Meeting Title: [Eden] Daily Standup Date: 2025-05-01 Meeting participants: Annie Yu, Demilade Agboola, Robert Tseng, Josh
WEBVTT
1 00:04:29.100 ⇒ 00:04:30.050 Demilade Agboola: Alright, everyone.
2 00:04:34.130 ⇒ 00:04:35.100 Annie Yu: Hello!
3 00:04:35.520 ⇒ 00:04:36.860 Robert Tseng: Yeah, anything about it.
4 00:04:47.020 ⇒ 00:04:50.190 Robert Tseng: hey? I’ll share my screen.
5 00:04:54.731 ⇒ 00:04:59.450 Robert Tseng: Yeah, any I per se, I’ll say, is, I’m like
6 00:05:00.480 ⇒ 00:05:04.280 Robert Tseng: reviewing your pharmacy dash work and then trying to like.
7 00:05:04.820 ⇒ 00:05:08.889 Robert Tseng: get to a v 2. 0, it’s just a lot of like
8 00:05:09.840 ⇒ 00:05:17.780 Robert Tseng: I don’t know how clearly I’m writing it out. So I I try. And I’m gonna just try to cover some of that on this call.
9 00:05:19.980 ⇒ 00:05:23.640 Robert Tseng: But yeah, I’m also trying to follow up and get some more
10 00:05:23.780 ⇒ 00:05:39.449 Robert Tseng: clarity. I mean, I’ll start with like, yeah, this was great good good walk through. I understand the 2 tabs very clearly. There’s like a order workflow tab. There’s a customer tickets, metrics. Tab think those are great.
11 00:05:39.620 ⇒ 00:05:43.229 Robert Tseng: If I look back at the pharmacy analytics Channel.
12 00:05:43.850 ⇒ 00:05:50.810 Robert Tseng: you know, Rebecca just kind of throws a bunch of ideas out there. And it’s not like that helpful. So we’re trying to like.
13 00:05:52.710 ⇒ 00:05:55.820 Robert Tseng: yeah, just a lot of it. The design
14 00:05:55.920 ⇒ 00:06:10.319 Robert Tseng: we’re just, you know, I I feel like there’s a lot of work to kind of get like to extract. What is she really looking for? What like? How do I really think from her perspective? And yeah, it’s just anyway, I I do think that
15 00:06:11.078 ⇒ 00:06:16.440 Robert Tseng: we need a couple more tabs in that dashboard.
16 00:06:16.660 ⇒ 00:06:28.280 Robert Tseng: So what I’ve been doing is I’ve been creating another ticket for that to try to describe what those 2 tabs are. There’s another link ticket to this as well. I’m calling
17 00:06:28.830 ⇒ 00:06:29.490 Robert Tseng: bye.
18 00:06:31.090 ⇒ 00:06:32.040 Robert Tseng: So
19 00:06:32.430 ⇒ 00:06:47.359 Robert Tseng: I originally thought we could just update one of the sections, but I think that that’s probably not the best way to approach it. We should just create like a separate tab for it. If you have questions, you know, we can, we can talk about it.
20 00:06:47.780 ⇒ 00:06:50.290 Robert Tseng: But yeah, the idea is really just
21 00:06:56.340 ⇒ 00:06:57.170 Robert Tseng: yeah.
22 00:06:57.530 ⇒ 00:07:04.930 Robert Tseng: So this section is everything from like order completed to deliver right? And
23 00:07:05.640 ⇒ 00:07:22.710 Robert Tseng: yeah, we broke it out more granularly than when she asked for she was thinking that she just wanted to see order to deliver it. Whether or not it’s taking more 5, more, or less than 5 days, I think what we’ve put out is is better than when she asked for. And Josh kind of sign up on that. So no issues with that.
24 00:07:24.010 ⇒ 00:07:26.759 Robert Tseng: I think what this doesn’t talk about, though, is.
25 00:07:27.980 ⇒ 00:07:35.020 Robert Tseng: you know, we’re it’s there’s not really like a specific focus on like the free
26 00:07:36.585 ⇒ 00:07:48.839 Robert Tseng: or like this is just like a view from the order journey. And I think what’s missing is we need something that’s focusing on the prescription process. So like when
27 00:07:49.798 ⇒ 00:08:03.400 Robert Tseng: like a when an order is placed, a doctor kind of receives the order they have to make some sort of prescription before it gets sent to the pharmacy. And so there’s like this.
28 00:08:03.510 ⇒ 00:08:17.419 Robert Tseng: I guess it’s the part between when the order is created to when it’s sent to the pharmacy. There’s some level of granularity there that we don’t. We don’t see currently, with this view. So I think that’s what one of the tabs is going to be focused on.
29 00:08:18.370 ⇒ 00:08:22.989 Robert Tseng: and then the other one is just more general, like from a patient perspective.
30 00:08:23.533 ⇒ 00:08:26.360 Robert Tseng: And that’s what I tried to describe
31 00:08:26.560 ⇒ 00:08:32.300 Robert Tseng: here in this ticket. Where the question is like.
32 00:08:32.370 ⇒ 00:08:57.949 Robert Tseng: how do I find patients that have refilled a particular drug broken out by pharmacy? And I mean, file size is not a real. It’s just like quantity. So we kind of have, like an order table with most of the right filters. But it’s just trying to like, get a view of, like the patient, like a top line view of the patient. You know how many patients are refilling.
33 00:08:59.100 ⇒ 00:09:02.259 Robert Tseng: and over the course you know in
34 00:09:02.500 ⇒ 00:09:06.429 Robert Tseng: of like the past 7 days. 30 days, 60 days. Kind of thing.
35 00:09:07.220 ⇒ 00:09:09.810 Robert Tseng: So that’s like the second set of.
36 00:09:10.490 ⇒ 00:09:13.310 Robert Tseng: we got a second view that I think we need to
37 00:09:13.570 ⇒ 00:09:15.900 Robert Tseng: like, bring bring out of here. So
38 00:09:16.900 ⇒ 00:09:24.159 Robert Tseng: I know that was like, kind of abstract. I tried to like, create some level of distinction there.
39 00:09:24.680 ⇒ 00:09:26.860 Robert Tseng: Yeah, is there anything that we can just like
40 00:09:27.000 ⇒ 00:09:30.569 Robert Tseng: talk through to help you get a better sense of what I’m describing.
41 00:09:31.611 ⇒ 00:09:38.580 Annie Yu: So one question I know that you said the second tab we want to focus on is between when order placed
42 00:09:38.870 ⇒ 00:09:42.450 Annie Yu: and to send to pharmacy? And do we have
43 00:09:42.610 ⇒ 00:09:47.020 Annie Yu: any data or visibility within that period?
44 00:09:47.770 ⇒ 00:09:55.630 Robert Tseng: So I don’t fully know, like I think, within, like these order statuses that she had sent.
45 00:09:55.740 ⇒ 00:10:04.559 Robert Tseng: There’s like some stuff here like error, doctor Error. Consult only I’m imagining that these
46 00:10:04.760 ⇒ 00:10:11.770 Robert Tseng: these are I mean, we don’t. We don’t use any of those statuses in here currently, but we we surely we must. We must get this data somehow.
47 00:10:12.350 ⇒ 00:10:13.120 Robert Tseng: Wow.
48 00:10:13.560 ⇒ 00:10:20.560 Robert Tseng: yeah, that’s why I don’t know if it’s in this in the model. Right now, I think Tim lade maybe can answer a bit more from the order. Summary perspective.
49 00:10:21.070 ⇒ 00:10:22.150 Robert Tseng: That’s
50 00:10:22.300 ⇒ 00:10:27.640 Robert Tseng: yeah. That’s like a Oh, we. We may not be able to do this right now, as I guess, my my answer.
51 00:10:28.080 ⇒ 00:10:33.887 Demilade Agboola: Isn’t this the bit where, like kind of where we need transactions, data where we can then go?
52 00:10:34.210 ⇒ 00:10:37.489 Josh: 15 is fine. It used to be $15.
53 00:10:37.490 ⇒ 00:10:45.750 Demilade Agboola: Yeah. So isn’t this a bit where we need like transactions, data where we go from like, this is the point where this person paid for this.
54 00:10:45.850 ⇒ 00:10:52.240 Demilade Agboola: and this is the point in which the order status had become like shit.
55 00:10:52.380 ⇒ 00:10:56.900 Demilade Agboola: And so we can then use the disparity and be like that is the doctor’s
56 00:10:57.130 ⇒ 00:11:01.510 Demilade Agboola: the time the doctor used to prescribe, or is that.
57 00:11:03.200 ⇒ 00:11:16.310 Robert Tseng: I I mean, I hear what you’re saying. I don’t think it’s say I think this should all be happening after the order is complete, like order completed is, I mean, I don’t know the exact order of this like, but I’m assuming it’s
58 00:11:16.730 ⇒ 00:11:24.780 Robert Tseng: pending, completed some sequence of this, then sent to pharmacy. So like, I, I don’t know
59 00:11:25.080 ⇒ 00:11:26.480 Robert Tseng: how I would like.
60 00:11:27.400 ⇒ 00:11:35.659 Robert Tseng: yeah, I don’t know what the what the the order of this is, but I don’t. I think this is all after the transaction and after order is already created.
61 00:11:36.410 ⇒ 00:11:43.759 Demilade Agboola: Gotcha, but also we we. Some of these statuses I’ve not seen before. To be honest, I’ve not seen, like doctor’s error or.
62 00:11:43.760 ⇒ 00:11:49.327 Robert Tseng: Yeah, I don’t believe that we’re actually pulling them in through web book. So I don’t really think we can answer that right now.
63 00:11:50.010 ⇒ 00:11:54.889 Robert Tseng: but it’s just like a something that we should we? I don’t know if we end up needing to go and
64 00:11:55.540 ⇒ 00:12:00.700 Robert Tseng: set up another web hook for it, or whatever like but that I think that was part of the
65 00:12:01.080 ⇒ 00:12:02.829 Robert Tseng: what what she wanted as well.
66 00:12:04.590 ⇒ 00:12:23.779 Demilade Agboola: Okay. So I’ll dig in just like the 30 min session, just looking and just kind of feel like if we have that data. If we don’t, then I’ll just like put it up there like, Hey, we don’t have that data and potentially, if it’s something we need to figure out. If it’s a different web hook, or if it’s something that pass needs to set up for us, you know I don’t know. We’ll we’ll figure it out.
67 00:12:24.200 ⇒ 00:12:24.850 Robert Tseng: Yeah.
68 00:12:28.140 ⇒ 00:12:38.149 Robert Tseng: okay, so yeah, I’m not. I don’t have high confidence that we can actually report on this but then the other tab on the patient outcomes piece Andy, do you have any questions on that one?
69 00:12:38.564 ⇒ 00:12:40.639 Annie Yu: About the refill. I know.
70 00:12:40.640 ⇒ 00:12:40.970 Robert Tseng: Yeah.
71 00:12:40.970 ⇒ 00:12:49.230 Annie Yu: Handle refill data yet. So if there’s any columns I should start with. Are we good to know.
72 00:12:51.920 ⇒ 00:13:03.260 Robert Tseng: Yeah, I think within the order data there should be like we should be able to tell if it’s like a net new order. But it’s a refill, I think, even if there isn’t like a field, we should just be able to look at
73 00:13:03.948 ⇒ 00:13:09.890 Robert Tseng: like we. We might have to create a proxy field for that. Yeah.
74 00:13:10.340 ⇒ 00:13:14.760 Robert Tseng: So yeah, I
75 00:13:14.880 ⇒ 00:13:20.009 Robert Tseng: I’ve not. Yeah. I I think this might might take a little bit of digging to to figure out how, how we will.
76 00:13:20.950 ⇒ 00:13:25.640 Robert Tseng: how we will label the we have to be able to label orders on whether or not the refills right.
77 00:13:26.730 ⇒ 00:13:28.030 Annie Yu: Yeah, yeah.
78 00:13:29.260 ⇒ 00:13:30.420 Annie Yu: Okay,
79 00:13:33.060 ⇒ 00:13:44.490 Annie Yu: So before, I guess before we figure out the older status, the doctor prescription status, we can start digging in with the patient perspective.
80 00:13:45.180 ⇒ 00:13:47.879 Robert Tseng: Yeah, I would say you would probably be able to
81 00:13:48.240 ⇒ 00:13:52.089 Robert Tseng: go and investigate this one while, like
82 00:13:52.400 ⇒ 00:14:04.072 Robert Tseng: they have a lot, is kind of looking into the order statuses. There. I’ll like keep working this to try to get it into a format for the ticket, and I was kind of hard to explain.
83 00:14:04.880 ⇒ 00:14:12.819 Robert Tseng: But yeah, it’s it’s tough. Because, like we have. Yeah, I’m like, well.
84 00:14:13.400 ⇒ 00:14:29.509 Robert Tseng: it’s just a matter of like labeling orders the right way. And I don’t really know if we have all those right labels, some like the the patient label, we should be able to calculate with existing fields, and we just have to be able to. We just have to create the refill label. I don’t think we need past data for that.
85 00:14:29.510 ⇒ 00:14:43.609 Robert Tseng: But then for the other order, statuses of like doctor Error versus error. Whatever like. That’s not something we can really, you know, create. So this, it is like we have to go and figure out what we get and what we, what we don’t have.
86 00:14:44.270 ⇒ 00:14:46.060 Annie Yu: Okay, okay?
87 00:14:46.160 ⇒ 00:14:50.062 Annie Yu: And I actually do have a separate question.
88 00:14:50.550 ⇒ 00:15:00.780 Josh: Before you switch topics here. I also want to make sure that on the dashboard that you’re just showing you’re showing all the most up to date pharmacies as well
89 00:15:01.510 ⇒ 00:15:05.290 Josh: that we’re using. So we also are adding in like Olympia
90 00:15:05.690 ⇒ 00:15:11.140 Josh: or not, Olympia. Sorry, Pharmacy Hub, I don’t see Pharmacy hub inside of here.
91 00:15:11.370 ⇒ 00:15:12.030 Josh: Now I do.
92 00:15:12.030 ⇒ 00:15:12.620 Annie Yu: Last, one.
93 00:15:12.710 ⇒ 00:15:18.770 Josh: Yeah, it’s the last and good. You guys got it. Okay, that’s perfect. Okay, perfect.
94 00:15:18.940 ⇒ 00:15:20.640 Josh: Yeah. 3 X is 19.
95 00:15:21.660 ⇒ 00:15:24.289 Josh: Sorry. I’m actually, I’m sitting with.
96 00:15:24.740 ⇒ 00:15:28.369 Josh: I’m sitting with basically this whole team here.
97 00:15:28.780 ⇒ 00:15:29.730 Robert Tseng: Oh, cool!
98 00:15:31.010 ⇒ 00:15:36.480 Josh: Yeah, yeah, so yeah, can, you guys can keep going.
99 00:15:37.020 ⇒ 00:15:37.690 Robert Tseng: Okay.
100 00:15:38.950 ⇒ 00:15:40.840 Josh: I do have some other stuff.
101 00:15:41.160 ⇒ 00:15:51.390 Josh: What is that? Yeah. But anyway, so we do have one other thing, too. So though so. But I know, Annie, you had some other questions, so go ahead, and then I’ll jump in. Towards the end.
102 00:15:52.430 ⇒ 00:16:07.089 Annie Yu: Okay? Yeah. So my questions focus on the second page. So I I just this morning, I realized that data source hasn’t been refreshed since April 20, th
103 00:16:07.300 ⇒ 00:16:13.319 Annie Yu: and I duck into it and saw that because we were using.
104 00:16:13.630 ⇒ 00:16:16.819 Robert Tseng: We use a manual refresh on Zendesk data. That’s that’s why.
105 00:16:16.970 ⇒ 00:16:17.740 Annie Yu: Oh, okay.
106 00:16:17.740 ⇒ 00:16:21.079 Robert Tseng: Away. She’s out of office, so he didn’t refresh it. Probably.
107 00:16:23.108 ⇒ 00:16:27.760 Annie Yu: I don’t think that’s really why I’m asking so.
108 00:16:27.760 ⇒ 00:16:28.660 Robert Tseng: Oh, yeah.
109 00:16:29.199 ⇒ 00:16:30.279 Annie Yu: Data sources
110 00:16:30.440 ⇒ 00:16:39.030 Annie Yu: connected connected to this dashboard. And there’s 1 that’s order summary. So what she did was blending
111 00:16:39.460 ⇒ 00:16:52.710 Annie Yu: order summary with fact tickets with dim agents. So that’s how we could get the ticket level detail but because that’s
112 00:16:52.840 ⇒ 00:16:58.789 Annie Yu: only doable when you directly source from bigquery instead of published.
113 00:16:59.447 ⇒ 00:17:04.479 Annie Yu: We only have extract on that day when she extracted it.
114 00:17:04.859 ⇒ 00:17:08.290 Annie Yu: So that was back on April 20.th
115 00:17:08.690 ⇒ 00:17:10.880 Annie Yu: And so I guess.
116 00:17:11.910 ⇒ 00:17:19.440 Annie Yu: to blend relationships. We can’t. We can’t just publish a data source here with the blending relationship.
117 00:17:19.630 ⇒ 00:17:23.930 Annie Yu: it has to be stay connected to the direct data source
118 00:17:24.611 ⇒ 00:17:41.179 Annie Yu: and with that, bigquery doesn’t let us schedule a refresh daily, and I saw on tableau. There is something called Tableau Bridge, where it would allow you to refresh
119 00:17:41.680 ⇒ 00:17:46.160 Annie Yu: data sources from Bigquery. But I can’t do that.
120 00:17:46.820 ⇒ 00:17:47.630 Annie Yu: My Mac.
121 00:17:47.890 ⇒ 00:17:51.170 Robert Tseng: What if we just took her join and just made it a separate model? And then, just.
122 00:17:51.170 ⇒ 00:17:56.539 Annie Yu: Yeah. So that’s why I’m saying, I think we should do that. We we probably have to do that.
123 00:17:57.030 ⇒ 00:17:57.730 Robert Tseng: Okay.
124 00:17:58.320 ⇒ 00:18:03.260 Robert Tseng: Alright. Well, thanks for catching that. I didn’t. I didn’t realize that. That’s how she did it before.
125 00:18:04.257 ⇒ 00:18:09.959 Robert Tseng: Okay, so I mean, we probably need an additional ticket on that to well.
126 00:18:11.030 ⇒ 00:18:15.136 Annie Yu: Yeah, but I think it’s good that we have that in a separate tab. So.
127 00:18:15.410 ⇒ 00:18:15.740 Robert Tseng: Okay.
128 00:18:15.740 ⇒ 00:18:18.949 Annie Yu: We can hide it before we get it fixed.
129 00:18:19.140 ⇒ 00:18:19.830 Robert Tseng: Okay.
130 00:18:27.950 ⇒ 00:18:32.409 Demilade Agboola: Can we have an idea of what the blend is done on like the joins? And like.
131 00:18:32.590 ⇒ 00:18:36.760 Demilade Agboola: what? Exactly on the
132 00:18:38.490 ⇒ 00:18:43.800 Demilade Agboola: yeah, just like an idea of what the blend is done on. So at least, like when I’m like handling it. I have an idea of what’s going on.
133 00:18:43.970 ⇒ 00:18:50.249 Annie Yu: Yeah, I I’ll try my best to explain that I always avoid it because planned
134 00:18:50.620 ⇒ 00:18:53.919 Annie Yu: behaves more like inner join. But
135 00:18:54.910 ⇒ 00:19:04.440 Annie Yu: so it’s only showing a dimension categorical data from inner join. But it brings all the values across sources.
136 00:19:04.830 ⇒ 00:19:11.099 Annie Yu: So it’s it’s I. I just avoid using it. Rather than
137 00:19:11.210 ⇒ 00:19:17.689 Annie Yu: direct join, and on tableau you can join only 2 tables, but then for this one we had.
138 00:19:18.302 ⇒ 00:19:19.300 Robert Tseng: The 3, yeah.
139 00:19:19.690 ⇒ 00:19:23.649 Demilade Agboola: Yeah, I know, tableau has a limitation. Yeah, yeah, I understand.
140 00:19:24.230 ⇒ 00:19:32.870 Demilade Agboola: has just like a game plan. So like, you, are, you just trying to explain to the best of your ability, like maybe a quick loom like going through and explaining what happens. And then
141 00:19:32.980 ⇒ 00:19:40.359 Demilade Agboola: I would build out the model, put in a staging, you can test it and see like if it works well. If it doesn’t, and if
142 00:19:40.490 ⇒ 00:19:45.929 Demilade Agboola: you give the go ahead, we can just switch direct to the model that I created.
143 00:19:48.990 ⇒ 00:19:51.619 Annie Yu: Okay, yeah. The the join should be easy.
144 00:19:52.140 ⇒ 00:19:53.400 Demilade Agboola: Alright, sounds good.
145 00:19:53.880 ⇒ 00:20:01.790 Robert Tseng: Okay, yeah, I’m just, I’m escalating that one. Just let’s just get that pass because we did share it out and don’t want it to be like out for too long.
146 00:20:02.110 ⇒ 00:20:02.880 Robert Tseng: Okay,
147 00:20:06.010 ⇒ 00:20:16.749 Robert Tseng: alright, let’s just keep going through this. So let’s talk about embeddables update here. I mean, we had Ryan on the call yesterday. I guess. Any any updates there they wanted.
148 00:20:20.045 ⇒ 00:20:20.490 Demilade Agboola: Yes.
149 00:20:20.490 ⇒ 00:20:24.480 Robert Tseng: Segment schema. You saw the data coming to bigquery. Follow the thread. Yeah.
150 00:20:24.480 ⇒ 00:20:38.590 Demilade Agboola: Yeah, so like the data is there? We’ll need to model it. To fit the like, the intake form that we currently have, or like how we form to look like. But yeah, the data is there.
151 00:20:40.040 ⇒ 00:20:40.790 Robert Tseng: Okay?
152 00:20:44.250 ⇒ 00:20:52.009 Robert Tseng: Yeah. I mean, I guess on that note, the mixed panel stuff. Yeah. I mean, I I just tested the events yesterday haven’t actually
153 00:20:52.540 ⇒ 00:20:54.970 Robert Tseng: deployed it yet. But I will today.
154 00:20:55.730 ⇒ 00:20:57.000 Robert Tseng: And then.
155 00:20:57.810 ⇒ 00:21:04.040 Robert Tseng: yeah, I mean, I guess once we have the preliminary model and staging. Let’s just send that to Ryan. Make sure he
156 00:21:04.530 ⇒ 00:21:15.880 Robert Tseng: I mean he he’ll just, I think he should be the product owner on, like what else he needs to see in that table. So we’ll just use the existing schema we built for type form. We’ll get the Embeddables data into a similar format.
157 00:21:16.070 ⇒ 00:21:20.890 Robert Tseng: We’ll send it to him be like, look like, this is what we had last time like. Was there anything else we’re missing?
158 00:21:21.200 ⇒ 00:21:35.699 Robert Tseng: I I would say that there’s probably just like one thing we’re missing because we only did question answer data in the previous model. But he has like products selected or something. So we might have to figure out how to get that. But I think that’s that’s the only thing I foresee.
159 00:21:44.830 ⇒ 00:21:48.370 Robert Tseng: Okay, I think you’re muted. So I’m assuming that you you got you got that?
160 00:21:51.640 ⇒ 00:21:59.230 Robert Tseng: yeah. And then I guess just to kind of wrap up this spike, I mean, just since Josh is on this call, I’ll just kind of mention.
161 00:22:00.286 ⇒ 00:22:05.653 Robert Tseng: Yeah. So basically, the whole transaction order thing with
162 00:22:07.490 ⇒ 00:22:14.090 Josh: Yeah, I saw great success. You you use 89 messages later we convinced Zack.
163 00:22:15.510 ⇒ 00:22:22.410 Robert Tseng: Yeah, but he’s still kind of just like spinning us around like it’s not a real answer, in my opinion.
164 00:22:22.410 ⇒ 00:22:26.390 Josh: I think that he’s gotta figure out what is so broken with the system. He doesn’t know.
165 00:22:27.600 ⇒ 00:22:38.519 Robert Tseng: Yeah. I mean, it’s just like he’s like trying to act like this is normal. But it’s it’s not like it’s he doesn’t have. This is is a fluke where he like he doesn’t know how to tie them together.
166 00:22:38.680 ⇒ 00:22:49.270 Robert Tseng: This concept of a treatment like not we’ve not used it in our data models. Rob’s never seen it like we don’t. They don’t. It’s just something that he has in his internal system.
167 00:22:49.490 ⇒ 00:22:57.549 Robert Tseng: I don’t know. It’s just like weird. I just feel like any other like Random. I mean, any order or any you know your piece should have like a
168 00:22:58.490 ⇒ 00:23:02.029 Robert Tseng: this is why, between us, why we’re doing what we’re doing.
169 00:23:02.390 ⇒ 00:23:04.510 Josh: Yeah, because we
170 00:23:05.030 ⇒ 00:23:10.740 Josh: good thing. So hey, we just invest our, you know, a little bit of time to get this thing as usable as possible, and then we’re out.
171 00:23:11.440 ⇒ 00:23:16.830 Josh: So I would say even then, like, Don’t over index, because I know that we’re going to be building out our own stuff. So.
172 00:23:17.160 ⇒ 00:23:25.230 Robert Tseng: Okay, yeah. So I mean, we’re just kind of stuck on the like Rebecca has like a metric that she cares about. That’s like.
173 00:23:25.990 ⇒ 00:23:31.590 Robert Tseng: from the time the or the customer placed the order to delivered. There’s a 5 day sla there
174 00:23:31.690 ⇒ 00:23:50.359 Robert Tseng: like I mentioned, like we only can confidently say when the order has been processed like we. We don’t have the the time when the transaction was made from the customer. So like that’s the the gap from the transaction to the order completed. We’re still missing 30 to 40, 30 to 50% of that. So.
175 00:23:50.360 ⇒ 00:23:57.310 Josh: Yeah, until just stay on Zack nicely and just say, Hey, we still need this, you know. Deliver this explain.
176 00:23:58.330 ⇒ 00:24:06.610 Josh: So I mean, he’ll probably get back to us on like Monday, or something about this. If he doesn’t get back to you on Monday about this, then I’ll ping him and just remind me to ping him on Monday.
177 00:24:07.820 ⇒ 00:24:19.452 Josh: because we have a lot of other shit, I believe, like right now, like dude. We’re in the middle of like, I said. There’s a couple of things I wanted to talk to you about. We’re launching like 13 new skews over the next like 4 days.
178 00:24:19.940 ⇒ 00:24:28.549 Josh: The farm Ops team has their own data source sheet. Do you have anybody on your squad that can just transfer it over from that sheet into whichever one you need.
179 00:24:28.950 ⇒ 00:24:35.388 Robert Tseng: Yeah, I saw like Rebecca sent a message in that today. I don’t know where I clicked it out, but
180 00:24:36.300 ⇒ 00:24:42.999 Josh: Yeah, because I had this team super backed up and dealing with these product launches right now.
181 00:24:43.320 ⇒ 00:24:49.460 Robert Tseng: Okay. Yeah, I’ll have to dig it up. I kind of forgot where I saw it. But oh, there it is, this this one.
182 00:24:49.840 ⇒ 00:24:51.139 Josh: No, no, no, no, no.
183 00:24:51.730 ⇒ 00:24:58.060 Josh: there’s there’s this. But then there’s also like the thing that Christiana has to do, or she’s like updating file for you or whatever it takes
184 00:24:58.780 ⇒ 00:25:14.659 Josh: forever. If there’s like some data person like dude that you guys just have access to just like a data entry person that we can leverage for this this one time to do this because she is like heads down with some really heavy stuff. Right now
185 00:25:15.040 ⇒ 00:25:20.969 Josh: for some really big impact stuff for the business. And so it’s we’re we’re kind of in a rough spot.
186 00:25:20.970 ⇒ 00:25:25.199 Robert Tseng: Okay? I mean, yeah, if it’s like 13, th whatever like, we’ll do it. Like, it’s not.
187 00:25:26.360 ⇒ 00:25:27.120 Robert Tseng: Yeah.
188 00:25:28.570 ⇒ 00:25:32.219 Josh: That’s awesome. Yeah. But like, so we’re adding these 13 skews.
189 00:25:32.550 ⇒ 00:25:35.020 Josh: Basically, I think everything will be live
190 00:25:35.140 ⇒ 00:25:37.469 Josh: like half. Most of it will be live tonight.
191 00:25:37.700 ⇒ 00:25:42.579 Josh: and then the remainder of it will be live, probably by end of day tomorrow into Monday.
192 00:25:42.870 ⇒ 00:25:49.269 Josh: And that’s what I have everyone here on site working on. So we have our med kit stuff that all went live today.
193 00:25:49.520 ⇒ 00:25:50.210 Robert Tseng: Yep.
194 00:25:50.490 ⇒ 00:25:54.609 Josh: And there’s those 5 variants of the med kits.
195 00:25:54.970 ⇒ 00:25:59.589 Josh: and then we and some of them have titration schedules, too, like we were talking about with
196 00:26:00.340 ⇒ 00:26:05.369 Josh: one of your people like the med kit. I don’t know what number it is, but it has.
197 00:26:05.640 ⇒ 00:26:12.770 Josh: It’s the same cost, but it’s a different product variant. But it’s the same line like it’s the 1st month they have to. They take that.
198 00:26:13.020 ⇒ 00:26:25.029 Josh: And then the next month they get the full dose. So it’s like 150 milligrams of bupropion, and then it moves to 300 milligrams of bupropion price is the same. Cogs are a little bit different, but it’s still the same line.
199 00:26:25.440 ⇒ 00:26:29.829 Demilade Agboola: Yeah. So I think that that’s the conversation I was having. In the sense of.
200 00:26:29.980 ⇒ 00:26:33.399 Demilade Agboola: For every like medkit purchase.
201 00:26:33.994 ⇒ 00:26:41.039 Demilade Agboola: It’s based on like the treatment plan. And I think my question is, how do we aggregate?
202 00:26:41.630 ⇒ 00:27:04.089 Demilade Agboola: The amount paid back to the variant on and like, how is that done so, I know there’s a start. There’s a maintenance. And so for the start, like the 1st month that they pay that dollar amount, I’m guessing goes directly to the start. Variant. And then the subsequent payments do they do? Those are those attributed to the maintenance variance?
203 00:27:04.320 ⇒ 00:27:07.619 Demilade Agboola: Or is there like another way in which the like
204 00:27:07.950 ⇒ 00:27:10.640 Demilade Agboola: it’s just done in the aggregation is done.
205 00:27:11.430 ⇒ 00:27:24.160 Josh: Robert, for you like knowing that it’s the same product with just a different titration, because this is relevant to the other ones, too, because we’re we have the same product. But the variant is like titration dose, or something like that.
206 00:27:24.280 ⇒ 00:27:31.859 Josh: like Med Kit 3. It has 2 titrations in one line item for the product. And it’s like.
207 00:27:32.080 ⇒ 00:27:38.979 Josh: you know, the 1st one is low dose, and then the subsequent ones are the maintenance higher dosing.
208 00:27:39.170 ⇒ 00:27:42.960 Josh: So would you put them all on one line, or would you try to
209 00:27:43.070 ⇒ 00:27:50.380 Josh: put them together independently, like like he’s talking about right now, cause for me it’s the same price.
210 00:27:50.500 ⇒ 00:27:51.469 Josh: So I don’t.
211 00:27:51.650 ⇒ 00:27:55.390 Josh: I don’t know data wise how to do whatever the fuck you’re trying to do. But it’s.
212 00:27:55.390 ⇒ 00:27:59.840 Robert Tseng: The quantity is the same, like. The titration is just like the number I could just.
213 00:28:00.140 ⇒ 00:28:07.869 Robert Tseng: I guess, like like, because obviously, we have same drug different quantities. I’m like trying to mental model like, is this the same.
214 00:28:07.870 ⇒ 00:28:14.880 Josh: Same. It’s literally the same pill. It’s just they, it’s they don’t get. It’s nothing new. It’s like they have
215 00:28:15.470 ⇒ 00:28:21.759 Josh: 150 milligrams would be propion, and then the maintenance is 300 milligrams would be propion. So it goes from.
216 00:28:23.150 ⇒ 00:28:24.039 Josh: You know what I mean?
217 00:28:24.300 ⇒ 00:28:24.950 Robert Tseng: Yeah.
218 00:28:26.989 ⇒ 00:28:33.800 Robert Tseng: But, like you would, it would always be in that sequence. It would never. You can’t really do like you can’t go.
219 00:28:33.800 ⇒ 00:28:52.349 Josh: Unless they’ve been on treatment before, and they’re just switching providers. They may start at maintenance, but their order would reflect that, like the doctor would have to prescribe them the maintenance dose to start. If it’s treatment, naive, customer, brand new, they’ll all do that sequence, start maintenance, start maintenance.
220 00:28:54.130 ⇒ 00:28:54.690 Robert Tseng: Yeah.
221 00:28:56.630 ⇒ 00:29:08.719 Robert Tseng: so well, yeah, it’s not. It’s not fixed. Then. Yeah, I mean, if it was fixed, then I would say, what you were saying is probably right, we should just treat it as like a Sep separate one, but seems like.
222 00:29:08.720 ⇒ 00:29:09.120 Josh: And
223 00:29:09.750 ⇒ 00:29:16.509 Josh: people like the number of people that will come in on a maintenance dose is very, very low, like, sub
224 00:29:17.150 ⇒ 00:29:21.369 Josh: to 1%. Yeah, like, percent, very low amount.
225 00:29:22.920 ⇒ 00:29:27.590 Demilade Agboola: For a simple model. We can just kind of discount that for now, and just kind of I mean
226 00:29:28.060 ⇒ 00:29:29.200 Demilade Agboola: start maintenance.
227 00:29:30.140 ⇒ 00:29:33.120 Robert Tseng: Okay, I agree, let’s just let’s just discount it for now.
228 00:29:36.200 ⇒ 00:29:37.020 Robert Tseng: Okay.
229 00:29:37.430 ⇒ 00:29:39.270 Josh: So that’s I think that’s all of that.
230 00:29:39.480 ⇒ 00:29:50.049 Josh: And then the only other thing is, I think the team was working through doing some spot checks on the the dashboard that you created for product stuff for Joanna.
231 00:29:50.460 ⇒ 00:29:51.110 Robert Tseng: Yep.
232 00:29:51.480 ⇒ 00:29:54.400 Josh: And it looked like all of the revenue
233 00:29:54.630 ⇒ 00:30:00.009 Josh: stuff, was 6 x’d revenue, and run, rate the revenue, and run rate.
234 00:30:02.187 ⇒ 00:30:08.631 Robert Tseng: Maybe we just don’t calculate run rate the same. But revenue interesting that you think it’s 6 x
235 00:30:09.130 ⇒ 00:30:14.030 Josh: Like if you look at if you look at cerm was at 500 k.
236 00:30:15.850 ⇒ 00:30:18.860 Josh: About 500 k. For the last 30 days.
237 00:30:20.240 ⇒ 00:30:20.990 Robert Tseng: Yeah.
238 00:30:21.040 ⇒ 00:30:24.399 Josh: 300 like you can look at your other reports, and it’ll show you that.
239 00:30:25.430 ⇒ 00:30:26.310 Robert Tseng: Yeah.
240 00:30:27.270 ⇒ 00:30:34.060 Josh: I wish I was at that.
241 00:30:34.610 ⇒ 00:30:37.230 Josh: Your loans paid off. Yeah.
242 00:30:40.480 ⇒ 00:30:46.519 Josh: it might be that it’s like tabulating 6 months and not adjusting, or something. I don’t know.
243 00:30:47.170 ⇒ 00:30:52.540 Robert Tseng: Yeah, I mean, I mean, like, I’m just like eyeballing this. And this looks like
244 00:30:52.920 ⇒ 00:30:57.539 Robert Tseng: 500 k, like, it looks like I refer to just eyeball it, but.
245 00:30:58.270 ⇒ 00:31:02.960 Josh: But you see that the number up here at the thing where it says through email.
246 00:31:02.960 ⇒ 00:31:08.170 Robert Tseng: Yeah, yeah. So I mean, I think the aggregation is off. Yeah. And we can, we look into that.
247 00:31:09.380 ⇒ 00:31:17.329 Annie Yu: Yeah. So just to clarify. So now we are filtered on April 1st to April 29.th And we want the revenue to be
248 00:31:18.290 ⇒ 00:31:21.429 Annie Yu: period, right? Because that’s how it’s calculated. Now.
249 00:31:21.690 ⇒ 00:31:31.079 Robert Tseng: Yeah, it should be just this period, right? But they’re saying, I mean, we should. We should just cross reference with like the product Roas, Ltv. Dashboard like that one has, like the
250 00:31:31.340 ⇒ 00:31:33.310 Robert Tseng: revenue that we trust, or whatever.
251 00:31:35.010 ⇒ 00:31:36.170 Josh: Asterisk
252 00:31:39.220 ⇒ 00:31:42.930 Josh: and the Aov in this one, too, like are there?
253 00:31:43.270 ⇒ 00:31:48.310 Josh: They can’t. There’s no ability for them to make that much of a purchase like
254 00:31:48.910 ⇒ 00:31:52.069 Josh: at all. I think the highest they can get is 8, 88.
255 00:31:52.290 ⇒ 00:31:56.549 Josh: Yeah, like, you can’t have an Aov that high with this product.
256 00:31:58.660 ⇒ 00:31:59.370 Robert Tseng: Okay.
257 00:32:00.100 ⇒ 00:32:06.040 Josh: So like that whole. All that aggregation line up top is just off, that’s like over a ton.
258 00:32:08.860 ⇒ 00:32:18.599 Josh: Well, how about the rest of it is all the rest. No, I’m talking, not products. I’m talking about the graph like in terms of like, what you guys are looking for is everything else
259 00:32:19.280 ⇒ 00:32:25.920 Josh: there. Like, yeah, yeah. Okay. Everything else looks like it’s pretty good.
260 00:32:26.220 ⇒ 00:32:29.999 Josh: And is there any way to add state into this.
261 00:32:31.883 ⇒ 00:32:33.009 Robert Tseng: Yeah. Well.
262 00:32:33.010 ⇒ 00:32:42.709 Josh: You do as a dropdown. I just need to like the, you know, like the one select dropdown. I don’t really care what it is it just that tells me
263 00:32:42.830 ⇒ 00:32:57.829 Josh: I tie that to pharmacies so like? If I were looking at Sema, and I see California has 5,000 orders. And now, the pharmacy says we’re no longer shipping to California. That’s a fucking issue. So it’s basically just top
264 00:32:58.430 ⇒ 00:33:01.480 Josh: 5 states by sale. If if it’s too big.
265 00:33:02.680 ⇒ 00:33:03.400 Josh: Yeah.
266 00:33:06.060 ⇒ 00:33:13.520 Robert Tseng: Yeah, I mean, I we just didn’t add it in because it wasn’t available. I mean, it’ll just take a bit to add to to go grab that field and pull it in so.
267 00:33:13.520 ⇒ 00:33:18.720 Josh: And literally, you can just make it at the top. I think it’s super simple. It’s a multi select
268 00:33:19.070 ⇒ 00:33:20.110 Josh: drop down
269 00:33:20.370 ⇒ 00:33:29.010 Josh: where it’s always aggregate. It always starts on. All 50 are selected. There’s a select, all deselect all. And then you can just like, you know, quickly. Choose.
270 00:33:29.490 ⇒ 00:33:34.989 Josh: You know, a number of states that you want to include in the analysis. And are we unable to do order by status?
271 00:33:35.600 ⇒ 00:33:40.559 Josh: Oh, that shit’s broken right in bask, I think bass order status is broken.
272 00:33:40.860 ⇒ 00:33:43.530 Josh: Yeah, there’s it.
273 00:33:44.340 ⇒ 00:33:51.029 Josh: We can see pending. Oh, yeah, people getting canceled orders.
274 00:33:53.550 ⇒ 00:33:56.355 Josh: I’m just thinking, if there’s a better way to do that.
275 00:33:59.350 ⇒ 00:34:07.260 Josh: that be in the pharmacy. Yeah, that’s what I’m saying. I think that’s a pharmacy board thing. Don’t conflate the revenue stuff with that operation stuff.
276 00:34:08.626 ⇒ 00:34:13.069 Josh: Okay, cool. That’s that’s the big stuff that the team wanted to go through. Robert.
277 00:34:13.400 ⇒ 00:34:17.869 Robert Tseng: Okay, yeah, I mean, we’ll we’re we’ll. We’ll fix it today.
278 00:34:18.340 ⇒ 00:34:19.870 Josh: Cool, awesome.
279 00:34:20.219 ⇒ 00:34:28.650 Josh: Thanks, guys, I gotta jump to another call. But yeah have everyone on site, too. So you know, if you got questions, we can go over whatever, whenever alright.
280 00:34:28.650 ⇒ 00:34:29.850 Robert Tseng: Okay. Thank you.
281 00:34:29.850 ⇒ 00:34:30.620 Josh: Thanks guys.
282 00:34:30.880 ⇒ 00:34:31.489 Robert Tseng: Hi.
283 00:34:34.204 ⇒ 00:34:38.970 Robert Tseng: yeah, I guess for this. Any any other questions for things outstanding? I know we’re a bit over.
284 00:34:39.989 ⇒ 00:34:42.269 Robert Tseng: Seems like we just gotta go and fix.
285 00:34:42.440 ⇒ 00:34:46.850 Robert Tseng: Fix the that product, launch dashboard, make sure. And then we have, like some.
286 00:34:47.170 ⇒ 00:34:49.490 Robert Tseng: obviously some model tweaks to make.
287 00:34:51.137 ⇒ 00:34:58.290 Annie Yu: Yeah, I don’t have access to that looker studio. So can you. I also don’t have the link to that.
288 00:35:00.450 ⇒ 00:35:01.870 Annie Yu: So can you check that.
289 00:35:02.650 ⇒ 00:35:06.560 Robert Tseng: The looker studio report is, not I? I
290 00:35:09.950 ⇒ 00:35:17.099 Robert Tseng: yeah, I I could. I could send it to you. I don’t know how helpful, how helpful it will be, honestly. But yeah, I’ll send it. I’ll I’ll get you.
291 00:35:17.560 ⇒ 00:35:22.490 Annie Yu: Okay, that’s so weird. Okay? Cause I’m I’m looking at it. And I think it’s
292 00:35:22.950 ⇒ 00:35:29.390 Annie Yu: the calculation is straightforward. So I’m gonna take some time with looking into it.
293 00:35:31.850 ⇒ 00:35:38.310 Robert Tseng: Yeah, I mean, like the the revenue calculation I would reference against like, well, so maybe if I just
294 00:35:40.180 ⇒ 00:35:41.050 Robert Tseng: Hi.
295 00:35:52.280 ⇒ 00:35:59.190 Robert Tseng: so let’s see, we’ll do last is.
296 00:36:00.670 ⇒ 00:36:05.879 Robert Tseng: and we’ll do some or some Moreland. Okay.
297 00:36:09.920 ⇒ 00:36:15.550 Robert Tseng: yeah, 500 k, so, and we’re saying, it’s 3 million.
298 00:36:15.790 ⇒ 00:36:16.640 Annie Yu: Okay.
299 00:36:16.810 ⇒ 00:36:17.790 Annie Yu: Okay.
300 00:36:19.100 ⇒ 00:36:24.830 Robert Tseng: Yeah, it’s weird, because the order number is pretty comparable.
301 00:36:25.020 ⇒ 00:36:27.179 Annie Yu: The Aov is way off.
302 00:36:28.280 ⇒ 00:36:31.909 Robert Tseng: Customer count is, you know, pretty. It’s fine. So yeah, it’s like.
303 00:36:32.370 ⇒ 00:36:43.580 Robert Tseng: I mean, but yeah, the it’s not a matter. It’s I guess it’s not an orders and cut and customer thing. It’s probably yeah. Whatever the revenue, whatever’s going on, the revenue side is kind of just off. Yeah.
304 00:36:43.750 ⇒ 00:36:48.080 Annie Yu: Okay, then, what about run rate? I’ve are we
305 00:36:49.610 ⇒ 00:36:52.840 Annie Yu: calculating the way they’re calculating it.
306 00:36:53.090 ⇒ 00:37:02.239 Robert Tseng: Yeah, I mean, I don’t think they know how to calculate run rate to be honest, so like the way that you did it, I think makes sense like I think it just looks off. But it’s it’s directly correlated with revenue. Yeah.
307 00:37:02.240 ⇒ 00:37:03.520 Annie Yu: Makes sense. Okay.
308 00:37:04.990 ⇒ 00:37:10.879 Robert Tseng: Yeah. And I know we didn’t add state, like, I just, we didn’t have it in the orders model. I I didn’t pull it in. But
309 00:37:11.740 ⇒ 00:37:14.350 Robert Tseng: yeah, I guess I’ll have to go and bring it in now.
310 00:37:22.310 ⇒ 00:37:23.903 Robert Tseng: okay, cool.
311 00:37:24.990 ⇒ 00:37:31.650 Robert Tseng: yeah. Anything else. We’ll just kinda keep going to slack. I know we’re we’re fixing up some stuff today. But overall good. Good week.
312 00:37:33.710 ⇒ 00:37:43.950 Demilade Agboola: Yeah, I think it’s a good week. The fact that, like the entire teams there and like everyone, seems to be fairly confident in our data. And it’s just like, Oh, actually, just this bit seems off is encouraging
313 00:37:44.260 ⇒ 00:37:45.300 Demilade Agboola: rather than.
314 00:37:45.610 ⇒ 00:37:52.160 Robert Tseng: There’s more trust. So I think we’re it clearly shows we’re, you know, we’re we’re we’re in a good spot with this client. So
315 00:37:53.350 ⇒ 00:37:59.019 Robert Tseng: yeah, you know. Honestly, this is the most consistent client we have out of, like any client we have here. So
316 00:37:59.280 ⇒ 00:38:10.429 Robert Tseng: we’re kind of in a place where Josh just trusts us to just go and do stuff and maybe in some ways it’s kind of tough, because I have to go and
317 00:38:10.600 ⇒ 00:38:16.189 Robert Tseng: plan work and kind of figure out what the priorities are. But then also, like.
318 00:38:16.380 ⇒ 00:38:21.750 Robert Tseng: Yeah, I think we’re, it’s it’s it’s become more mature as like we can. We can.
319 00:38:21.910 ⇒ 00:38:26.934 Robert Tseng: Yeah, we we have. We have more. I feel more like a partner and less like a
320 00:38:28.180 ⇒ 00:38:31.309 Robert Tseng: we’ll just do whatever they tell us to do, you know. So.
321 00:38:33.200 ⇒ 00:38:34.780 Demilade Agboola: Yeah, I agree, sounds good.
322 00:38:34.780 ⇒ 00:38:38.609 Robert Tseng: Yeah. Okay, anyway. All right, thanks. Everyone. Talk to you later.
323 00:38:39.160 ⇒ 00:38:40.290 Robert Tseng: Thank you.
324 00:38:40.290 ⇒ 00:38:40.910 Demilade Agboola: Thank you.