Meeting Title: CDP Work and Ticket Review Sync Date: 2025-07-08 Meeting participants: Awaish Kumar, Robert Tseng, Fireflies.ai Notetaker Tigran, Annie Yu, Amber Lin, Demilade Agboola
WEBVTT
1 00:03:02.060 ⇒ 00:03:03.349 Awaish Kumar: Hello, Robert!
2 00:03:03.965 ⇒ 00:03:10.559 Awaish Kumar: I just asked for a comment on the, and he told me that
3 00:03:10.730 ⇒ 00:03:16.429 Awaish Kumar: he started like sending the bill 2 weeks ago when they build the circle.
4 00:03:16.760 ⇒ 00:03:19.160 Awaish Kumar: Yeah. But we haven’t started to.
5 00:03:19.450 ⇒ 00:03:22.320 Awaish Kumar: I think the Zendesk data. So I will just
6 00:03:22.850 ⇒ 00:03:27.550 Awaish Kumar: switch it on for and between, then regularly sync the data.
7 00:03:28.080 ⇒ 00:03:28.455 Robert Tseng: Okay.
8 00:03:53.140 ⇒ 00:03:56.740 Robert Tseng: I still need something for Eden to sign. I I can’t just like
9 00:03:56.910 ⇒ 00:04:02.969 Robert Tseng: I mean, like I. I understand that the contract, whatever went through us. Or I’m not entirely sure, like I
10 00:04:03.130 ⇒ 00:04:12.760 Robert Tseng: for all tooling decisions I need, I need someone else on. I need someone else’s signature needed. So they’re not gonna pay it unless unless they unless they see something in writing.
11 00:04:15.070 ⇒ 00:04:15.680 Awaish Kumar: Hey?
12 00:04:15.790 ⇒ 00:04:20.199 Awaish Kumar: So should we ask them to. Instead of sending the bill, they send some contract.
13 00:04:21.952 ⇒ 00:04:29.270 Robert Tseng: Well, yeah. So I I mean, I I’ve been pretty hands off with polytomic, I think. Typically how a vendor selection
14 00:04:29.390 ⇒ 00:04:30.949 Robert Tseng: process works is
15 00:04:31.561 ⇒ 00:04:45.429 Robert Tseng: there may be a trial for some tool. There’s some signature that needs to go out for some for a trial period, and then, after that when we have to put an actual credit card down, there’s another contract that comes out. So I mean, I’m
16 00:04:45.960 ⇒ 00:04:56.869 Robert Tseng: I just have been hands off with it. If you don’t feel comfortable with kind of handling that, then, just like, Get get your time to do it. But I’m not. I don’t think I’m gonna talk to polytomic personally.
17 00:04:59.390 ⇒ 00:05:09.180 Robert Tseng: but I’m just saying like, if Poly Thomas send us a bill 2 weeks ago, they’re expecting us to pay. Well, Eden hasn’t seen anything. They haven’t signed anything, so they’re not gonna pay. And then we’re gonna run to something.
18 00:05:11.070 ⇒ 00:05:15.570 Awaish Kumar: Okay, okay, I’ll ask them to create some contract.
19 00:05:16.490 ⇒ 00:05:17.040 Robert Tseng: Yeah.
20 00:06:01.400 ⇒ 00:06:07.530 Robert Tseng: okay, we could just take like a couple of minutes to go and update your tickets. I think that would help.
21 00:07:07.840 ⇒ 00:07:09.020 Amber Lin: Hi Robert!
22 00:07:10.320 ⇒ 00:07:11.030 Robert Tseng: A.
23 00:07:13.370 ⇒ 00:07:16.220 Amber Lin: I think most of the times. My conflicts are fine.
24 00:07:16.630 ⇒ 00:07:21.330 Amber Lin: I’ll just have to move one of the urban stems ones.
25 00:07:21.960 ⇒ 00:07:22.790 Robert Tseng: Okay.
26 00:07:23.060 ⇒ 00:07:23.840 Amber Lin: Yeah.
27 00:07:28.270 ⇒ 00:07:34.889 Robert Tseng: I’m just having the team update tickets right now for a couple of minutes, and then we’ll we’ll just start a bit later.
28 00:07:35.290 ⇒ 00:07:36.000 Amber Lin: Okay.
29 00:07:36.540 ⇒ 00:07:37.210 Robert Tseng: Yeah.
30 00:08:58.830 ⇒ 00:09:06.959 Robert Tseng: okay, well, I guess hopefully, that’s enough time to kind of get started here.
31 00:09:07.520 ⇒ 00:09:16.499 Robert Tseng: So I’m having amber kind of join these, I think just needing her. Pm. Support. I think.
32 00:09:17.460 ⇒ 00:09:29.970 Robert Tseng: I think, for now I think Amber will just kind of listen in this week, and then obviously help me find find out like where we’re where we’re where we’re stuck. But I’m gonna spend the 1st half of this call just talking through quick.
33 00:09:30.110 ⇒ 00:09:44.309 Robert Tseng: rapid fire things through the tickets. We’re not going to spend more than 10 min, and then the remaining half of the time is going to be on like the the big project that we’re working on. Which is the Cdp work on for for the sprint?
34 00:09:46.250 ⇒ 00:09:49.580 Robert Tseng: I’ll share my screen, and then we’ll just kind of go into it.
35 00:09:52.200 ⇒ 00:10:04.980 Robert Tseng: yeah, pretty much anything that we get stuck on. That’s gonna take more than like 30 seconds. I’m just gonna ask you guys to either meet with me separately, or you can find find time to meet with each other. So I think the 1st thing I’ll just say is like, Okay, well.
36 00:10:05.260 ⇒ 00:10:18.570 Robert Tseng: yeah. And anything that urgently needs to be brought that you’re blocked by, that we that we want to talk talk about. Otherwise I’m just gonna go go in my order. But I’ll let let you kind of. I’ll let others kind of dictate where we’re where we’re headed first.st
37 00:10:28.170 ⇒ 00:10:36.369 Robert Tseng: Okay? Well, if not, then we’re just gonna go through the rest. So I like to start from yeah, just like plant review. This stuff is.
38 00:10:37.240 ⇒ 00:10:45.329 Robert Tseng: it’s kind of just sitting there, I mean, I think this newsletter summary query like.
39 00:10:45.600 ⇒ 00:10:50.325 Robert Tseng: I think we got some feedback. I don’t think this ever got closed out. So
40 00:10:51.140 ⇒ 00:10:53.430 Robert Tseng: I think this is something to do with
41 00:10:53.750 ⇒ 00:11:01.230 Robert Tseng: like product names, something to do with quick events. It’s there I’ve lost contacts here. It’s been like a month. So since this was assigned.
42 00:11:02.630 ⇒ 00:11:03.140 Robert Tseng: is that.
43 00:11:03.140 ⇒ 00:11:11.320 Awaish Kumar: Like, basically, this is done they. They came came back to us in a meeting, and I mentioned that they wanted
44 00:11:11.560 ⇒ 00:11:19.139 Awaish Kumar: to see like initial request was to have a table which we build now they want it to be in in tableau like they want to see it
45 00:11:19.680 ⇒ 00:11:25.830 Awaish Kumar: more like it’s some like want to see some in some interactive
46 00:11:26.280 ⇒ 00:11:31.539 Awaish Kumar: dashboard. What they told us in in one of the standoffs when you are on leave.
47 00:11:33.180 ⇒ 00:11:35.101 Robert Tseng: When I was out. Oh,
48 00:11:38.040 ⇒ 00:11:42.649 Robert Tseng: okay, well, I I mean, I I don’t know of this request. I don’t think it ever got onto
49 00:11:43.440 ⇒ 00:11:49.290 Robert Tseng: our plate. So, but if this is done, then we’ll just close it out. If they wanna ask for later, we can. We can talk. Then.
50 00:11:52.970 ⇒ 00:11:58.309 Robert Tseng: yep, okay data model for?
51 00:11:58.490 ⇒ 00:12:04.540 Robert Tseng: Oh, okay, is this, the is what you’re talking about. Verify, how?
52 00:12:04.900 ⇒ 00:12:10.499 Robert Tseng: Okay? I mean, this is just kind of outdated at this point. So I’m just gonna I’m just gonna assume that
53 00:12:10.934 ⇒ 00:12:22.979 Robert Tseng: member categorization here. This is related to the circle stuff. I believe. No, no, this is just like this one random ad hoc request. I think Annie already kind of handled this before she went out.
54 00:12:23.400 ⇒ 00:12:24.939 Robert Tseng: So I think this is done.
55 00:12:25.800 ⇒ 00:12:27.250 Robert Tseng: Oh, okay.
56 00:12:27.250 ⇒ 00:12:29.424 Annie Yu: That’s actually done by Dumadi.
57 00:12:30.560 ⇒ 00:12:31.400 Robert Tseng: Okay?
58 00:12:34.370 ⇒ 00:12:40.989 Robert Tseng: Oh, well, it was a I don’t. I don’t know how to go back to it at this point.
59 00:12:41.460 ⇒ 00:12:48.820 Robert Tseng: Okay. Channel sources dashboard Mattesh, still waiting on Mattesh.
60 00:12:49.310 ⇒ 00:12:51.479 Robert Tseng: No need, no need to touch that. For now.
61 00:12:51.800 ⇒ 00:12:58.240 Robert Tseng: Okay, vile related things. This is kind of, I think this is kind of urgent
62 00:12:59.090 ⇒ 00:13:04.410 Robert Tseng: this is something that is directly impacting
63 00:13:05.790 ⇒ 00:13:09.100 Robert Tseng: what Rebecca is asking for here. So
64 00:13:09.830 ⇒ 00:13:15.210 Robert Tseng: yeah, they have. A lot of this is probably tied to like 3 or 4 separate tickets here around.
65 00:13:15.970 ⇒ 00:13:19.380 Robert Tseng: Yeah, just is our data model ready to like.
66 00:13:20.250 ⇒ 00:13:27.470 Robert Tseng: answer this question support forecasting. Basically, do we have file size ready to go at the order level.
67 00:13:28.530 ⇒ 00:13:34.240 Demilade Agboola: I mean, we have it to go, but we don’t have it to go at a a
68 00:13:34.390 ⇒ 00:13:36.789 Demilade Agboola: how do I put it? So? Number one.
69 00:13:36.940 ⇒ 00:13:40.010 Demilade Agboola: Whatever vowels we’re looking at have to be similar.
70 00:13:40.220 ⇒ 00:13:45.510 Demilade Agboola: So that’s 1 limitation 2. We’re only categorizing Sema
71 00:13:45.720 ⇒ 00:13:52.119 Demilade Agboola: from after the 1st of April. So that means if there were summer products that were launched before that.
72 00:13:52.701 ⇒ 00:13:58.669 Demilade Agboola: We don’t have that in the sheet to then calculate the valve sizes. So that’s another limitation.
73 00:13:59.070 ⇒ 00:14:07.669 Demilade Agboola: And then the 3rd limitation is even within our current sheet of semi variants. There were some that we don’t have the
74 00:14:07.930 ⇒ 00:14:12.060 Demilade Agboola: flow of like how the the valve, sizes and the
75 00:14:12.310 ⇒ 00:14:16.240 Demilade Agboola: like, the sending frequency and all that works. So
76 00:14:16.630 ⇒ 00:14:22.809 Demilade Agboola: we’re looking at like it does exist. But it does exist for a subset. It doesn’t exist for the entirety.
77 00:14:24.630 ⇒ 00:14:26.350 Robert Tseng: Okay, yeah, I mean.
78 00:14:26.920 ⇒ 00:14:30.650 Robert Tseng: some of products are the majority of their business. So I think that will kind of work.
79 00:14:31.655 ⇒ 00:14:45.184 Robert Tseng: If you could respond to Rebecca. Let her know what you’re basically what you just told me. Like what the constraints are. You could probably pull what you said out of the meeting notes from this zoom call later on. So but yeah, just like, let her know what we can and can’t answer.
80 00:14:48.675 ⇒ 00:14:49.710 Robert Tseng: Yeah.
81 00:14:50.110 ⇒ 00:14:54.029 Demilade Agboola: Okay, actually, another way to to look at it would be
82 00:14:55.080 ⇒ 00:15:03.649 Demilade Agboola: if we look, I mean, but there’s no val sizes I was thinking about like using the treatments, but then there are no valve sizes there, so that idea doesn’t work.
83 00:15:05.940 ⇒ 00:15:08.030 Robert Tseng: Yeah, I
84 00:15:10.810 ⇒ 00:15:21.209 Robert Tseng: yeah, I think. Just let her know what we can can’t do. She’ll probably come back to you and be like, why can’t we do this product, that product, or whatever? And there’s gonna be a back and forth? But
85 00:15:21.640 ⇒ 00:15:26.090 Robert Tseng: yeah, I think that’s that’s something that we can answer her on
86 00:15:29.810 ⇒ 00:15:33.749 Robert Tseng: So what I do with this. So I just leave it here like this is just gonna sit here.
87 00:15:35.330 ⇒ 00:15:38.199 Demilade Agboola: I mean, like I said this, this
88 00:15:39.010 ⇒ 00:15:42.550 Demilade Agboola: it does exist like, I said, it’s just about like
89 00:15:42.840 ⇒ 00:15:46.279 Demilade Agboola: the how much does it cover
90 00:15:46.940 ⇒ 00:15:57.669 Demilade Agboola: have pushed into production. It does actually exist and is running right now. But it’s not just as expansive as I personally would like it to be.
91 00:15:58.410 ⇒ 00:15:58.850 Robert Tseng: Yeah.
92 00:15:59.700 ⇒ 00:16:00.800 Demilade Agboola: Great. Yeah.
93 00:16:02.220 ⇒ 00:16:27.160 Robert Tseng: Okay? Well, yeah, I mean, just yeah. You let her know what we can and can’t do. There, I think this is just close out like we may need a v 2 later, we have to update. This sounds like we don’t have all the products. And obviously there’s some limitations before April or whatever, but for the most part, like we we did. I mean we did push. We we started this 2 months ago, and like something like we have already kind of brought brought it to production in some way. So we’re just kind of live with that.
94 00:16:28.500 ⇒ 00:16:43.229 Robert Tseng: Okay, other things. So tracking plan competition. That stuff is kind of in progress. Data requests. I’m just keeping a log of things that I’m not having this team take on anything about random tool integrations. Because.
95 00:16:43.520 ⇒ 00:17:06.339 Robert Tseng: you know, I’ve reviewed kind of like the budget that we’re given. Just for context like this team is, you know, we? We have a 50 $50,000 a month budget. And so the way that I manage this team is like, yeah, part of that obviously goes into paying all of you, but then also goes into the tooling and whatever. So I’m incentivized to try to like. And then they also have.
96 00:17:06.430 ⇒ 00:17:19.800 Robert Tseng: They also say that 10% of of like their engineering time is actually being used for data related work which I do not believe. But you know, that’s there’s nothing I can really do about that. So I’m just keeping a log of like what
97 00:17:20.200 ⇒ 00:17:21.250 Robert Tseng: we have.
98 00:17:22.450 ⇒ 00:17:38.769 Robert Tseng: I think, obviously, by given that budget. My objective is to maximize the biggest share for our team. So as much as we can cut tooling costs, and also to actually like deflect work, so that we only stay focused on things that we want to do.
99 00:17:39.066 ⇒ 00:17:58.039 Robert Tseng: That’s how I’m that’s the leverage that I have now. So if you see these random requests, and I’m on. I’m in slack kind of defending you all, or like trying to like not get us involved in certain things like these are a couple of examples of of things that I don’t want us to do. That I feel like
100 00:17:58.060 ⇒ 00:18:00.050 Robert Tseng: I mean, nobody has told me
101 00:18:00.090 ⇒ 00:18:07.500 Robert Tseng: otherwise. So I’ve just been saying, no, we’re not going to do these things. So if anyone is wondering, that’s why that’s there.
102 00:18:09.790 ⇒ 00:18:29.980 Robert Tseng: yeah. And then for day on this refactoring. I I guess it’s not super important. So I don’t want to cover this. I also want to spend the rest of the time. So I’m just gonna quickly scan through the rest anything else that needs to be called out. I’m not gonna talk about these now things that are in testing. I think this is, you know, a single slack channel
103 00:18:30.390 ⇒ 00:18:43.560 Robert Tseng: report that they wanted set up. I believe that this came from Katie, so if you could just kind of give her an update there that I’d I think she’d appreciate that because they’re so undergoing like a there’s like a big pharmacy kind of like.
104 00:18:43.650 ⇒ 00:19:06.280 Robert Tseng: Blow up right? I think you can tell Booth. One’s been overcharging. Their performance is actually lower than they think. I did see some threads around like, Hey, how you know. Maybe Annie, fixing the report yesterday, didn’t actually move the the average so much in terms of like average turnaround time. But yeah, I don’t know. Is there anything else you want to call out about that? That point?
105 00:19:09.320 ⇒ 00:19:09.750 Demilade Agboola: Yeah.
106 00:19:09.750 ⇒ 00:19:11.309 Robert Tseng: Believe it was. Yeah.
107 00:19:11.690 ⇒ 00:19:21.189 Demilade Agboola: I did. I remember asking any about that, if the average, because the average seemed to be quite like consistent even with the change to it.
108 00:19:21.940 ⇒ 00:19:23.080 Demilade Agboola: And so.
109 00:19:26.220 ⇒ 00:19:29.912 Robert Tseng: I find the thread that you and Annie were talking about this on. But
110 00:19:30.690 ⇒ 00:19:35.649 Robert Tseng: yeah, I guess you guys know what I’m talking about. So, okay, so we did shift.
111 00:19:35.910 ⇒ 00:19:38.550 Robert Tseng: We shifted. I mean
112 00:19:39.170 ⇒ 00:19:46.890 Robert Tseng: is, I mean, we expand the denominator. Does the denominator match like what the pharmacy team thinks, I think, is kind of like the 1st question.
113 00:19:47.130 ⇒ 00:19:50.609 Robert Tseng: If you want to validate that, you know, I would go into bask.
114 00:19:51.220 ⇒ 00:19:56.780 Robert Tseng: and you can sign in. We have all the same creds. I would just pick orders
115 00:19:56.930 ⇒ 00:19:59.080 Robert Tseng: like, look at orders that were placed
116 00:20:02.600 ⇒ 00:20:12.590 Robert Tseng: like, I just wanna make sure that we are like on the same page as what the pharmacy thinks they’re going to look at the orders. That were placed in the past. I don’t know. We just pick like a
117 00:20:13.660 ⇒ 00:20:18.820 Robert Tseng: random period of time. Huh!
118 00:20:18.990 ⇒ 00:20:25.100 Robert Tseng: Feel like this. Ui has changed. Or maybe I just haven’t logged into this in so long that I don’t exactly know how to
119 00:20:25.460 ⇒ 00:20:26.909 Robert Tseng: get to that view.
120 00:20:28.450 ⇒ 00:20:30.389 Robert Tseng: This doesn’t look the same
121 00:20:32.160 ⇒ 00:20:37.960 Robert Tseng: But anyway, like, I think you can get some sort of export out of this.
122 00:20:38.641 ⇒ 00:20:43.040 Robert Tseng: I mean, if you don’t want to do it from the Ui, you can reference our data. I just
123 00:20:43.550 ⇒ 00:20:55.580 Robert Tseng: before we go back to the pharmacy team and say, like, Hey, actually didn’t actually we? There was no, there was no change. I just want to make sure that we’re sure. That’s that’s my whole point of this. We should check. We should check, invest. We should check in our.
124 00:20:55.900 ⇒ 00:21:02.550 Robert Tseng: you know, we have data models that are hooked up to bask data as well. So like.
125 00:21:02.900 ⇒ 00:21:08.899 Robert Tseng: I, I just want a couple couple of different checks before we go back to the pharmacy team and tell them that
126 00:21:10.550 ⇒ 00:21:19.740 Robert Tseng: they’re like, we’re basically telling them they’re wrong. They think that there’s a 20% difference in turnaround time. And if we stand by
127 00:21:20.350 ⇒ 00:21:33.930 Robert Tseng: our number that it’s 78% and not 50 ish percent. That’s a big difference. And people are going to react strongly to that. So I just want us to be sure that we’re able to defend that.
128 00:21:35.621 ⇒ 00:21:42.290 Annie Yu: Okay, I’ll I’ll go into bask and validate. And one thing to note, the denominator actually didn’t change, because
129 00:21:42.720 ⇒ 00:21:49.930 Annie Yu: the original setup was already what they wanted, the denominator and numerator. But I realized that
130 00:21:50.130 ⇒ 00:21:58.480 Annie Yu: the chart was using a date filter that’s based on order date. So I now change it to based on
131 00:21:58.810 ⇒ 00:22:00.709 Annie Yu: sent to pharmacy date.
132 00:22:00.830 ⇒ 00:22:05.459 Annie Yu: I think that was probably what caused the denominator shift
133 00:22:06.110 ⇒ 00:22:10.520 Annie Yu: because it was tied to order date filter, but
134 00:22:10.740 ⇒ 00:22:19.620 Annie Yu: in terms of the setup, like denominator and numerator. They they are, they are! What was expected.
135 00:22:25.010 ⇒ 00:22:31.299 Robert Tseng: I do not know which ticket this is. I’m sorry, but this is.
136 00:22:31.300 ⇒ 00:22:39.240 Annie Yu: So it’s called, add absolute order value, is it.
137 00:22:40.090 ⇒ 00:22:42.070 Robert Tseng: Oh, okay.
138 00:22:44.880 ⇒ 00:22:54.989 Robert Tseng: yeah. I guess if you’re working on it, just put put into in progress, because I I usually don’t look at to do when I’m when I’m scanning things. Yeah. So.
139 00:22:54.990 ⇒ 00:22:56.980 Annie Yu: Even after the change I validate.
140 00:22:57.690 ⇒ 00:23:00.349 Robert Tseng: So it dropped by about 10%. I see.
141 00:23:00.778 ⇒ 00:23:05.060 Annie Yu: Wait. No, that was just taking one week, for example.
142 00:23:05.280 ⇒ 00:23:10.839 Annie Yu: But I’m not sure. What is that? 78%? Is there a time range for that?
143 00:23:11.480 ⇒ 00:23:13.200 Robert Tseng: I think they were just.
144 00:23:13.910 ⇒ 00:23:22.439 Robert Tseng: It probably was just looking at a week. I’m assuming if this is June 8, th maybe they were just looking at the past week. So they saw the 78%. Right? That’s what I’m saying. Here.
145 00:23:22.730 ⇒ 00:23:23.410 Annie Yu: Yeah.
146 00:23:23.920 ⇒ 00:23:24.560 Robert Tseng: Yeah.
147 00:23:25.310 ⇒ 00:23:26.280 Robert Tseng: Okay.
148 00:23:26.881 ⇒ 00:23:34.599 Robert Tseng: yeah. So please, like, just communicate with them in that thread, I think. I think they’re probably confused, too. Like, I wouldn’t say.
149 00:23:34.700 ⇒ 00:23:43.559 Robert Tseng: you know, as the data team, we need to be like trust what they say, but verify like I don’t. I don’t automatic. I don’t assume that anybody knows what they’re talking about.
150 00:23:43.760 ⇒ 00:23:44.140 Annie Yu: Basically.
151 00:23:44.140 ⇒ 00:23:45.843 Robert Tseng: Point I think.
152 00:23:46.630 ⇒ 00:23:47.330 Annie Yu: Hmm.
153 00:23:47.830 ⇒ 00:23:51.730 Robert Tseng: Yeah, there’s I know that we’ve set some stuff here.
154 00:23:52.010 ⇒ 00:23:54.630 Robert Tseng: Where? Where is that main thread? Is it?
155 00:24:01.520 ⇒ 00:24:04.239 Robert Tseng: my goodness, there’s too many things here.
156 00:24:09.130 ⇒ 00:24:18.079 Robert Tseng: I told tomorrow, okay, there we go. This is this is the thread. So
157 00:24:19.190 ⇒ 00:24:27.560 Robert Tseng: yeah, high visibility. The CEO is looking at this. So I wanna make sure that we do get very crisp answer to them. I did say that we would give them an answer by tomorrow.
158 00:24:27.710 ⇒ 00:24:37.919 Robert Tseng: We did make some changes based on tableau based on our meeting with Sarah, which I gave you the notes on. So yeah. And if you could just own the follow up on this. I think this is what it would. Yeah, like.
159 00:24:38.080 ⇒ 00:24:45.199 Robert Tseng: whatever your findings were here like, communicate with them. There may be some back and forth. But I I yeah, I’m I’m a bit.
160 00:24:47.740 ⇒ 00:25:03.050 Robert Tseng: But yeah, I I just, there’s gonna be disagreement. We’re because they they don’t. I mean, we may not be wrong. But, like I I just, I just think that maybe we just have to spend some time with them telling them how things are calculated. Why, like things got shifted around. I,
161 00:25:03.330 ⇒ 00:25:18.610 Robert Tseng: instead of going off of order, created date and going towards set to pharmacy date. That makes sense to me. I’m surprised that the dealt like the denominator didn’t shift very much, but I haven’t looked at the data so I can’t. Really. I don’t have a really a point of view on that.
162 00:25:20.870 ⇒ 00:25:25.710 Annie Yu: So they never send a sample. Sla report. Is that correct?
163 00:25:26.445 ⇒ 00:25:36.320 Robert Tseng: Yeah, no, I don’t think it did. I don’t exactly know how Sarah came up with her 50% number, I think, or yeah. She looked at this. You can just read her comments here. She said that she looked at something.
164 00:25:36.620 ⇒ 00:25:41.870 Robert Tseng: ran it again. Whatever that means. She saw 58% somewhere. So.
165 00:25:42.227 ⇒ 00:25:46.160 Annie Yu: So the only other thing I can validate against is bask.
166 00:25:47.680 ⇒ 00:25:52.129 Robert Tseng: Yeah, so you can validate against fast. I will also say that like well.
167 00:25:52.910 ⇒ 00:26:16.520 Robert Tseng: I I don’t know how this factors into it, but because we don’t. The the even the like the events like sent to pharmacy, is not a live web hook. Right? We we still live in this world where at web hooks come to us in consistent timeframes. So it’s possible that, like a batch of set to pharmacy, orders got like added later on. So when she looked at that week, it only looked at 58%.
168 00:26:16.530 ⇒ 00:26:23.597 Robert Tseng: But now, if we look at that same week, maybe it’s higher than 58, I would guess. So cause that that makes sense. So
169 00:26:23.920 ⇒ 00:26:36.979 Robert Tseng: I think maybe she doesn’t understand that as well. Thinking that it’s always like a live or like real time kind of thing, where, like, I think, dame, a lot. And I looked at this this probably a couple of months ago, but we were saying something like.
170 00:26:37.247 ⇒ 00:26:54.879 Robert Tseng: web hooks maybe have like a 10 to 14 day window before they actually settle in. So like we don’t know when the event will fire, but it will fire within 10 to 14 days of it actually happening. And we’re when we were pretty sure of that, you know. Keep, make sure that that’s what if you recall that? I think that was our conclusion.
171 00:26:55.210 ⇒ 00:27:01.660 Robert Tseng: So maybe, like what we can only tell them is like, Hey, like this is only really accurate, you know, if you look.
172 00:27:01.950 ⇒ 00:27:11.420 Robert Tseng: you know, 2 weeks back, because and it’s gonna always gonna be a lagging indicator just because of the nature of how the web hooks come to us like I I don’t know. Maybe that is the answer.
173 00:27:14.580 ⇒ 00:27:18.509 Demilade Agboola: Yeah, but I I won’t. I saw, like some of the
174 00:27:18.720 ⇒ 00:27:23.429 Demilade Agboola: results that Annie got. I know they seem to be expecting about 50%.
175 00:27:23.858 ⇒ 00:27:34.029 Demilade Agboola: We’re getting 78%. And then some weeks back, I know there was like, maybe 2 weeks back, there was a 58, 56% there about. But then, further back.
176 00:27:34.250 ⇒ 00:27:38.899 Demilade Agboola: there was a 70, something percent like there was also higher, like, you know.
177 00:27:39.180 ⇒ 00:27:43.300 Demilade Agboola: So I’m guessing we would just need to settle on
178 00:27:43.880 ⇒ 00:27:52.703 Demilade Agboola: everything like what they’re looking for from bask. And I can look at that today. But basically what they’re looking for from Basque, the the raw numbers.
179 00:27:53.910 ⇒ 00:27:56.860 Demilade Agboola: we can also look at the like.
180 00:27:57.030 ⇒ 00:28:08.580 Demilade Agboola: Turn around like we can now calculate the turnaround time based off those raw numbers. We can see the total orders they’re looking at are way less than the numbers that you know bask seems to have from the Csv. That we just downloaded
181 00:28:09.329 ⇒ 00:28:17.709 Demilade Agboola: and that allows us to be able to do that sort of comparative analysis. Also, we do filter out certain states. Maybe they don’t filter out states.
182 00:28:18.233 ⇒ 00:28:26.009 Demilade Agboola: So we don’t look at things like canceled error abandoned potentially. Maybe that could factor into how they calculated on their end.
183 00:28:26.589 ⇒ 00:28:38.449 Demilade Agboola: So yeah, just trying to get the just clearly define what our process is, what we are eliminating, and just confirm the raw numbers compared to bask.
184 00:28:38.610 ⇒ 00:28:44.700 Demilade Agboola: When you do the same thing on their data from the Csv. And once those match, then it’s I think we should be fine.
185 00:28:45.820 ⇒ 00:28:57.560 Robert Tseng: Okay, yeah, no. I think that’s that sounds like a perfect plan. I think. Yeah, I don’t want you guys spending your time clicking around the basket. Ui! If anything, just get Sarah on a call. Just tell her to tell you how she got to this 58%.
186 00:28:57.981 ⇒ 00:29:17.380 Robert Tseng: She needs to be able to reproduce it because she’s the one that raised it. And then, yeah, I think sounds like we’re pretty clear on, like how everything’s calculated on our end, so we can walk them through like what we’re excluding, what? Not. What could factor into the variance there. So yeah, I think I you know, whoever what one of you or I mean.
187 00:29:17.420 ⇒ 00:29:27.969 Robert Tseng: yeah, like, you guys, couldn’t. You guys couldn’t meet with Sarah and let her know and then, just like, you know, CC. Rebecca, because she’s she’s got her eyes on this on this one.
188 00:29:30.560 ⇒ 00:29:31.940 Robert Tseng: Okay, cool.
189 00:29:32.930 ⇒ 00:29:51.170 Robert Tseng: took a while. I know we didn’t get through everything, but with the remaining time I may go a bit over. But if you guys gotta drop feel free to I wanna kind of just catch up on the work that was done here. So I did post like this really long video, it’s not really related to maybe, and
190 00:29:51.775 ⇒ 00:30:00.939 Robert Tseng: any right now, but I think just to kind of do it. I would just I would watch it, anyway, just to get a recap of this type of work.
191 00:30:01.190 ⇒ 00:30:03.950 Robert Tseng: the work that we’re doing here I was.
192 00:30:04.090 ⇒ 00:30:15.849 Robert Tseng: and I still may bring in somebody else to run this, but because we have like a time crunch, and we couldn’t agree on bringing on like where they’re gonna bring on another person in house to go and run this
193 00:30:16.487 ⇒ 00:30:32.552 Robert Tseng: but I’m still kind of negotiating with him, I guess. So. He’s not here yet, or we were gonna go with a partner, which I also that those conversations fell through so for any. In any case, I ended up running this. So
194 00:30:33.300 ⇒ 00:30:34.860 Robert Tseng: we have
195 00:30:35.210 ⇒ 00:30:39.507 Robert Tseng: 4 objectives. I don’t expect all of them to be accomplished by the end of the month.
196 00:30:40.080 ⇒ 00:31:08.940 Robert Tseng: and I think this is just like a high level, like, you know, understanding of, like what we’re trying to get to. I think realistically, the the main urgent, the urgency that’s driving this decision is the tooling decision. Whether or not like how we’re going to handle the segment renewal. And so yeah, the main objective that I want. I I do the overview of the entire process. And then I kind of talked extensively about the second objective here in the video. And so I think a wish. This is really need you to kind of
197 00:31:09.282 ⇒ 00:31:25.670 Robert Tseng: absorb that information. What I’ve done here is I’ve gone to segment. I’ve pulled out all the traits that are currently in there. There’s about 350. Not all of them are active. And I would say, the main problem is that from a customer data model perspective, we have 3 different sources of truth.
198 00:31:25.770 ⇒ 00:31:48.140 Robert Tseng: We have customer I/O, which has its own set of of traits that you know. Bobby just randomly went to Rob, and he directly put some stuff in there. There’s a lot of like lost context there. I don’t exactly know where all that all those traits come from, but it seems like there’s about 40 active traits in there. Out of a pool of around 200. I don’t really know how these came about.
199 00:31:48.370 ⇒ 00:31:59.099 Robert Tseng: Then we have what’s in segment. Segment has about 3 50 super wide. Impossible to maintain. A lot of it is not really being used. The only tool that’s really using the segment profiles is mixed panel.
200 00:31:59.480 ⇒ 00:32:14.801 Robert Tseng: which is not. I mean it is live, but not many people use it. And then, with our within our own customer data model like 10 customers. In bigquery. I’d like, like I mentioned to you a wish like we are
201 00:32:17.280 ⇒ 00:32:20.810 Robert Tseng: customer data control.
202 00:32:23.770 ⇒ 00:32:24.560 Robert Tseng: Hold on.
203 00:32:25.990 ⇒ 00:32:32.860 Robert Tseng: I don’t like how notion doesn’t let doesn’t preserve where I stand. Okay.
204 00:32:33.090 ⇒ 00:32:40.769 Robert Tseng: yeah. So yeah, in our own models, like the data comes from bask. And then there’s like one random like field that comes from Facebook.
205 00:32:41.130 ⇒ 00:32:49.500 Robert Tseng: So big issue there like these are not gonna tie out. Obviously, I think
206 00:32:50.420 ⇒ 00:32:54.179 Robert Tseng: that’s that’s probably like the biggest problem that we need to solve.
207 00:32:55.860 ⇒ 00:33:01.419 Robert Tseng: Discuss Cdp work at 1130. Did I throw that call on sorry.
208 00:33:01.420 ⇒ 00:33:02.000 Awaish Kumar: I didn’t.
209 00:33:02.490 ⇒ 00:33:03.740 Robert Tseng: Oh, you did it. Okay.
210 00:33:04.000 ⇒ 00:33:16.360 Robert Tseng: cool. Well, then, in that case, yeah, I guess if people want to drop off, you can drop off and wish we can stay on. We can keep. We can keep talking about it. But I were to just kind of wrap up with this. So it stays relevant
211 00:33:16.792 ⇒ 00:33:24.450 Robert Tseng: where Dame a lot and and Annie will probably be more helpful, is in the sec. And one of the other objectives.
212 00:33:24.886 ⇒ 00:33:34.379 Robert Tseng: Demo. I know you asked Bobby, about some treatment stuff like, how do we handle treatments and orders? I was thinking, well, we have order, summary model, and we have.
213 00:33:34.670 ⇒ 00:33:42.119 Robert Tseng: I don’t really know what the patient journey dashboard is on honestly, but I think we need to. Now that we have treatment level.
214 00:33:42.730 ⇒ 00:33:50.960 Robert Tseng: granularity. We need to have like a treatment journey. Summary model, something like that. If you want to review this, you can. You can take a look. I kind of just
215 00:33:51.150 ⇒ 00:33:56.329 Robert Tseng: did some light brainstorming here around. Okay? Well, what’s a way that we can?
216 00:33:57.240 ⇒ 00:34:04.231 Robert Tseng: you know, tie every stage of the patient journey together in a way that’s the most helpful for lifecycle.
217 00:34:04.930 ⇒ 00:34:23.629 Robert Tseng: yeah, like, right now, it’s it’s purely off of like order events, I guess. And so I think now that we have orders and treatments able to be matched, we can really think about like a treat, a full life treatment journey. And what the different components are. I I think that this is
218 00:34:23.800 ⇒ 00:34:30.259 Robert Tseng: probably the direction we would need to head in in order to build a model that’s more useful for Bobby’s replacement.
219 00:34:30.898 ⇒ 00:34:32.970 Robert Tseng: But yeah, I think
220 00:34:33.370 ⇒ 00:34:43.230 Robert Tseng: I you know, I want you to kind of just review that. Give me your point of view on on this 1st tldr. It’s a site more expansive order summary model.
221 00:34:43.440 ⇒ 00:34:54.460 Robert Tseng: And then with Annie. This is all customer. I/O stuff that’s not necessary. But yeah, we’ll definitely lean on you more on the reporting side. Once we actually have
222 00:34:55.310 ⇒ 00:35:15.520 Robert Tseng: this new data model exposed in customer I/O, we need to be thinking about like, oh, how are we measuring like? You know, in improvements like, pretty much right? So I think there are a couple of core metrics that are in this document. If you look at it of things that we would want to measure, change over time.
223 00:35:15.560 ⇒ 00:35:29.540 Robert Tseng: such as repurchase rates and like, we can kinda add, I’ve listed out a few other things here. But that’s that’s why I’m not involving you super early or like as early right now, because it’s really just like
224 00:35:29.850 ⇒ 00:35:35.718 Robert Tseng: infrastructure and setup, and whatever right now, and not really much to report on. So
225 00:35:36.430 ⇒ 00:35:38.470 Robert Tseng: that’s where we’re at with this.
226 00:35:39.250 ⇒ 00:35:55.219 Robert Tseng: yeah, if you have any questions, you know, let me know. I guess wish, and I will jump into another call, and that’ll be recorded as well. So you can kind of follow along there. But yeah, that’s that’s what I wanted to update everyone on on what I’ve been working on this week.
227 00:35:59.780 ⇒ 00:36:03.520 Demilade Agboola: Sounds good. I look forward to seeing the the summary of the other call.
228 00:36:04.280 ⇒ 00:36:05.862 Robert Tseng: Okay, cool. Alright.
229 00:36:06.847 ⇒ 00:36:10.009 Robert Tseng: Yeah. I wish let’s just jump to the other call. So we can have like a
230 00:36:10.410 ⇒ 00:36:12.359 Robert Tseng: a recording of of that.
231 00:36:13.060 ⇒ 00:36:13.990 Awaish Kumar: Okay. Thank you.
232 00:36:13.990 ⇒ 00:36:15.569 Robert Tseng: Okay. Alright. See? You.