Meeting Title: Zoom Meeting Date: 2025-06-02 Meeting participants: Robert Tseng, Awaish Kumar, Annie Yu, Demilade Agboola
WEBVTT
1 00:00:05.420 ⇒ 00:00:06.600 Robert Tseng: It’s not Utam.
2 00:00:17.320 ⇒ 00:00:18.180 Robert Tseng: Ugh!
3 00:00:54.010 ⇒ 00:00:54.700 Awaish Kumar: Hello!
4 00:00:56.480 ⇒ 00:00:57.310 Robert Tseng: I wish.
5 00:00:58.970 ⇒ 00:01:01.010 Awaish Kumar: Hi! How are you doing.
6 00:01:01.720 ⇒ 00:01:03.280 Robert Tseng: I am good.
7 00:01:04.310 ⇒ 00:01:05.250 Robert Tseng: It’s good to.
8 00:01:05.250 ⇒ 00:01:05.850 Awaish Kumar: So.
9 00:01:07.690 ⇒ 00:01:21.729 Robert Tseng: Yeah. So I’m I’m not back in New York yet. I’m in. I’m in Amsterdam this week. But I will be working. I just my hours are kind of maybe more like I/O hours rather than my normal hours.
10 00:01:24.240 ⇒ 00:01:26.620 Awaish Kumar: Nice. How has been your vacation?
11 00:01:27.350 ⇒ 00:01:31.743 Robert Tseng: Yeah, last week was great. I was in Kenya last week.
12 00:01:32.500 ⇒ 00:01:35.279 Robert Tseng: yeah, I was. It was. It was hard to.
13 00:01:36.140 ⇒ 00:01:48.359 Robert Tseng: I didn’t have very good Wi-fi. So it was. It was hard to really help help you guys out, but seems like people were happy. Eden was very happy with kind of the way you guys picked it up. So thank you for covering, and
14 00:01:49.190 ⇒ 00:01:53.799 Robert Tseng: I won’t say it was the most restful trip. There were a couple of stressful nights. I just
15 00:01:54.000 ⇒ 00:01:58.110 Robert Tseng: one. I’ll share one thing I we did like a safari
16 00:01:59.050 ⇒ 00:02:04.950 Robert Tseng: like a self drive safari. So I drove, and then we got stuck
17 00:02:05.511 ⇒ 00:02:21.430 Robert Tseng: at night, and I we didn’t get back to camp until like 3 Am. In the morning. So that was a pretty rough night. And it was kind of scary. There were like animals and stuff and hyenas, and it was. It was the whole thing. But
18 00:02:21.960 ⇒ 00:02:27.980 Robert Tseng: we were safe, and it was okay. And yeah, I I it was. It was good overall.
19 00:02:29.650 ⇒ 00:02:32.270 Awaish Kumar: Hey? You’re riding like you’re driving in the desert.
20 00:02:33.340 ⇒ 00:02:39.739 Robert Tseng: Yeah. So you would think that it’s a desert. The part we were in was actually not a desert. It was more like a forest. So
21 00:02:39.930 ⇒ 00:02:50.779 Robert Tseng: I was like driving. And then, because it had been flooding so much like there were trees that were blocking the path, so we just like couldn’t really figure out how to navigate to our camp.
22 00:02:51.488 ⇒ 00:02:57.250 Robert Tseng: And then it was just not very safe to drive on and so we got stuck in the mud.
23 00:02:57.790 ⇒ 00:03:01.950 Robert Tseng: So it was actually not a desert. It was, it was too wet, and it was a forest.
24 00:03:02.950 ⇒ 00:03:04.430 Awaish Kumar: Okay. Yeah.
25 00:03:04.430 ⇒ 00:03:10.360 Robert Tseng: Yeah, yeah, hey, Annie, hey? Gim lade.
26 00:03:12.890 ⇒ 00:03:14.429 Demilade Agboola: Hello! Glad to have you back.
27 00:03:15.770 ⇒ 00:03:25.140 Robert Tseng: Thank you. Yes, I’m I’m glad to be back. Got to be back in the 1st world, and having having stuff, not not having to think too much about
28 00:03:25.370 ⇒ 00:03:26.680 Robert Tseng: getting around.
29 00:03:28.640 ⇒ 00:03:40.380 Robert Tseng: yeah, I know I I did blocked off this. I didn’t really block off too much time, so we’ll catch up more later. I’ll I’ll be talking to most of you throughout the week. But yeah, I guess a couple of things. One, yeah, this Monday call
30 00:03:41.130 ⇒ 00:03:47.310 Robert Tseng: shifting it back an hour or so, because now Eden is running like weekly
31 00:03:48.731 ⇒ 00:03:53.150 Robert Tseng: Kpi, like leadership Kpi meetings with the rest of like
32 00:03:53.280 ⇒ 00:04:15.509 Robert Tseng: the team leaders which is really cool. I’m gonna take some screenshots and send it to you all because, I think, like half of the things that they’re reporting on are coming from our reports. So clearly, like our work has enabled them to be able to view the business in a measurable way so that they’re gonna so that they can actually build on these metrics and start to think about.
33 00:04:16.010 ⇒ 00:04:20.780 Robert Tseng: you know, start, yeah, start to think about how to measure their their their progress quantitatively.
34 00:04:21.227 ⇒ 00:04:49.800 Robert Tseng: Which is good, because I think this puts the ownership more in their hands. They’re the ones that are gonna have to. They have to talk about their metrics every week. And you know, they can’t just default to being like, Oh, yeah. Well, the data team didn’t give us this or that or whatever. So I think this is a good step forward for for the rest of the team. And yeah, I think that’s just that’s that was cool to see. And then.
35 00:04:50.770 ⇒ 00:04:58.880 Robert Tseng: yeah, I think other things I was going through and the backlog today and trying to clean everything up. So we’ll spend more of our time in linear today.
36 00:04:59.258 ⇒ 00:05:02.810 Robert Tseng: Just because there’s new assignments. And then I want to close out some of the old stuff.
37 00:05:03.616 ⇒ 00:05:06.690 Robert Tseng: So we’ll just kind of jump to that for now, if that’s okay.
38 00:05:12.950 ⇒ 00:05:15.533 Robert Tseng: Okay. So here we go.
39 00:05:17.120 ⇒ 00:05:24.410 Robert Tseng: yeah. So I cleared out everything that was already done or pending client feedback. I moved it out. A few things here.
40 00:05:28.480 ⇒ 00:05:29.670 Robert Tseng: I think.
41 00:05:31.170 ⇒ 00:05:38.719 Robert Tseng: Yeah. The. So I think 1 1 big topic want to talk about is like questionnaire and intake. I know a way she sent me
42 00:05:38.860 ⇒ 00:05:46.860 Robert Tseng: like a video and some documentation to read. I haven’t done that yet. I will do that today. But that’s like an area where I want to spend some time talking about
43 00:05:48.320 ⇒ 00:05:53.220 Robert Tseng: I’m not Gonna necessarily click into a ticket unless you call it out. But my understanding is
44 00:05:53.360 ⇒ 00:06:01.650 Robert Tseng: we were having Sebastian go and try to implement it. He did not. And so a use of the on the new Emr team is supposed to be doing this.
45 00:06:01.880 ⇒ 00:06:07.513 Robert Tseng: I wish you also tried to do some tracking yourself. I kind of read some of your comments. But
46 00:06:08.030 ⇒ 00:06:22.122 Robert Tseng: yeah, I mean, all. All I know is, it’s been a couple of weeks, and like Ryan doesn’t have what he wants. So he’s kind of like bugging me for it. So I just wanna get a clear action plan on, how? How are we gonna handle the tracking
47 00:06:22.850 ⇒ 00:06:26.000 Robert Tseng: regardless of his post log later on. Yeah.
48 00:06:26.810 ⇒ 00:06:42.319 Awaish Kumar: So there are like, we want to track some events right from a questionnaire, and like there are 2 paths being taken right now, number one is that we implement this in the Gtm.
49 00:06:42.560 ⇒ 00:07:06.489 Awaish Kumar: So that we can do it now, and number 2 we implemented for the new Emr. So Sebastian shared this document with Ayush and Ayush like kind of I I synced up with him, and he showed me what he was doing, and basically he was what he’s just working on, building a new Emr, and they have a new questionnaire kind of they have built up flow for new questionnaire, and
50 00:07:06.680 ⇒ 00:07:08.089 Awaish Kumar: I have shared the
51 00:07:08.529 ⇒ 00:07:20.299 Awaish Kumar: your document. And all these things he needs to implement for to track all these things in the new Emr, so basically, he’s going to add all over all the events we need in the new Emr side.
52 00:07:20.580 ⇒ 00:07:27.630 Awaish Kumar: and we can just like collected using segment and then push it to bigquery.
53 00:07:27.860 ⇒ 00:07:55.160 Awaish Kumar: This is like one of the things. Second thing about gtm, I try to add it. But the problem is that this data we want to collect. It’s not coming in data layer, which is the way. Normally, Sebastian’s team is collecting all the data like this. Dlv underscore score underscore thing. So all the things we need like question and answer and things like that, they’re not there. So I like, they’re just getting the questionnaire screen
54 00:07:56.010 ⇒ 00:08:07.180 Awaish Kumar: title, and there’s no specific question in there. There’s no specific answer which the the user is selecting. It’s not coming up in the data layer. So I’m not able to
55 00:08:07.581 ⇒ 00:08:33.470 Awaish Kumar: like like, get it in any of our tags. So that’s why I tried to talk to Sebastian like how they handle something from Javascript like, do you showed me that we have this data in Javascript? And I asked him the same thing like we have this data in this Javascript, but we don’t have this in the data layer. And he said, like, if it’s in the like, it’s not in the data layer. Then, like
56 00:08:33.809 ⇒ 00:08:42.280 Awaish Kumar: he said, like. We can collect it somehow from this Javascript thing. But he said, like it is, it totally depends on the bask
57 00:08:43.017 ⇒ 00:08:48.389 Awaish Kumar: and if the changes anything on their website our like, this event
58 00:08:49.176 ⇒ 00:08:51.880 Awaish Kumar: that, the trigger is going to fail.
59 00:08:52.473 ⇒ 00:09:00.990 Awaish Kumar: So it’s like kind of this thing. And how to get this from this Javascript I was not sure, and I’m I was just constantly pushing for that like
60 00:09:01.300 ⇒ 00:09:05.490 Awaish Kumar: strictly like, pair me with someone in your team, and I can just
61 00:09:06.030 ⇒ 00:09:13.130 Awaish Kumar: have, get, get some help with there. But yeah, he didn’t like said, Okay, for that.
62 00:09:13.900 ⇒ 00:09:16.120 Robert Tseng: Okay, yeah,
63 00:09:19.010 ⇒ 00:09:19.950 Robert Tseng: So
64 00:09:21.390 ⇒ 00:09:31.519 Robert Tseng: I thought that the data layer, I thought, even in setting those requirements in the notion, Doc, and sending it to use like I had already looked at like a chunk from the data layer.
65 00:09:33.800 ⇒ 00:09:36.118 Robert Tseng: I mean, I have to go and write those those
66 00:09:37.170 ⇒ 00:09:43.509 Robert Tseng: have to have to write in the console again. But I mean even just clicking into here. We have different objects for every question right that come through.
67 00:09:43.620 ⇒ 00:09:45.020 Robert Tseng: And then within that.
68 00:09:45.020 ⇒ 00:09:51.620 Awaish Kumar: From Javascript. Right? If you go into the Gtm. In the Gtm. You can see one tab called data layer.
69 00:09:51.910 ⇒ 00:09:52.300 Robert Tseng: Yeah.
70 00:09:52.300 ⇒ 00:10:00.900 Awaish Kumar: The suggestion says everything you see in the data there can be collected. But there’s no like I cannot. I see the screen like the
71 00:10:01.030 ⇒ 00:10:06.970 Awaish Kumar: the screen title and things like that. But I cannot see, like the answer or other things they are missing.
72 00:10:10.100 ⇒ 00:10:11.990 Robert Tseng: Yeah, okay.
73 00:10:13.630 ⇒ 00:10:29.460 Robert Tseng: this my memory is kind of fuzzy, but I feel like I got on a call with Sebastian and Ryan before we talk through this. But okay, I’m gonna go back and look into it. Sure, if that’s your if that’s your conclusion, you think that we can’t get it from there, and we need to get it from Javascript.
74 00:10:29.990 ⇒ 00:10:34.809 Robert Tseng: Then that’s fine. So wh? What? We’re okay, yeah, maybe you just kind of
75 00:10:35.010 ⇒ 00:10:37.120 Robert Tseng: I mean, I can help nudge Sebastian.
76 00:10:37.120 ⇒ 00:10:40.009 Awaish Kumar: Also one more thing, the
77 00:10:40.160 ⇒ 00:11:00.310 Awaish Kumar: event like you showed me that we we saw it like on the page view event. That’s only when you click on getting get started. Only that button has that event triggered so like whenever you click next next, or you click on some answer, it does not trigger that event. So
78 00:11:00.820 ⇒ 00:11:10.099 Awaish Kumar: in my while debugging, I try to add that event across all the screens. But in the production version does not have this on each screen.
79 00:11:10.410 ⇒ 00:11:16.060 Awaish Kumar: It’s only triggering on when you click on getting started. Only 1st click to start the questionnaire.
80 00:11:17.270 ⇒ 00:11:17.980 Robert Tseng: Yeah.
81 00:11:19.250 ⇒ 00:11:33.660 Robert Tseng: And that’s largely because it is a Javascript app, and it’s hard to you. Can’t I mean you? You. You can’t use the traditional Gtm approach to logging page views like the the URL may change. But it’s not, it’s it’s technically still the same page. Right?
82 00:11:33.780 ⇒ 00:11:35.710 Robert Tseng: Actually, yeah, the order doesn’t even change.
83 00:11:36.170 ⇒ 00:11:37.470 Robert Tseng: Right? You see that.
84 00:11:37.770 ⇒ 00:11:44.010 Robert Tseng: So yeah, these have to be Javascript events that we’re firing back into like.
85 00:11:44.550 ⇒ 00:11:46.899 Robert Tseng: I guess, either through segment or
86 00:11:47.398 ⇒ 00:11:55.130 Robert Tseng: yeah, like we, we have to. We have to use. We have to. We have to add Javascript to to the.
87 00:11:55.340 ⇒ 00:11:58.659 Robert Tseng: to the questionnaire in order to get to to track the answers.
88 00:12:00.150 ⇒ 00:12:08.929 Robert Tseng: But okay, well, we’re gonna we’ll help you keep pushing on that we can. That’s kind of where we’re at. That’s fine. We’ll we’ll just kind of go from there.
89 00:12:09.310 ⇒ 00:12:13.310 Robert Tseng: Okay, I’ll review this, and then we’ll I’ll if you could just help me. Just
90 00:12:13.840 ⇒ 00:12:20.660 Robert Tseng: point me to the message you sent to Sebastian. If you have anything else you want me to like to follow up with him on. Just let like let me know.
91 00:12:21.640 ⇒ 00:12:25.460 Awaish Kumar: Yeah, I have a chat where I communicated with him. I can share with you.
92 00:12:27.074 ⇒ 00:12:33.559 Robert Tseng: Okay? So yeah, that’s the main thing I wanted to talk about. And then, as far as like things that are urgent.
93 00:12:34.691 ⇒ 00:12:49.900 Robert Tseng: Yeah, cause the issues like post hog thing is not gonna happen anytime soon, like none of the live questionnaires are gonna go that go there until we actually cut over. It’s good to know and good for him to get started on that parallel. But we do need the short term solution as well. So
94 00:12:51.540 ⇒ 00:12:54.780 Robert Tseng: yeah, here on.
95 00:12:54.780 ⇒ 00:12:56.329 Awaish Kumar: But the other one I mentioned.
96 00:12:56.730 ⇒ 00:12:57.300 Robert Tseng: Yep.
97 00:12:57.860 ⇒ 00:13:03.200 Awaish Kumar: Sorry for the other one I just mentioned, because they are in the early stages of development, kind of
98 00:13:03.320 ⇒ 00:13:05.920 Awaish Kumar: like as much as we can give them the
99 00:13:06.290 ⇒ 00:13:11.799 Awaish Kumar: the things we want to monitor. They will implement it right now because they are still in development phase.
100 00:13:12.300 ⇒ 00:13:12.910 Robert Tseng: Good
101 00:13:13.400 ⇒ 00:13:24.000 Robert Tseng: do are you? Do? They have like a weekly like call, or something like how we like. I want us to stay like on top of their progress, so that we can keep giving them stuff along the way.
102 00:13:25.780 ⇒ 00:13:30.139 Awaish Kumar: Yeah, like they don’t. They must have like they just added me for one meeting.
103 00:13:30.140 ⇒ 00:13:33.719 Robert Tseng: Oh, they only added you for one. Okay? So I’ll also kind of ping
104 00:13:33.820 ⇒ 00:13:35.860 Robert Tseng: special to get added to that as well.
105 00:13:37.470 ⇒ 00:13:45.039 Robert Tseng: Okay, yeah, I mean, their timeline is July launch. Do you think that’s actually gonna happen.
106 00:13:48.935 ⇒ 00:13:51.360 Awaish Kumar: Like. I don’t think so.
107 00:13:52.090 ⇒ 00:13:55.770 Awaish Kumar: There, there is a lot of things to do like they have built kind of a
108 00:13:56.100 ⇒ 00:14:04.820 Awaish Kumar: like like a lot of things. But like you can say on the front end side, he mentioned that only 30% of work is done.
109 00:14:06.230 ⇒ 00:14:06.950 Robert Tseng: Okay.
110 00:14:08.200 ⇒ 00:14:20.870 Robert Tseng: Alright? Well, yeah. So I don’t really think they’ll be done by July. I mean, August or September is like what I think is gonna end up happening. So yeah, we’re gonna be stuck with our current system for a while. So we definitely need the short term solution.
111 00:14:21.510 ⇒ 00:14:24.529 Robert Tseng: Okay, good to know.
112 00:14:24.650 ⇒ 00:14:28.260 Robert Tseng: And then alright, let’s let’s talk about this one really quick. So
113 00:14:28.420 ⇒ 00:14:39.129 Robert Tseng: yeah, we already closed this one out. I know that, Annie, and wish you kind of touched this last week. I think there’s still a little bit more that we could do with them. So I just sent a follow up message, but nothing to do there.
114 00:14:39.562 ⇒ 00:14:45.630 Robert Tseng: Zach Bask, if there are any, follow ups, I’m just logging them here so that we can keep doing that weekly check in with him.
115 00:14:46.460 ⇒ 00:14:54.779 Robert Tseng: And then, yeah, I didn’t want it. I know you sent this. I don’t know what this is like. It didn’t show up, so I don’t really know.
116 00:14:55.080 ⇒ 00:15:02.809 Robert Tseng: Oh, there it is. Oh, okay, it’s actually a loop. Okay. I didn’t watch it because I never. I guess I never really saw it load until now. So.
117 00:15:03.274 ⇒ 00:15:06.119 Robert Tseng: yeah, is there anything that I need to know here.
118 00:15:10.920 ⇒ 00:15:15.829 Demilade Agboola: Trying to remember the concept of that? Oh, yeah. So it was basically about the like.
119 00:15:15.830 ⇒ 00:15:16.990 Robert Tseng: And file size.
120 00:15:17.663 ⇒ 00:15:22.750 Demilade Agboola: How like it’s being counted. But summarily, it’s a thing about like
121 00:15:23.450 ⇒ 00:15:30.619 Demilade Agboola: the vowels she puts are like when they’re more than one in a single row that represents the
122 00:15:30.770 ⇒ 00:15:39.450 Demilade Agboola: different valve sizes that will be shipped to a customer on the cost of there are like treatment and
123 00:15:42.440 ⇒ 00:15:48.189 Demilade Agboola: depending on the shipment frequency that can change. And so.
124 00:15:49.160 ⇒ 00:16:08.779 Demilade Agboola: for instance, if on each certain shipment the 1st order is made, and it’s 2, 3, 3, 4, for instance, the 1st shipments will be 2, the second order will be 3, the 3rd will be 4, and then the 4th order will be 4 as well. However, it’s possible that they can have a certain frequency, so say quarterly that
125 00:16:09.050 ⇒ 00:16:10.530 Demilade Agboola: they can ship
126 00:16:10.790 ⇒ 00:16:32.559 Demilade Agboola: multiple at once. And so that’s what we created the very last column at the end, for so that lets us know how many are shipped at once. If not, then we can then default to the like quarterly, like the frequency, like quarterly, and then use it to go. How many refills are left? We will just count. So like if, for instance, there are 6 of them 6 valve sizes in a row.
127 00:16:33.303 ⇒ 00:16:41.889 Demilade Agboola: But they ship 3 at once, so that means the 1st 3 will be eliminated, and then it will be 3 left, because, you know.
128 00:16:42.080 ⇒ 00:16:49.850 Demilade Agboola: the next, the next quarter shipment will only have those 3. So that’s kind of how we walk through it, and just kind of came up to a function of the logic.
129 00:16:51.170 ⇒ 00:16:58.920 Robert Tseng: Okay, so is this more like, you want to run your logic by her and like, get her to sign off on it, or like, what’s the what are we waiting on here.
130 00:16:59.290 ⇒ 00:17:04.579 Demilade Agboola: So for this. I am trying to like, create logic for it.
131 00:17:05.304 ⇒ 00:17:10.299 Demilade Agboola: In such a way that it it doesn’t get caught on edge cases.
132 00:17:12.970 ⇒ 00:17:13.915 Robert Tseng: Yeah.
133 00:17:16.359 ⇒ 00:17:34.010 Robert Tseng: I mean, this seems complicated for fields that are dynamically changing, based off of one of them. This like, seems like there’s too many contingencies. I don’t know if you think thought that that was the best approach, like I I trust you there. But if you feel like you want more time and just like to review this model to make sure that this this makes sense.
134 00:17:34.230 ⇒ 00:17:37.689 Robert Tseng: Then we can. We can. I just wanna give her an update on this as well.
135 00:17:38.020 ⇒ 00:17:41.969 Demilade Agboola: Yeah, I mean, we’re still like, that’s part of the it’s a like.
136 00:17:42.530 ⇒ 00:17:53.129 Demilade Agboola: because that means for every order like the tricky part is like, basically for every order slash treatment. So every treatment we’re trying to find out what order they’re on. And then, from every order we’re trying to mark.
137 00:17:53.410 ⇒ 00:18:03.599 Demilade Agboola: And then if it’s like the 3rd order, figure out what the 3rd well, size is, and then how many are? Let? It’s that’s kind of where it becomes a bit tricky.
138 00:18:13.610 ⇒ 00:18:14.590 Robert Tseng: yeah.
139 00:18:17.660 ⇒ 00:18:21.660 Robert Tseng: Still working through the logic here. Okay.
140 00:18:29.470 ⇒ 00:18:32.540 Robert Tseng: I don’t have a solution off the top of my head, I think.
141 00:18:37.840 ⇒ 00:18:46.840 Robert Tseng: Well, I guess, just talking it through it out loud. If every variant variant plan.
142 00:18:47.230 ⇒ 00:18:51.970 Robert Tseng: Well, yeah, like the the base unit is a variant. Every variant has
143 00:18:52.600 ⇒ 00:18:56.500 Robert Tseng: like a set of vial sizes.
144 00:18:57.730 ⇒ 00:19:07.009 Robert Tseng: right like a variant on a quarterly plan, is going to be different than I will vary it on a monthly plan, or a 6 month plan. So though it’s like every plan type gets split out right?
145 00:19:07.710 ⇒ 00:19:08.680 Robert Tseng: So
146 00:19:08.940 ⇒ 00:19:15.910 Robert Tseng: then it’s a 1 to one relationship between the variant and the vial size order, like maybe sometimes it’ll, you know, like it, it’s
147 00:19:16.020 ⇒ 00:19:18.400 Robert Tseng: and and as long as we have this.
148 00:19:19.540 ⇒ 00:19:27.160 Robert Tseng: we know that that’s the set from which we can choose from. And we then we need to. We know, we need to know, like which
149 00:19:27.370 ⇒ 00:19:33.240 Robert Tseng: treatment number they are on within that set to be able to determine like which number we pull from.
150 00:19:33.620 ⇒ 00:19:40.420 Robert Tseng: So I wonder if it’s just like, you know, a 1, you know, one to one relationship between, like, okay, like
151 00:19:40.840 ⇒ 00:19:53.139 Robert Tseng: one variant to one set. And then we need to have like another. It’s like a treatment identifier that that labels like which number and that set to pick from I don’t know. Something like that is like. Seems to me
152 00:19:53.370 ⇒ 00:19:57.541 Robert Tseng: like I mean, I think that’s what your logic is trying to do already. But
153 00:19:58.410 ⇒ 00:20:03.299 Robert Tseng: I wonder if that’s if that’s a i mean, I’m trying to just describe it in a way that might make sense for you.
154 00:20:04.580 ⇒ 00:20:10.999 Demilade Agboola: Yeah, I mean, so well, like effects, what we’re trying to just do is if we know the order number
155 00:20:11.140 ⇒ 00:20:12.700 Demilade Agboola: amongst the treatments.
156 00:20:13.350 ⇒ 00:20:18.090 Demilade Agboola: I think that’s where we can start mapping it easily. So if we know like
157 00:20:18.450 ⇒ 00:20:22.539 Demilade Agboola: this, this is the 4th order for this treatment.
158 00:20:22.740 ⇒ 00:20:30.618 Demilade Agboola: then we know the file sizes that have been sent so far. So we count one to 4 like we’re just making an assumption for the monthly
159 00:20:31.200 ⇒ 00:20:32.610 Demilade Agboola: a monthly order.
160 00:20:33.000 ⇒ 00:20:38.130 Demilade Agboola: So we count one to 4, and then we know maybe 5 and 6 are left. So therefore there are 2
161 00:20:39.370 ⇒ 00:20:40.050 Robert Tseng: Yeah.
162 00:20:40.530 ⇒ 00:20:42.180 Demilade Agboola: Now size is left. The problem.
163 00:20:42.180 ⇒ 00:20:43.560 Robert Tseng: And do we okay?
164 00:20:44.100 ⇒ 00:20:47.180 Demilade Agboola: It’s a key part like, how like.
165 00:20:49.080 ⇒ 00:20:57.149 Demilade Agboola: that’s kind of the tricky part where I’m trying to like. Figure out like, how do we know this is the second order for that treatment versus this is the 3rd order for that same treatment.
166 00:21:04.070 ⇒ 00:21:13.746 Demilade Agboola: So I will have to kind of. And that’s kind of where the edge cases could come in, because even if I count the the patient’s orders and say,
167 00:21:15.120 ⇒ 00:21:22.620 Demilade Agboola: this is their 4th order. Potentially they could have. They could be on multiple treatment cycles or multiple treatment things so that could.
168 00:21:22.870 ⇒ 00:21:28.570 Demilade Agboola: you know, make it a bit tricky.
169 00:21:29.340 ⇒ 00:21:49.269 Robert Tseng: Okay, I’m just gonna have you. And you can think through it. But yeah, I would just ask Katie Christiana, they’re not gonna know from a data perspective. But they’re gonna just be able to go and tell you from operations perspective. Christiana may show you how she does it in bask. And then Katie may show you from like a different perspective. But maybe that’ll give you some some direction. There.
170 00:21:51.130 ⇒ 00:21:52.410 Demilade Agboola: Okay. Sounds good.
171 00:21:52.810 ⇒ 00:21:53.380 Robert Tseng: Yeah.
172 00:21:54.080 ⇒ 00:22:05.069 Robert Tseng: okay, let’s keep that moving. Alright. So those are all the things that are pending feedback outstanding. Annie, I know we haven’t touched on anything from you yet but a couple of things, I added to cycle for you.
173 00:22:08.430 ⇒ 00:22:18.160 Robert Tseng: Yeah, they’re just a few quick queries. I think sales breakdown by state. So I think.
174 00:22:19.410 ⇒ 00:22:30.010 Robert Tseng: yeah, this, there’s some description here on, like what they want to see. Originally it was just for Florida. But now they want to see it by state. So I think that’s just like one query that you’ll have to have to run.
175 00:22:30.140 ⇒ 00:22:34.800 Robert Tseng: And then patients who live in Colorado is another like query.
176 00:22:35.010 ⇒ 00:22:41.320 Robert Tseng: which I think is just based off of, like one of our existing models. So we can just use that and then filter on the zip codes found there
177 00:22:42.176 ⇒ 00:22:43.510 Robert Tseng: and then
178 00:22:44.323 ⇒ 00:23:07.250 Robert Tseng: reporting all free funds process the previous day. So we’re gonna be getting more of these like random, like one off requests, because I’m having all the requests run through us rather than like. I guess this is what Rob was spending his time on just like doing like random stuff like this. So I know, Andy, you will probably get an influx in these like random one off requests. I expect maybe 3 to 5 a week.
179 00:23:07.607 ⇒ 00:23:12.560 Robert Tseng: And I just wanna see like how our capacity isn’t taking it on so that I can go back to
180 00:23:13.370 ⇒ 00:23:17.099 Robert Tseng: to, you know, leadership and be like, Okay, well.
181 00:23:17.270 ⇒ 00:23:22.930 Robert Tseng: if this is all Rob has been doing like 3 to 5 of these a week, then we could. We could easily absorb it.
182 00:23:26.400 ⇒ 00:23:27.495 Annie Yu: Okay,
183 00:23:28.980 ⇒ 00:23:35.419 Annie Yu: I might have more questions as I go. But one question I do have now is the refunds
184 00:23:37.936 ⇒ 00:23:43.980 Annie Yu: is there a field that indicates that.
185 00:23:45.000 ⇒ 00:23:54.609 Robert Tseng: I do not know. I don’t know. I haven’t worked refund data at all, so if you don’t know where to start, you can just feel free to ask like the farm Ops team like you. Seems like you have a good relationship with them. Now.
186 00:23:54.880 ⇒ 00:23:58.330 Robert Tseng: you can just like try to just deconstruct it and work with them through that.
187 00:24:00.029 ⇒ 00:24:02.180 Demilade Agboola: I will get back to you on that today.
188 00:24:06.080 ⇒ 00:24:14.560 Demilade Agboola: I know. I looked through Monday. I saw it, and I requested it from, I requested where refunds could be found from Rob, and he told me where. So I’m going to look at that and let you know.
189 00:24:14.970 ⇒ 00:24:19.590 Robert Tseng: Okay, so work with people this.
190 00:24:20.290 ⇒ 00:24:38.860 Robert Tseng: yeah. So the process is like any internal, any request from the from the Eden team is going to go to the Monday board. I’m gonna triage it, and either like, say no or like, I’ll add it to our linear. Obviously I wasn’t here last week to do it. So everything kind of got put on hold. But yeah, I that’s how I’m trying to manage like.
191 00:24:39.160 ⇒ 00:24:43.230 Robert Tseng: get a sense of like, how much the volume of requests that we get from their team.
192 00:24:44.053 ⇒ 00:24:51.699 Robert Tseng: I know there’s a few other things that are blocked in progress that I haven’t touched on here today. But hopefully, that gives you enough to get started.
193 00:24:53.980 ⇒ 00:25:00.920 Robert Tseng: Yeah, I any anything else that people want to call out. I can. I can keep scrubbing this
194 00:25:01.110 ⇒ 00:25:02.040 Robert Tseng: later.
195 00:25:15.550 ⇒ 00:25:20.499 Annie Yu: I do have one question for a wish. This is for the
196 00:25:22.180 ⇒ 00:25:26.180 Annie Yu: Ltv heat map table. Last week we
197 00:25:26.890 ⇒ 00:25:30.030 Annie Yu: we’re saying there’s discrepancy of the
198 00:25:30.670 ⇒ 00:25:37.730 Annie Yu: distinct customers from that table, and it’s the other one.
199 00:25:53.490 ⇒ 00:25:54.770 Robert Tseng: That’s for a wish. Right?
200 00:25:55.820 ⇒ 00:25:57.310 Annie Yu: Yeah, bye.
201 00:25:57.540 ⇒ 00:25:58.999 Robert Tseng: Sorry. I’m just like, going.
202 00:25:59.720 ⇒ 00:26:00.320 Robert Tseng: Okay.
203 00:26:00.670 ⇒ 00:26:08.080 Awaish Kumar: I was just mute. So it was between product sales, summary table and the retention revenue summary table.
204 00:26:09.118 ⇒ 00:26:12.550 Annie Yu: Cohort, revenue, retention, summary and product sales. Summary.
205 00:26:12.910 ⇒ 00:26:16.930 Awaish Kumar: Yeah, okay, okay, I will look at that correct.
206 00:26:17.600 ⇒ 00:26:23.559 Annie Yu: Okay, okay, yeah. Just wanna make sure if there was any update. But okay.
207 00:26:38.490 ⇒ 00:26:46.879 Robert Tseng: Okay? With the remaining time. Yeah, I think this this one has been blocking us on the Cmo dashboard for a while.
208 00:26:47.270 ⇒ 00:26:53.890 Robert Tseng: This cohort thing, this cohort meat heat map. Yeah. What’s what’s the update here?
209 00:26:58.552 ⇒ 00:27:12.490 Annie Yu: We kind of figure out the Ncac difference which is expected because in that in this model we are using, we’re not assigning on categorized to other products. But right now it’s the
210 00:27:13.904 ⇒ 00:27:20.270 Annie Yu: distinct customer count. That’s a bit different. So while she’s investigating.
211 00:27:28.080 ⇒ 00:27:44.739 Robert Tseng: So on the Ncac. Difference. I don’t know how we resolved it, but why would we? Shouldn’t we just use the what we already have done like. I don’t want people to not trusted because they see something’s off with the Ncaa. We’ve already have the modeling done. Why why can’t we just like categorize everything.
212 00:27:48.890 ⇒ 00:27:56.769 Awaish Kumar: So like we created in the table, because for this chart the the structure of the table was
213 00:27:57.410 ⇒ 00:27:58.860 Awaish Kumar: different, and.
214 00:27:58.860 ⇒ 00:27:59.320 Robert Tseng: Yeah.
215 00:27:59.770 ⇒ 00:28:06.850 Awaish Kumar: And the problem is like the only thing which is coming in. Like in the product sales summary. We have a logic where we
216 00:28:07.611 ⇒ 00:28:12.100 Awaish Kumar: distribute our uncategorized ad spend to all other
217 00:28:12.280 ⇒ 00:28:20.819 Awaish Kumar: products. Right? And in this model we have, we were not doing that. So I just asked any if we want to do that.
218 00:28:21.140 ⇒ 00:28:26.200 Awaish Kumar: so we can do but like it depends on what is the requirement for this dashboard.
219 00:28:27.670 ⇒ 00:28:33.210 Robert Tseng: That will not matter for the Ltv section, but it will matter for the Ltv Cac ratio right?
220 00:28:33.820 ⇒ 00:28:40.400 Robert Tseng: We have to keep. I think we have to keep the same cac, that we calculate across. I mean, we yeah, like.
221 00:28:43.410 ⇒ 00:28:47.810 Robert Tseng: yeah, I think we should just spread it. We should just we should just keep it. Keep it the same
222 00:28:48.390 ⇒ 00:28:49.080 Awaish Kumar: Okay. Wait.
223 00:28:49.080 ⇒ 00:28:53.160 Robert Tseng: We haven’t updated our Cac definition in a while, so we should just use it. Use the same one.
224 00:28:56.360 ⇒ 00:29:00.469 Awaish Kumar: Okay? So that in that case I will just distribute the
225 00:29:00.800 ⇒ 00:29:06.370 Awaish Kumar: uncategorized aspect to the products based on the number of orders.
226 00:29:06.580 ⇒ 00:29:07.760 Awaish Kumar: At least.
227 00:29:08.050 ⇒ 00:29:08.710 Awaish Kumar: Okay.
228 00:29:08.710 ⇒ 00:29:10.950 Robert Tseng: And then customer count difference.
229 00:29:13.840 ⇒ 00:29:18.929 Awaish Kumar: Yeah, that I’m not sure where it’s coming from. Like in the product sales summary, it’s lower
230 00:29:19.130 ⇒ 00:29:21.819 Awaish Kumar: again in the new table I’m building.
231 00:29:24.520 ⇒ 00:29:27.120 Awaish Kumar: And yeah, I’m not sure why.
232 00:29:28.110 ⇒ 00:29:30.170 Robert Tseng: Maybe we’re double counting customers.
233 00:29:31.180 ⇒ 00:29:39.610 Robert Tseng: because I mean, this is just a a random guess. But like I think, and you can decide. But from what I remember we’re going off of.
234 00:29:39.810 ⇒ 00:29:52.770 Robert Tseng: If the 1st time the customer buys a particular product they are counted in in that one. So let’s say a customer has ends up buying 2 products. They may what may. Maybe they’re being counted twice.
235 00:29:54.750 ⇒ 00:29:59.440 Robert Tseng: whereas product sales summary only anchors them to the 1st product. They came in on, no matter what.
236 00:30:02.620 ⇒ 00:30:09.580 Awaish Kumar: Yeah, but we basically create a window function partition by customer Id.
237 00:30:09.970 ⇒ 00:30:12.920 Awaish Kumar: and we go give the row numbers and take the 1st row.
238 00:30:13.040 ⇒ 00:30:14.170 Awaish Kumar: So it should not be.
239 00:30:14.170 ⇒ 00:30:15.580 Robert Tseng: Oh, so it should be the same thing.
240 00:30:17.790 ⇒ 00:30:19.170 Robert Tseng: Okay? Well, anyway.
241 00:30:19.170 ⇒ 00:30:19.530 Awaish Kumar: Sure.
242 00:30:19.530 ⇒ 00:30:20.639 Robert Tseng: I’ll let you look into that. Yeah.
243 00:30:20.640 ⇒ 00:30:21.430 Awaish Kumar: I’ve got.
244 00:30:24.350 ⇒ 00:30:32.480 Robert Tseng: Okay, but yeah, we should probably yeah, we should. We should. We should try to resolve that and give and give Mattesh an update sap on that.
245 00:30:32.910 ⇒ 00:30:45.260 Robert Tseng: Yeah, I feel like when I was out, we just we didn’t make progress on the marketing stuff. I know that you guys got like, derailed by a lot of random requests. So it’s not a problem. But yeah, just to get back on track there.
246 00:30:46.740 ⇒ 00:30:53.259 Robert Tseng: Yeah, I know we’re slightly bit over time. So just wanna I mean, I can keep going in and just tagging.
247 00:30:53.570 ⇒ 00:30:58.520 Awaish Kumar: Considering that for this marketing ticket, like we had further
248 00:30:58.740 ⇒ 00:31:01.031 Awaish Kumar: new models we built out.
249 00:31:03.060 ⇒ 00:31:08.139 Awaish Kumar: there is like, if you see the bottom of this ticket. There is more tickets in there.
250 00:31:13.510 ⇒ 00:31:14.250 Robert Tseng: In here.
251 00:31:14.750 ⇒ 00:31:17.220 Awaish Kumar: No, not, I think, in the bottom.
252 00:31:18.170 ⇒ 00:31:19.270 Robert Tseng: Oh! In the bottom.
253 00:31:19.580 ⇒ 00:31:20.400 Awaish Kumar: We are.
254 00:31:22.460 ⇒ 00:31:23.399 Robert Tseng: Oh, yeah. Okay.
255 00:31:23.400 ⇒ 00:31:28.090 Awaish Kumar: This this one. And inside of this we, we build like 3, 4 more models.
256 00:31:35.000 ⇒ 00:31:35.779 Robert Tseng: I see.
257 00:31:45.930 ⇒ 00:31:59.969 Robert Tseng: Okay, yeah, I mean, I I think, yeah, I know. I knew I knew we had to make new bottles. I think I’m just talking about just getting the dashboard out to him. So I know we’re slightly blocked here. But let’s just let’s just keep working on working through that.
258 00:32:00.785 ⇒ 00:32:10.169 Robert Tseng: I didn’t talk about the Ltv projections project, Annie. Maybe we’ll talk about it more tomorrow after we close out this cohort based heat mapping.
259 00:32:10.300 ⇒ 00:32:15.939 Robert Tseng: I think that would be a good win for us to get afterwards. So I definitely want to do it in this cycle.
260 00:32:16.380 ⇒ 00:32:19.930 Robert Tseng: Okay.
261 00:32:20.510 ⇒ 00:32:25.628 Robert Tseng: anything else. I will just kind of keep tagging you guys throughout the day. But I think,
262 00:32:26.520 ⇒ 00:32:31.779 Robert Tseng: yeah, if you need any other escalations like, let me know I’m I’m around today moving forward to kind of help push them.
263 00:32:35.280 ⇒ 00:32:45.409 Annie Yu: Okay, just clarifying. So for the Otv heat map, from what I understand is that we will also apply the same logic, for in Cac
264 00:32:46.060 ⇒ 00:32:46.750 Annie Yu: to this one.
265 00:32:46.750 ⇒ 00:32:48.020 Robert Tseng: Yeah, we should.
266 00:33:00.155 ⇒ 00:33:09.210 Annie Yu: Robert, I do have one more question for you. I saw that in the chat you said you were asking if I can walk through Katie.
267 00:33:09.870 ⇒ 00:33:18.970 Annie Yu: how to view our existing tableau reports it’s does that mean like the specific one or.
268 00:33:20.370 ⇒ 00:33:29.940 Robert Tseng: The original request was in that in that message. I looked at it, and I thought, I don’t really think we need to do anything for this. I think we just need to show her how to use our existing report.
269 00:33:30.150 ⇒ 00:33:33.080 Robert Tseng: I think in our order dashboard. I think
270 00:33:33.180 ⇒ 00:33:49.969 Robert Tseng: we could probably just show her how to use the filters to get. She’s just looking for daily or like delivered orders right like I think she can easily see that that table through one of our dashboards. I don’t. I just don’t think she knows where to look this. So I was just asking if you can just show show that to her.
271 00:33:51.470 ⇒ 00:34:02.780 Annie Yu: Okay, and and how? Cause I am not all. I’m also like, not super clear on how often bask, update the data and sent to us.
272 00:34:03.150 ⇒ 00:34:10.340 Robert Tseng: Oh, well, I I sent those limitations to her, so there’s no way we’ll she’ll get real time. But if she just wants to see a daily
273 00:34:10.690 ⇒ 00:34:16.439 Robert Tseng: report like we don’t need to build her a new query. So like, this is one of those examples where.
274 00:34:16.750 ⇒ 00:34:37.909 Robert Tseng: yeah, like, they’ll ask us for stuff, but I don’t think everything they asked for. We really need to build things for a lot of as much as we can drive them to use the reports that we’ve already built them, and just like teach them how to look for what they’re looking for. We should do that. And like I default to not taking on more more reporting requests.
275 00:34:38.885 ⇒ 00:34:39.540 Annie Yu: Okay.
276 00:34:39.540 ⇒ 00:34:40.120 Robert Tseng: Yeah.
277 00:34:40.420 ⇒ 00:34:43.750 Annie Yu: And Katie is on the same team as Rebecca. Is that it.
278 00:34:44.000 ⇒ 00:34:51.349 Robert Tseng: Yeah, you can just think of it as like, Rebecca is like their strategic leader. And then Katie is more of like the operational leader.
279 00:34:52.040 ⇒ 00:34:53.529 Robert Tseng: Okay, yeah.
280 00:34:59.430 ⇒ 00:35:05.620 Robert Tseng: Okay, cool. Yeah. If anything else let me know. Otherwise, I’m gonna I’m gonna jump off now.
281 00:35:07.020 ⇒ 00:35:08.569 Annie Yu: Okay. Thank you team.
282 00:35:08.570 ⇒ 00:35:09.979 Robert Tseng: Thanks everyone, bye.