
254 00:29:17.880 ⇒ 00:29:26.659 Demilade Agboola: right? Like you can actually see it rolled up back to what the actual variant in each of the med kits was based off.
255 00:29:27.130 ⇒ 00:29:27.890 Demilade Agboola: So.
256 00:29:27.890 ⇒ 00:29:30.680 Josh : So like when you say the variant, are you trying to do like
257 00:29:30.820 ⇒ 00:29:32.679 Josh : like? What? What like are you trying to say, hey?
258 00:29:33.150 ⇒ 00:29:41.000 Josh : Much Metformin. Would we do this much? Ldn, like I’m I’m just like, help me understand? Like, what is your what is the business purpose.
259 00:29:41.630 ⇒ 00:29:49.300 Demilade Agboola: Yes, so we can say you did. You did this much revenue in like or this much sales of this particular drug type.
260 00:29:50.550 ⇒ 00:29:51.300 Josh : Okay.
261 00:29:51.790 ⇒ 00:29:54.940 Demilade Agboola: Instead of just knowing that you sold med kits, you can actually know
262 00:29:56.216 ⇒ 00:30:01.109 Demilade Agboola: different like drugs that you’re selling within each med kits based on each.
263 00:30:03.110 ⇒ 00:30:04.920 Demilade Agboola: Like over a period of time.
264 00:30:05.190 ⇒ 00:30:05.850 Demilade Agboola: Yeah.
265 00:30:08.380 ⇒ 00:30:09.720 Josh : Yeah, I guess so.
266 00:30:10.830 ⇒ 00:30:16.630 Josh : I mean, I I have to see what you’re trying to say. But yeah, I mean, yeah, make sense.
267 00:30:17.870 ⇒ 00:30:18.620 Demilade Agboola: Oh, okay.
268 00:30:18.940 ⇒ 00:30:29.319 Demilade Agboola: alright. So that’s that’s that’s work in progress. I had a call with Rebecca, and she was able to explain some of these things to me, so I’ll be able to start modeling
269 00:30:29.420 ⇒ 00:30:32.899 Demilade Agboola: this week and to be able to push something out.
270 00:30:33.830 ⇒ 00:30:34.510 Josh : Cool.
271 00:30:35.330 ⇒ 00:30:40.620 Demilade Agboola: Yeah. Does anyone have any other things to like? Bring up.
272 00:30:46.576 ⇒ 00:30:52.909 Annie Yu: For the 3 tickets that I created, not sure them all day and wish how you’re gonna split the work.
273 00:30:57.640 ⇒ 00:31:00.075 Demilade Agboola: I mean to be fair. I could just
274 00:31:01.880 ⇒ 00:31:08.489 Demilade Agboola: I wish, have you gone through the tickets? Are there any ones you want to take? Or are there ones that kind of close to what you’ve done already?
275 00:31:11.180 ⇒ 00:31:16.559 Demilade Agboola: I think that might be easier for you to do so. If you’ve done something very similar to it. It would just be faster for you to do that way.
276 00:31:22.960 ⇒ 00:31:24.420 Awaish Kumar: Is it a question for me.
277 00:31:25.480 ⇒ 00:31:33.939 Demilade Agboola: Yes, I was asking if there’s anyone that you’ve done like makes quite similar to what you’ve done in like this project, so it would be faster for you to do it.
278 00:31:35.258 ⇒ 00:31:37.709 Awaish Kumar: Which one would cross, sell one.
279 00:31:39.764 ⇒ 00:31:47.069 Annie Yu: Also one. I already assigned to you a wish, just because last Friday we got a chance to talk about it. But there’s.
280 00:31:47.070 ⇒ 00:31:48.460 Awaish Kumar: We can work on that.
281 00:31:49.350 ⇒ 00:31:50.179 Annie Yu: Yeah, okay.
282 00:31:50.180 ⇒ 00:31:55.819 Awaish Kumar: I can work on that one, and I can also work on the one we just added for Ltv.
283 00:31:55.990 ⇒ 00:31:57.650 Awaish Kumar: Which which you just described.
284 00:31:58.780 ⇒ 00:32:02.830 Demilade Agboola: Okay, alright. So I’ll just quickly do that.
285 00:32:02.830 ⇒ 00:32:05.559 Awaish Kumar: For sure, for other ones, I don’t know. Like.
286 00:32:05.980 ⇒ 00:32:08.889 Awaish Kumar: maybe if it is related something I don’t know.
287 00:32:09.910 ⇒ 00:32:12.370 Demilade Agboola: Oh, no, no, it’s fine. If if there’s anything that you have to like
288 00:32:12.490 ⇒ 00:32:21.673 Demilade Agboola: like, yeah, it’s not, it’s new to you. Then I might as well just do it, because then it’s also like I like that would just ease your burden.
289 00:32:22.800 ⇒ 00:32:27.629 Awaish Kumar: No, no, yeah, that I was just saying that. What? What is the what are the 2 about like.
290 00:32:31.870 ⇒ 00:32:37.601 Demilade Agboola: Give me one second. I’m trying to find the tickets. Alright. So they have. We have
291 00:32:38.420 ⇒ 00:32:39.590 Awaish Kumar: Product.
292 00:32:40.170 ⇒ 00:32:41.360 Demilade Agboola: Let me share my screen.
293 00:32:44.770 ⇒ 00:32:47.090 Demilade Agboola: But we have category and product mapping.
294 00:32:47.300 ⇒ 00:32:51.650 Demilade Agboola: We have cross sale and we have product switching. And we also have the.
295 00:32:51.650 ⇒ 00:32:57.109 Awaish Kumar: I’m just asking about 3, 2, 7, and 3 to 8 from any like. What is this about?
296 00:32:57.620 ⇒ 00:33:11.180 Annie Yu: Yeah, 37 demode. I think you are probably already on this for a little bit. But that’s just we want to see what product, what product falls under which category and
297 00:33:11.570 ⇒ 00:33:25.309 Annie Yu: I talked to Joanna. And the way we do category now in our model are is probably not up to date. So if you can click into that, I did list out
298 00:33:25.510 ⇒ 00:33:36.679 Annie Yu: some of the some of the key pro no key categories that she want to see, and I don’t think they have a structured way to see category now. But
299 00:33:37.140 ⇒ 00:33:41.260 Annie Yu: I asked her, oh, would it be okay if we just like, have those
300 00:33:41.660 ⇒ 00:33:58.550 Annie Yu: key ones on your website. And she said, That’s totally fine. But she would split hormones and hair. So that would mean like 3 categories. And I touch on this briefly with Robert. He’s also acknowledged that we probably will have to go through
301 00:33:58.720 ⇒ 00:34:05.069 Annie Yu: like maybe join our someone else for them to manually map that is.
302 00:34:05.070 ⇒ 00:34:08.299 Awaish Kumar: Product category, basically coming from those sheets.
303 00:34:08.889 ⇒ 00:34:13.459 Awaish Kumar: Robert, the product mapping sheet and the product offering sheet.
304 00:34:14.460 ⇒ 00:34:14.699 Demilade Agboola: And
305 00:34:14.949 ⇒ 00:34:16.909 Awaish Kumar: And it was entered by someone. Panels.
306 00:34:19.159 ⇒ 00:34:23.156 Demilade Agboola: Yes, I’ll I’ll you know. I’ll just look into that, and I’ll
307 00:34:23.790 ⇒ 00:34:24.300 Awaish Kumar: Yep.
308 00:34:24.300 ⇒ 00:34:25.080 Demilade Agboola: Interesting.
309 00:34:25.810 ⇒ 00:34:29.259 Demilade Agboola: That is something we push, and then we have.
310 00:34:31.216 ⇒ 00:34:32.369 Annie Yu: Another, one.
311 00:34:32.870 ⇒ 00:34:35.770 Demilade Agboola: The cross sale. Are we say, Okay, start to wish already.
312 00:34:35.770 ⇒ 00:34:36.570 Awaish Kumar: It’s okay.
313 00:34:38.000 ⇒ 00:34:41.380 Demilade Agboola: And then last one is.
314 00:34:43.429 ⇒ 00:34:45.149 Awaish Kumar: For the switching model.
315 00:34:46.929 ⇒ 00:34:50.489 Demilade Agboola: Yes, I believe product switching model.
316 00:34:51.010 ⇒ 00:34:56.150 Annie Yu: Yeah, this one is probably more complex.
317 00:34:56.159 ⇒ 00:34:56.939 Awaish Kumar: Oh, yeah.
318 00:34:57.580 ⇒ 00:34:59.010 Annie Yu: So there are some
319 00:35:01.910 ⇒ 00:35:03.950 Awaish Kumar: Yeah, we discussed that as well.
320 00:35:05.080 ⇒ 00:35:11.799 Demilade Agboola: Yeah, I do have. I do have a bit of room on my on my plate for Eden, so I could. If it’s like something I can handle.
321 00:35:14.410 ⇒ 00:35:15.190 Awaish Kumar: I’m sorry.
322 00:35:15.960 ⇒ 00:35:20.580 Demilade Agboola: I said I, I do have a lot of like a bit more room on my plate, so if it’s something I can.
323 00:35:20.830 ⇒ 00:35:22.199 Demilade Agboola: I can. I can take on it.
324 00:35:26.700 ⇒ 00:35:31.509 Awaish Kumar: Like. No, no, like what what you said like you already have a lot of in your plate, or you.
325 00:35:31.790 ⇒ 00:35:32.500 Demilade Agboola: I haven’t.
326 00:35:32.500 ⇒ 00:35:34.330 Awaish Kumar: Our context into this.
327 00:35:35.320 ⇒ 00:35:40.910 Demilade Agboola: I don’t. I don’t actually have the context into this, but I do have room on my plate to tackle it. If there’s.
328 00:35:42.870 ⇒ 00:35:48.220 Awaish Kumar: It’s okay, like, if you want to take it, you can. Otherwise I can work on that as well.
329 00:35:55.470 ⇒ 00:35:59.060 Demilade Agboola: I think I’ll just take it. So we’ll
330 00:36:01.110 ⇒ 00:36:04.198 Demilade Agboola: I can just like Sync and just
331 00:36:05.200 ⇒ 00:36:08.509 Demilade Agboola: figure out like the product switching products that you model.
332 00:36:10.980 ⇒ 00:36:19.960 Annie Yu: Yeah. And Demo A, I’m not sure if I I try to be clear here. But if you do need more like clarification, we can
333 00:36:20.090 ⇒ 00:36:22.080 Annie Yu: have a huddle if anything.
334 00:36:22.990 ⇒ 00:36:24.569 Demilade Agboola: Okay, that’s good.
335 00:36:28.410 ⇒ 00:36:34.590 Demilade Agboola: Alright, I need to hop. I have. Kickoff call with Urban Sams.
336 00:36:35.730 ⇒ 00:36:36.770 Demilade Agboola: Okay, yeah.
337 00:36:37.150 ⇒ 00:36:39.609 Annie Yu: This is about it. Thank you so much.
338 00:36:40.330 ⇒ 00:36:41.639 Demilade Agboola: Thank you. Bye.
339 00:36:41.910 ⇒ 00:36:42.580 Awaish Kumar: Alright!