Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-05-15 Meeting participants: Annie Yu, Demilade Agboola, Robert Tseng, Josh, Rob, Awaish Kumar
WEBVTT
1 00:01:22.830 ⇒ 00:01:24.480 Josh : Mr. Robert.
2 00:01:25.430 ⇒ 00:01:26.269 Robert Tseng: Hey, Josh.
3 00:01:26.920 ⇒ 00:01:28.380 Josh : What’s happening? Man.
4 00:01:30.390 ⇒ 00:01:33.639 Robert Tseng: It’s and when for the team to join.
5 00:01:34.950 ⇒ 00:01:39.450 Josh : What what are we? What are we working through right now? What are the big things.
6 00:01:41.680 ⇒ 00:01:45.500 Robert Tseng: Yeah, I think one. So I mean the whole like.
7 00:01:46.780 ⇒ 00:01:59.080 Robert Tseng: there’s like the med kit like updates the product hierarchy that you know you walk. You came. You popped in earlier this week, and you, you know we talked through that screenshot of like a whiteboard drawing. You did
8 00:01:59.621 ⇒ 00:02:23.750 Robert Tseng: so so that’s been applied. That’s across. I mean, that’s updated across the order. Level reports, I think, like the Bobby cutter kind of thing that came up yesterday was more. I mean, that’s it’s a similar like problem. But it’s not exactly the same thing. And they were asking for that same product, hierarchy to be exposed at the treatment level, because that’s how
9 00:02:24.408 ⇒ 00:02:27.200 Robert Tseng: Bobby triggers his campaigns, I guess. So.
10 00:02:27.480 ⇒ 00:02:34.390 Robert Tseng: Yeah, I mean, there was like a rabbit hole we had to go through. But, like, you know, I think this is something we didn’t real. I mean, the treatment
11 00:02:35.280 ⇒ 00:02:39.925 Robert Tseng: concept, like the treatment object isn’t in the data warehouse.
12 00:02:41.300 ⇒ 00:02:47.689 Robert Tseng: lo and behold! Like, I guess this is one of the things that Rob had set up directly to connect to customer. I/O
13 00:02:48.187 ⇒ 00:03:07.760 Robert Tseng: and just like, I mean, I guess he had set this up a while ago. So it never landed in the data warehouse. And so we’re we’re just. I think there was like questions on like, who’s actually gonna make that happen, I think, I guess. Join that call, but just joined the call. But he’s working with Rob to kind of get that routed through the warehouse.
14 00:03:08.140 ⇒ 00:03:16.590 Robert Tseng: But for now like to get Bobby what he needs, rob will just have to update whatever he had done before. And then now we picked up on
15 00:03:16.790 ⇒ 00:03:22.920 Robert Tseng: on it. So we so we’re trying to just basically follow suit and and build that in the warehouse.
16 00:03:26.270 ⇒ 00:03:28.860 Robert Tseng: I think that’s so. That’s 1 like
17 00:03:29.540 ⇒ 00:03:35.329 Robert Tseng: big lift on the engineering side that just kind of sprung up in the past day.
18 00:03:37.210 ⇒ 00:03:52.150 Robert Tseng: for Rebecca that we’ve updated the dashboard. Given her pharmacy level vial, like kind of vial size. And just like for her to be able to hit her 80% target we got gave her a good view of that, so I think she’s happy with it. There’s
19 00:03:52.330 ⇒ 00:04:04.259 Robert Tseng: it’s like a couple small tweaks that she needs to make on the data quality side, because some of the data is coming in as null. So that’s I think that was like the other big thing that we pushed out yesterday.
20 00:04:06.360 ⇒ 00:04:16.879 Robert Tseng: And then the 3rd thing is on the cro tracking side I kind of handed something off to Sebastian on
21 00:04:17.426 ⇒ 00:04:21.829 Robert Tseng: exactly what we needed to pull out of the the data layer
22 00:04:22.523 ⇒ 00:04:28.060 Robert Tseng: from bask page that already exists with every kind of
23 00:04:28.220 ⇒ 00:04:31.710 Robert Tseng: page load with within the bask app.
24 00:04:32.390 ⇒ 00:04:35.000 Robert Tseng: Yeah, I mean, I work with Ryan. Kind of
25 00:04:35.290 ⇒ 00:04:53.540 Robert Tseng: was on a call with him, understood everything he wanted to to get out of it, and then kind of just like translated that into a clear plan for for a checklist, I guess, for for Sebastian to go and make sure that we have that in Google tag manager. So those are the 3 main things that we’re kind of
26 00:04:53.780 ⇒ 00:04:55.230 Robert Tseng: worked on yesterday.
27 00:05:00.410 ⇒ 00:05:04.660 Josh : Got it. Got it. Okay, cool makes sense.
28 00:05:05.770 ⇒ 00:05:13.560 Robert Tseng: I know that was like a big amount a mouthful with. I tried to strip out some jargon, so I don’t know how much you caught of it. But if you have any questions on any of those happy to kind of
29 00:05:13.720 ⇒ 00:05:15.160 Robert Tseng: talk about, if you want.
30 00:05:19.864 ⇒ 00:05:30.559 Josh : I mean truthfully the only ones I really have questions on our just making sure that you know the team is just getting what they need like. I don’t know all the details. You know what I mean.
31 00:05:30.860 ⇒ 00:05:37.469 Josh : I just wanna make sure that, like all the things that are asked of us are getting delivered. You know what I mean.
32 00:05:38.100 ⇒ 00:05:46.500 Robert Tseng: Yeah. So I mean, just in in short, response to that, it’s like, okay, everything that we had described about the new products and the hierarchy
33 00:05:46.940 ⇒ 00:05:49.389 Robert Tseng: that’s been applied to any order level report.
34 00:05:50.320 ⇒ 00:05:56.479 Robert Tseng: We didn’t have it at the treatment level, which is what I guess. Bobby and Connor flagging yesterday morning.
35 00:05:56.960 ⇒ 00:06:00.609 Robert Tseng: And we’ve yeah. We had to take a detour to figure out how to get that
36 00:06:00.760 ⇒ 00:06:06.500 Robert Tseng: to work. I think we know what we need to do moving forward now. So it does involve us, like just kind of
37 00:06:07.340 ⇒ 00:06:09.500 Robert Tseng: working with Rob to to bring.
38 00:06:09.500 ⇒ 00:06:10.250 Josh : It’s great!
39 00:06:10.250 ⇒ 00:06:15.359 Robert Tseng: That level of granularity in as well. So that was like just the detour. Yeah.
40 00:06:15.360 ⇒ 00:06:17.430 Josh : Okay.
41 00:06:18.990 ⇒ 00:06:26.120 Josh : makes sense, and then, in terms of like, you know, the stuff that you need to get from bask. Are you like
42 00:06:26.410 ⇒ 00:06:28.620 Josh : you getting everything like, are we good.
43 00:06:29.120 ⇒ 00:06:37.219 Robert Tseng: I mean you saw me make some noise in the Channel, I mean, I think T. Ground is kind of logging. Some of the recurring requests now. So I mean.
44 00:06:37.550 ⇒ 00:06:46.520 Robert Tseng: you know, Zach supposed to send us every Thursday an updated product list. He still hasn’t. But I nudged him again today. Tim Laude has went unanswered for 3 times. So
45 00:06:47.032 ⇒ 00:06:55.159 Robert Tseng: that’s 1 thing. If you notice in the channel you could just kind of follow along on, and then the other thing that we were.
46 00:06:55.160 ⇒ 00:06:55.730 Josh : Kid in this.
47 00:06:55.730 ⇒ 00:06:58.200 Robert Tseng: I didn’t actually ask it of him. It was.
48 00:06:58.370 ⇒ 00:07:06.940 Robert Tseng: you know, we we went. This was back to the whole like orphan transactions. Conversation we had before, like almost like 3 weeks ago. At this point.
49 00:07:06.940 ⇒ 00:07:07.440 Josh : Yeah.
50 00:07:08.300 ⇒ 00:07:24.199 Robert Tseng: You know, Zack pretty much directed us to like. Hey? You can go and solve it by, you know, accessing treatments, and so I think this is somewhat tied to this like new treatment thing that we’re trying to figure out with Rob. But like, yeah, I mean that.
51 00:07:24.570 ⇒ 00:07:33.910 Robert Tseng: What he said that he would, he would look into it and give us a clear mapping. He never did. And so I’m trying to give him like a very specific like this is how I think the mapping should be.
52 00:07:34.470 ⇒ 00:07:57.680 Robert Tseng: But I’m worried about not telling him what to do. So like. I’m just making sure that after once Rob Dinlotte and I kind of are are clear on like what? Exactly we’re gonna ask from him. Then then I’ll ask him that, hey? I want it to be mapped this way. So that’s that. There’s 1 request that I held off on because I wasn’t super clear on what the ask was for him.
53 00:07:59.890 ⇒ 00:08:00.850 Josh : Got it.
54 00:08:01.330 ⇒ 00:08:01.960 Robert Tseng: Yeah.
55 00:08:02.310 ⇒ 00:08:03.460 Josh : So what do I.
56 00:08:05.684 ⇒ 00:08:12.779 Robert Tseng: Yeah. So I mean, I guess we’re gonna get to that answer like, shortly after this, I know, hey, Rob, I know you just joined. Yeah, I think.
57 00:08:13.483 ⇒ 00:08:24.439 Robert Tseng: Don’t wanna re- repeat it right now. But Robert, gonna I mean, I’ll probably join that call, too. We’re gonna figure out what- what that- what? That correct what- what that question should be, and
58 00:08:25.041 ⇒ 00:08:36.799 Robert Tseng: once we send it to mass. I’ll probably give you another update, and then you can help us nudge that for that, because that’s probably the most urgent one for the purposes of like Cutter Bobby. Right now.
59 00:08:43.960 ⇒ 00:08:45.710 Josh : Or is that to me, or to rob.
60 00:08:46.020 ⇒ 00:08:51.070 Robert Tseng: Oh, that was to you, Josh, I was just like letting you know, because you’re asking, what can I do? I was like, basically.
61 00:08:51.839 ⇒ 00:08:58.030 Robert Tseng: we’re gonna we’re gonna send them a message, and I’ll let you know, like, Hey, this is the message we need to push on, because, like this is. That’s the urgent
62 00:08:58.360 ⇒ 00:09:05.609 Robert Tseng: that we’re gonna need from him in order to unblock something for Bobby and and Cutter.
63 00:09:06.870 ⇒ 00:09:11.970 Josh : Got it. Okay, cool. Shoot the note, tag me in it, and I’ll make sure I push it to the top. Okay.
64 00:09:12.230 ⇒ 00:09:13.499 Robert Tseng: Okay. Thank you.
65 00:09:14.070 ⇒ 00:09:15.329 Josh : Awesome thanks guys.
66 00:09:15.530 ⇒ 00:09:16.410 Robert Tseng: Yep.
67 00:09:19.200 ⇒ 00:09:25.028 Robert Tseng: yeah. So I guess let’s kind of catch up. I know, Rob. There’s like a lot going on with red.
68 00:09:26.250 ⇒ 00:09:29.430 rob: Yeah, along. I was just kidding caught up.
69 00:09:29.670 ⇒ 00:09:35.390 Robert Tseng: Yeah, I think if Tim Lade, you and I can get on a call, or if if I even if I’m not available like
70 00:09:36.109 ⇒ 00:09:40.860 Robert Tseng: we have some questions. So we could work through that after after this, if that’s okay.
71 00:09:42.311 ⇒ 00:09:44.538 Robert Tseng: yeah, do you guys just wanna
72 00:09:45.770 ⇒ 00:09:51.640 rob: Well, no, you could finish your stand up, and then you wanna just hit me up and we’ll total.
73 00:09:51.640 ⇒ 00:09:55.720 Robert Tseng: Yeah. So I have a 11. I have a I have to.
74 00:09:55.960 ⇒ 00:10:03.020 Robert Tseng: I have like an 1130 to 12, et like call so. But right after I’m I’m flexible after that. So
75 00:10:03.536 ⇒ 00:10:10.920 Robert Tseng: if we could coordinate, I guess, Rob, maybe just let me know like when when’s your earliest availability? And then we’ll just try to throw some time on your calendar.
76 00:10:11.860 ⇒ 00:10:18.419 rob: I’m actually pretty good today. So just about any time I’d rather do it earlier than later, though.
77 00:10:18.610 ⇒ 00:10:19.530 Robert Tseng: Yeah. Totally.
78 00:10:19.940 ⇒ 00:10:20.940 Robert Tseng: Okay.
79 00:10:20.940 ⇒ 00:10:22.779 rob: Tell me the earliest you’re available.
80 00:10:23.050 ⇒ 00:10:25.170 Demilade Agboola: Can we do? Can we do 12 et? Then.
81 00:10:25.170 ⇒ 00:10:26.680 Robert Tseng: Yeah, can we do 12 et.
82 00:10:26.980 ⇒ 00:10:28.280 rob: Yeah, let’s do it.
83 00:10:28.280 ⇒ 00:10:29.660 Robert Tseng: Okay, cool.
84 00:10:30.081 ⇒ 00:10:31.419 Robert Tseng: So I’ll I’ll throw that time on.
85 00:10:31.420 ⇒ 00:10:33.350 rob: In 50 min. Right.
86 00:10:33.500 ⇒ 00:10:34.560 Robert Tseng: Right, right, yeah.
87 00:10:34.560 ⇒ 00:10:38.409 rob: Okay, alright. Then I will jump. We’ll talk then.
88 00:10:38.690 ⇒ 00:10:39.969 rob: Thank you. Yeah. Alright.
89 00:10:39.970 ⇒ 00:10:41.020 rob: See, you guys, bye.
90 00:10:41.020 ⇒ 00:10:48.799 Robert Tseng: Yes, okay. So while I’m throwing that on let’s kinda just go through.
91 00:10:50.510 ⇒ 00:10:57.780 Robert Tseng: Yeah. Maybe we’ll start with Annie. Since I know we you had shipped out some stuff last day.
92 00:10:59.330 ⇒ 00:11:08.630 Annie Yu: Yeah. So the actually the message. And like the section for Rebecca yesterday, that was not for
93 00:11:08.960 ⇒ 00:11:15.540 Annie Yu: customer journey dashboard, which I think you update things there? So that’s
94 00:11:15.790 ⇒ 00:11:24.130 Annie Yu: mainly for the personalized plan versus conventional. And I added that section into product, drill down dashboard.
95 00:11:26.424 ⇒ 00:11:30.689 Robert Tseng: Okay, yeah, I mean, sorry. I just didn’t know which ticket it was. Second.
96 00:11:30.960 ⇒ 00:11:31.800 Annie Yu: Yeah, I think we
97 00:11:32.245 ⇒ 00:11:46.059 Annie Yu: yeah. But anyway, yeah. So I am gonna make some change that she mentioned. I think one question, though I I did follow up with her, but just earlier.
98 00:11:46.180 ⇒ 00:11:56.880 Annie Yu: So if you you have answers to that, she was saying that let me see.
99 00:11:57.520 ⇒ 00:12:04.070 Annie Yu: So for the personalized, we would just have 2 pharmacies boost win and pharmacy hub.
100 00:12:04.710 ⇒ 00:12:05.090 Robert Tseng: Yep.
101 00:12:05.090 ⇒ 00:12:07.659 Annie Yu: And then in that
102 00:12:08.090 ⇒ 00:12:22.900 Annie Yu: kind of section, I did see another pharmacy which is called precision telemedicine pharmacy, which doesn’t have any personalized plan, at least as of now. So I think my question is is that like 80% goal
103 00:12:23.990 ⇒ 00:12:30.170 Annie Yu: applies to all pharmacy with injectable Sema, or really just boost win and pharmacy Hub, because I think
104 00:12:31.160 ⇒ 00:12:32.449 Annie Yu: it’ll make a difference.
105 00:12:33.640 ⇒ 00:12:34.739 Robert Tseng: I see.
106 00:12:35.620 ⇒ 00:12:39.739 Annie Yu: Because right now, from that, telemedicine pharmacy is.
107 00:12:40.450 ⇒ 00:12:44.249 Robert Tseng: Including it in the denominator. And so it’s making the percentage lower.
108 00:12:44.250 ⇒ 00:12:45.329 Annie Yu: Yeah, yeah.
109 00:12:45.330 ⇒ 00:12:45.900 Robert Tseng: Yeah.
110 00:12:48.620 ⇒ 00:12:50.880 Annie Yu: So yeah, I don’t question for her.
111 00:12:50.880 ⇒ 00:12:52.210 Robert Tseng: Yeah, that’s a question for her.
112 00:12:54.090 ⇒ 00:13:00.049 Demilade Agboola: Out of curiosity. If you filter by pharmacy, does it? Does it change the percentages to what it should be, or what it looks like?
113 00:13:00.050 ⇒ 00:13:03.039 Annie Yu: So I think, so. Yeah, I believe so.
114 00:13:04.390 ⇒ 00:13:05.300 Robert Tseng: Slightly.
115 00:13:05.770 ⇒ 00:13:07.179 Robert Tseng: I mean, yeah, it’s.
116 00:13:07.500 ⇒ 00:13:09.990 Annie Yu: Yeah, I mean, it makes it makes a difference. Yeah.
117 00:13:11.250 ⇒ 00:13:19.220 Annie Yu: But that’s a good call out, yeah, as as if it it’s not included. We can just filter that out. So.
118 00:13:21.690 ⇒ 00:13:29.300 Robert Tseng: I would say it’s fine like if she wants to. She can filter it out. Yeah, I think I mean she. If if she gets back to us, and I think
119 00:13:30.450 ⇒ 00:13:42.299 Robert Tseng: I mean, she’s a bit confused. So it takes a lot of us asking the same question like 5 different ways for her to like, actually give us the answer that we want. So, as you guys have probably realized by now. So.
120 00:13:42.300 ⇒ 00:13:48.010 Annie Yu: I think she actually answered, at least to me, this time pretty quickly, so.
121 00:13:48.390 ⇒ 00:13:50.089 Robert Tseng: Oh, right? Yeah, yeah.
122 00:13:50.430 ⇒ 00:13:58.999 Robert Tseng: yeah. I mean, this one was clear. So I think it’s just like a renaming in one of these things. And then, yeah, I mean, as far as this part, I think that should be fine.
123 00:13:59.287 ⇒ 00:14:07.919 Annie Yu: Sorry one follow up about Oly. I know that, she said. It’s gonna be in pharmacy hub, and then we already have a pharmacy Hub, Llc. So I assume that
124 00:14:08.110 ⇒ 00:14:10.350 Annie Yu: they should be just one right.
125 00:14:10.350 ⇒ 00:14:15.910 Robert Tseng: Yeah, yeah, although I don’t see that here.
126 00:14:15.910 ⇒ 00:14:19.540 Annie Yu: Oh, here I only show the relevant pharmacies. But if.
127 00:14:19.540 ⇒ 00:14:19.950 Robert Tseng: Bye.
128 00:14:19.950 ⇒ 00:14:22.690 Annie Yu: The very top you can see all the pharmacy.
129 00:14:23.550 ⇒ 00:14:30.379 Annie Yu: Oh, actually wait! I don’t have a pharmacy filter on there. Maybe we should.
130 00:14:35.710 ⇒ 00:14:41.320 Robert Tseng: Okay? And then I also, you’re saying, I pretty much like mess this up. This is the wrong ticket.
131 00:14:41.320 ⇒ 00:14:43.580 Annie Yu: Yeah, that’s a customer journey.
132 00:14:46.350 ⇒ 00:14:53.000 Demilade Agboola: I’m out of curiosity. Is there any reason why we don’t have pharmacy hub in the dashboard?
133 00:14:54.260 ⇒ 00:14:55.230 Robert Tseng: Sorry, what what.
134 00:14:56.460 ⇒ 00:15:00.730 Demilade Agboola: In the dashboard, for, like personalized planning.
135 00:15:02.640 ⇒ 00:15:04.389 Robert Tseng: Yeah, that’s why I was asking.
136 00:15:06.150 ⇒ 00:15:11.970 Robert Tseng: But yeah, and he said, relevant.
137 00:15:12.490 ⇒ 00:15:20.889 Annie Yu: Yeah, I think for for this section I only show the pharmacy that’s relevant to the product name just because we only have
138 00:15:21.460 ⇒ 00:15:31.050 Annie Yu: to track injectable, Sema here. But the request from Rebecca was OLY. Should be pharmacy hub, so I’ll make that change.
139 00:15:32.540 ⇒ 00:15:40.040 Demilade Agboola: Okay, I think I I think I know what’s going on. I will make a a model fix to that.
140 00:15:41.190 ⇒ 00:15:41.890 Annie Yu: Okay.
141 00:15:42.400 ⇒ 00:15:45.950 Demilade Agboola: And then I’ll push that in today.
142 00:15:46.690 ⇒ 00:15:47.340 Robert Tseng: Okay.
143 00:15:47.520 ⇒ 00:15:48.259 Annie Yu: Okay, do you need.
144 00:15:48.260 ⇒ 00:15:48.760 Robert Tseng: Sorry.
145 00:15:48.760 ⇒ 00:15:49.390 Annie Yu: That.
146 00:15:51.230 ⇒ 00:15:51.970 Demilade Agboola: Pardon.
147 00:15:52.250 ⇒ 00:15:53.859 Annie Yu: You need a ticket for that.
148 00:15:54.750 ⇒ 00:15:57.459 Demilade Agboola: I mean, sure, we could draft a ticket. But yeah.
149 00:15:58.900 ⇒ 00:16:03.310 Robert Tseng: Yeah, I, Annie, I just I don’t know what to label this like, I don’t see any ticket that.
150 00:16:03.793 ⇒ 00:16:07.660 Annie Yu: I would probably put that under personalized plan.
151 00:16:08.730 ⇒ 00:16:13.380 Annie Yu: but I know that you have some other feedback for the dashboard itself
152 00:16:13.590 ⇒ 00:16:20.190 Annie Yu: and other sections, and that would be under product. Drill down dashboard.
153 00:16:20.190 ⇒ 00:16:20.820 Robert Tseng: Okay.
154 00:16:21.520 ⇒ 00:16:29.439 Robert Tseng: So like, I mean, I know we’re conflating it right now. And like, rather than just, is it okay? If I just let I left it in here like, is it?
155 00:16:30.070 ⇒ 00:16:34.839 Robert Tseng: Does it make a difference like? Are we still waiting on anything else? Is this is this confusing.
156 00:16:35.528 ⇒ 00:16:42.129 Annie Yu: Probably cause for this one. We are gonna continue with the the file size and all that.
157 00:16:42.750 ⇒ 00:16:45.320 Robert Tseng: Okay. So we’re still block that. And this doesn’t. Oh.
158 00:16:45.978 ⇒ 00:16:52.229 Demilade Agboola: I I want to. On that note. I could quickly share something. So there is. But it’s still like
159 00:16:53.240 ⇒ 00:16:54.290 Demilade Agboola: weird
160 00:16:58.340 ⇒ 00:16:59.620 Demilade Agboola: I’ll need to share my screen.
161 00:17:00.180 ⇒ 00:17:01.369 Robert Tseng: Yeah. Go ahead.
162 00:17:06.450 ⇒ 00:17:12.429 Demilade Agboola: To share alright. So this is the this is the sheet that
163 00:17:13.190 ⇒ 00:17:15.319 Demilade Agboola: Rebecca’s feeling. Can you see my screen.
164 00:17:15.760 ⇒ 00:17:16.380 Robert Tseng: Yeah.
165 00:17:16.589 ⇒ 00:17:22.609 Demilade Agboola: Alright. So she’s fitting in the valve sizes. And this is like super weird in the sense of like.
166 00:17:24.319 ⇒ 00:17:27.489 Demilade Agboola: how do you necessarily am I supposed to like.
167 00:17:28.519 ⇒ 00:17:41.539 Demilade Agboola: I don’t know. It’s just weird. It’s like a like. So, for instance, it’s clear when it’s just 3. But when for a particular variant. It’s 1, 1, 2, 2, 3. How do I go? Hey? This is the valve size for this variant.
168 00:17:42.318 ⇒ 00:17:46.289 Demilade Agboola: So I would. I’ll reach out to her like, and just like message her on that.
169 00:17:46.750 ⇒ 00:17:49.719 Robert Tseng: Well, I guess, like what she had said before was like.
170 00:17:49.860 ⇒ 00:17:53.609 Robert Tseng: okay, so let’s just pick one that’s complicated. So like the starter pack.
171 00:17:53.940 ⇒ 00:17:55.480 Robert Tseng: The first, st
172 00:17:55.590 ⇒ 00:18:03.370 Robert Tseng: you know the first.st So it’s like, you know, we’re gonna I don’t know. I guess we’re gonna see 4 or 6 orders over time.
173 00:18:04.166 ⇒ 00:18:06.539 Robert Tseng: If it’s the 1st order of the starter
174 00:18:07.100 ⇒ 00:18:19.120 Robert Tseng: like, it’s good. We’re gonna have 6 to like 6 orders with this, with this skew on it. The 1st 2 will be one quantity, and then next the 3rd one will be 2, 3, 4, 4, right, and then it keeps going.
175 00:18:19.230 ⇒ 00:18:33.549 Robert Tseng: So I guess, like if we, you know, I know people can change between plans. Let’s say we find a patient that only has 2 semaglutide, 2.5 starter packs, and then they switched to like
176 00:18:33.650 ⇒ 00:18:35.030 Robert Tseng: a quarterly plan.
177 00:18:35.140 ⇒ 00:18:48.949 Robert Tseng: and let’s say that they had 3 lifetime orders. Then the 1st order would have a quantity of one or file size, one quantity, one second, 1 1, and then the 3rd one would it would switch down to 3, 6.
178 00:18:49.190 ⇒ 00:18:50.460 Robert Tseng: Does that make sense?
179 00:18:53.040 ⇒ 00:18:54.680 Robert Tseng: Was that too much? You could say.
180 00:18:55.190 ⇒ 00:18:59.059 Demilade Agboola: Yeah, it’s just I. I’m still not clear. I don’t know. Maybe I need to like
181 00:18:59.430 ⇒ 00:19:03.000 Demilade Agboola: have it like, maybe write it. So it becomes clear to me, but like
182 00:19:03.220 ⇒ 00:19:06.212 Demilade Agboola: visualizing, it’s just a bit cause like
183 00:19:07.690 ⇒ 00:19:11.550 Demilade Agboola: So this sounds like this is one round
184 00:19:11.970 ⇒ 00:19:23.060 Demilade Agboola: and in one round, because when it comes to refills. I also want to visualize it in terms of refills as well, because ultimately, what we’re trying to do is like for each file size, how many fields are left?
185 00:19:25.660 ⇒ 00:19:35.039 Demilade Agboola: So what? When someone comes here and is on this plan, for instance, semaglutide plus B 12,
186 00:19:35.190 ⇒ 00:19:37.590 Demilade Agboola: and it’s 2, 3, 4, 4 4.
187 00:19:38.768 ⇒ 00:19:45.340 Demilade Agboola: My guess is, are they getting all of them at once? And so we don’t need to care about refills.
188 00:19:45.730 ⇒ 00:19:50.659 Demilade Agboola: or are they? Is this like each one is coming in.
189 00:19:52.380 ⇒ 00:20:02.380 Robert Tseng: Yeah, each one is coming in at a different account. Like one of the first.st Yeah, it’s like the 1st one they get is 2, and the second one they get is 3, and the 3rd one they get is 4.
190 00:20:04.010 ⇒ 00:20:08.080 Demilade Agboola: Okay. So now I need to check. If does this, how does this count in terms of refills? Though.
191 00:20:11.000 ⇒ 00:20:18.509 Demilade Agboola: like, I know, you don’t know the answer to that. That’s fine. But, like I’m saying like, I’m just wondering aloud, how does that count in terms of like refills? And like, if we’re.
192 00:20:18.510 ⇒ 00:20:27.860 Robert Tseng: It wasn’t refills. Just how many is left so like if they only did 3, then the number of refills left is 3 like it’s not, I thought. Refills doesn’t.
193 00:20:28.010 ⇒ 00:20:33.230 Robert Tseng: doesn’t have anything to do with file size and quantity is, it’s more like, how many
194 00:20:33.700 ⇒ 00:20:40.509 Robert Tseng: more like shipments do. They have like orders. Do they have remaining on that plan that they sign up for?
195 00:20:41.900 ⇒ 00:20:45.980 Robert Tseng: I mean, we could clarify. But I got that’s that’s what I thought. Refills. What meant.
196 00:20:47.210 ⇒ 00:20:57.909 Annie Yu: Wait. So does that mean? We only care about how many refills, but we don’t care about among all those remaining refills. What are the file sizes.
197 00:20:59.400 ⇒ 00:21:08.910 Robert Tseng: No, we we do, because ultimately, like the the remaining refills. That’s that’s basically the expected demand.
198 00:21:09.020 ⇒ 00:21:09.510 Robert Tseng: Last
199 00:21:09.920 ⇒ 00:21:38.570 Robert Tseng: right? And then you take that, you put that into your forecast for the pharmacy, so you need to be able to take refill like the refill metric is not something that the pharmacy cares about. That’s more of an operational one for us to know how many people like. Which stage are they in in their plan? Like? Are they dropping out after the 1st 2 like orders, or whatever like, are people actually like maxing out their entire plan, or they like dropping off the 1st orders, and then the vial size and quantity stuff that’s more of the pharmacy specific metric.
200 00:21:38.930 ⇒ 00:21:50.110 Robert Tseng: So I mean, yeah, we should definitely clarify the relationship between refills by quantity and vial size, or whatever those 3 3 kind of
201 00:21:50.250 ⇒ 00:21:53.770 Robert Tseng: abstractions are. But like, yeah, like, that’s.
202 00:21:53.960 ⇒ 00:21:56.530 Robert Tseng: I mean, that’s that’s how I think about it right now.
203 00:21:57.570 ⇒ 00:22:03.960 Demilade Agboola: Okay, yeah, I’ll I’ll clarify with Rebecca. But yeah, this is the source of like, the is personalized plan and stuff.
204 00:22:04.440 ⇒ 00:22:08.940 Robert Tseng: She only did it for, like I don’t know, like 20 of them, and she didn’t do it for the rest.
205 00:22:09.670 ⇒ 00:22:15.340 Demilade Agboola: Well, there’s some that still missing, but she’s done it for a decent chunk. So personalized plans for, like everything.
206 00:22:16.940 ⇒ 00:22:18.729 Robert Tseng: Oh, oh, got it? Okay.
207 00:22:18.730 ⇒ 00:22:21.810 Demilade Agboola: But then, for the Val size is still like a work in progress.
208 00:22:22.460 ⇒ 00:22:23.150 Robert Tseng: Yeah.
209 00:22:23.860 ⇒ 00:22:24.870 Robert Tseng: Okay.
210 00:22:35.690 ⇒ 00:22:47.459 Robert Tseng: okay, yeah, yeah. This is my bad. I’ll split it out to a new ticket. Yeah, in the future if I don’t know which ticket is. I’m just gonna create a new one rather than just guessing which one I’m gonna.
211 00:22:47.835 ⇒ 00:22:48.210 Annie Yu: Okay.
212 00:22:48.210 ⇒ 00:22:48.950 Robert Tseng: My feedback on.
213 00:22:49.120 ⇒ 00:22:55.809 Annie Yu: Yeah, yeah, for that. One part of it is personalized ticket. And part of it is.
214 00:22:56.270 ⇒ 00:23:03.530 Annie Yu: I don’t think we have an open ticket for product drill down as of yeah.
215 00:23:05.840 ⇒ 00:23:09.220 Annie Yu: yeah. But that’s an easy fix, and.
216 00:23:09.220 ⇒ 00:23:10.120 Robert Tseng: Anything else.
217 00:23:10.120 ⇒ 00:23:12.819 Annie Yu: Have that otv redefine.
218 00:23:13.100 ⇒ 00:23:13.830 Robert Tseng: Yeah.
219 00:23:13.940 ⇒ 00:23:17.000 Annie Yu: A different back there.
220 00:23:17.380 ⇒ 00:23:20.109 Robert Tseng: Oh, I did open it. I just haven’t reviewed it yet. Okay.
221 00:23:20.850 ⇒ 00:23:35.649 Annie Yu: Yeah. So I’m proposing like, and just so, you know, I did look into the past ticket, and I just I can’t find anything traceable. That explains why the team is using 12 months lacked. Ltv, but
222 00:23:35.870 ⇒ 00:23:51.039 Annie Yu: with that I’m proposing like 2 options. One’s more short term, and I’m really just mimicking what we did for Joby like this lifetimely style heat map. So also, like very similar to the retention Dashboard preferred this one. We would
223 00:23:52.777 ⇒ 00:23:59.920 Annie Yu: track each cohort, and then their cumulative revenue.
224 00:24:00.090 ⇒ 00:24:00.540 Robert Tseng: Yeah.
225 00:24:00.540 ⇒ 00:24:01.670 Annie Yu: Over time.
226 00:24:02.223 ⇒ 00:24:11.760 Annie Yu: And I think this is more straightforward and no no projections, no assumptions. And then that could be a short term solution
227 00:24:12.050 ⇒ 00:24:14.680 Annie Yu: longer term. I’m
228 00:24:15.640 ⇒ 00:24:23.410 Annie Yu: proposing like a more comprehensive model, but then I split them into 3 faces. I think the 1st one is
229 00:24:23.560 ⇒ 00:24:30.739 Annie Yu: to evaluate what kind of. But what variables are important
230 00:24:31.730 ⇒ 00:24:37.990 Annie Yu: kind of their correlation with the final Ltv, and then to determine
231 00:24:38.490 ⇒ 00:24:41.430 Annie Yu: what to use for the predictive modeling.
232 00:24:41.570 ⇒ 00:24:53.109 Annie Yu: or even like, do we need a model at all? And then the modeling phases, I think, depends on the phase. One result we would choose the best model to use.
233 00:24:53.460 ⇒ 00:25:00.740 Annie Yu: and then outputs would be like a 12 months prediction per.
234 00:25:01.420 ⇒ 00:25:06.179 Annie Yu: What’s that predicted? 12 month, predicted Ltb. Per customer.
235 00:25:06.180 ⇒ 00:25:06.750 Robert Tseng: Yep.
236 00:25:06.920 ⇒ 00:25:20.160 Annie Yu: And then, in phase 3, we can obviously apply that into the same like cohort level, summary. But then I think, what’s even like more appealing to me is the customer level segmentation. So we
237 00:25:20.510 ⇒ 00:25:23.850 Annie Yu: each customer with like high medium, low Ltv.
238 00:25:24.570 ⇒ 00:25:25.270 Robert Tseng: Okay.
239 00:25:25.500 ⇒ 00:25:40.780 Robert Tseng: Great. No. I mean, I think, yeah, I that notion that I sent you Bo had worked on something like this before. He was the 1st one to like kind of do something like that. He basically used Facebook’s profit model to kind of like fit like his prediction onto it. And then he.
240 00:25:40.780 ⇒ 00:25:46.789 Annie Yu: You share it. I think I saw like a model.
241 00:25:46.790 ⇒ 00:25:51.339 Robert Tseng: Yep, yeah, it was in there. Yeah. So
242 00:25:52.150 ⇒ 00:25:57.070 Robert Tseng: yeah, so this was, I mean, it wasn’t like.
243 00:25:59.710 ⇒ 00:26:10.300 Robert Tseng: yeah, it’s it’s not exactly what you’re saying, Yeah, he just built like a decay model. And then you could adjust for like, the Oh, yeah, how much you wanted to ask. Like.
244 00:26:10.450 ⇒ 00:26:19.289 Robert Tseng: I actually don’t understand like why you would set it up this way. I don’t understand what the Slider for the Ltv calculation was, but like, I just remember, like you could drag it.
245 00:26:19.450 ⇒ 00:26:47.559 Robert Tseng: You could say, you want to see decay over 36 months, or like versus 12 months or something. And yeah, well, anyway, like it was. It was a bit abstract like, I think it was hard to interpret. But somehow, from using this, Eden was like, Okay, that means that our Ltv. Timeline is 12 months, so like I, I don’t fully get it. I think this was this was part of the dashboard, like he had some out calculation here, and what he had convinced the Eden team that this was the number
246 00:26:47.690 ⇒ 00:26:53.989 Robert Tseng: I had tried so many times to get him to explain to me like what he was trying to do here. I didn’t understand it. To be honest.
247 00:26:54.250 ⇒ 00:27:00.389 Robert Tseng: I feel like this makes more sense to me like this is kind of what I would. This is how I would think about it as well. So
248 00:27:01.020 ⇒ 00:27:05.490 Robert Tseng: I mean, as far as like choosing the model, whatever like. I think there are already a bunch of Ecom
249 00:27:05.640 ⇒ 00:27:16.290 Robert Tseng: like the Facebook profit model is like industry standard, I think, for Ecom companies, because it has built in seasonality like you could, we could use it. So yeah, it’s a, it’s a regression model. Yeah.
250 00:27:16.520 ⇒ 00:27:17.470 Robert Tseng: So
251 00:27:17.610 ⇒ 00:27:28.680 Robert Tseng: I mean, I think that’s easier to interpret than the decay. But we can. We can get. We can talk about it when we get there. But yeah, as far as like, kind of the short term I read. This makes more sense to me.
252 00:27:30.390 ⇒ 00:27:49.940 Robert Tseng: Yeah, like, especially if you can filter by like product. So you still always have, like the monthly cohorted pay like cost like patient like Ltv, and then you can. You can filter it, based on the products that they that they’ve ordered, or I guess you’re doing it from their 1st order, because you’re just doing.
253 00:27:50.110 ⇒ 00:27:56.049 Annie Yu: We actually already have that. And I called it hybrid, because I think what we currently have is per
254 00:27:56.645 ⇒ 00:28:03.610 Annie Yu: we do take their 1st order month, as well as their 1st order product, so.
255 00:28:03.840 ⇒ 00:28:04.660 Robert Tseng: Oh, yeah.
256 00:28:04.660 ⇒ 00:28:05.450 Annie Yu: That’s right. Yeah.
257 00:28:07.660 ⇒ 00:28:20.009 Robert Tseng: Okay, well, so like, I guess the functionality that this was gonna be missing is like, well, so
258 00:28:20.970 ⇒ 00:28:25.710 Robert Tseng: in this version, right now.
259 00:28:29.010 ⇒ 00:28:33.970 Robert Tseng: whether this is the right way to view it or not. Like they. They want to see it at a product
260 00:28:34.100 ⇒ 00:28:34.770 Robert Tseng: level.
261 00:28:35.240 ⇒ 00:28:38.120 Robert Tseng: That’s why it exists in this dashboard.
262 00:28:38.700 ⇒ 00:28:39.820 Robert Tseng: So
263 00:28:40.720 ⇒ 00:28:58.830 Robert Tseng: yeah, I mean, right? Now, I think this is basically Ltv of people who came in, and their 1st product was injectable. So it’s probably similar logic to the hybrid model that we’re are using. But I’m just like letting you know. That’s why it’s viewed this way and not in the in the like the waterfall.
264 00:28:59.550 ⇒ 00:29:11.819 Robert Tseng: right? So we call it waterfall like. Why, it’s not viewed in this way. So I I’m I’m totally open to updating it. But you know, this basically just smushes down.
265 00:29:17.270 ⇒ 00:29:18.919 Robert Tseng: all of the.
266 00:29:20.380 ⇒ 00:29:27.259 Robert Tseng: But you’re you’re basically removing this part of the pivot table. You’re taking out the cohorts. And you’re just kind of
267 00:29:27.450 ⇒ 00:29:32.410 Robert Tseng: like this will include, you know, like this includes everybody that
268 00:29:32.770 ⇒ 00:29:43.709 Robert Tseng: started in September and started before September, or whatever right it’s not, it’s not, they don’t. It doesn’t actually break it out by like the month cohort. It’s an aggregation, and every month.
269 00:29:43.710 ⇒ 00:29:46.849 Annie Yu: Does. I think it does, at least by
270 00:29:47.350 ⇒ 00:29:50.570 Annie Yu: how the model is building. I think we do have.
271 00:29:51.220 ⇒ 00:29:55.200 Annie Yu: So think of it as like like, there’s
272 00:29:57.180 ⇒ 00:30:04.329 Annie Yu: a mass cohort for each product. So we have, like, January 25, injectable Sema and January 25
273 00:30:05.300 ⇒ 00:30:08.140 Annie Yu: whatever like product B.
274 00:30:08.140 ⇒ 00:30:08.900 Robert Tseng: Yeah.
275 00:30:10.000 ⇒ 00:30:13.500 Annie Yu: Which I think is what we need.
276 00:30:22.210 ⇒ 00:30:22.640 Robert Tseng: Okay.
277 00:30:22.640 ⇒ 00:30:28.240 Annie Yu: Myself. But I I do think we do have that granularity already.
278 00:30:28.450 ⇒ 00:30:31.070 Annie Yu: So the cohort, months by product.
279 00:30:31.820 ⇒ 00:30:32.520 Robert Tseng: Okay.
280 00:30:37.630 ⇒ 00:30:38.800 Robert Tseng: interesting.
281 00:30:41.670 ⇒ 00:30:53.225 Robert Tseng: I guess when you do end up building it, I would probably need a walkthrough of like, how is viewing the business this way, different from this way and like, like, what are the trade offs you’re making?
282 00:30:53.800 ⇒ 00:30:59.060 Robert Tseng: because you’re clearly hiding something by just like aggregating it by bar here right like
283 00:30:59.180 ⇒ 00:31:03.359 Robert Tseng: like, if I were to just naively think about this like
284 00:31:05.327 ⇒ 00:31:18.150 Robert Tseng: this is one since 1st quarter February. This only includes people who started in February like, I don’t think that that’s the same way that this visualization we already have a report that looks
285 00:31:18.270 ⇒ 00:31:24.540 Robert Tseng: like this, I think. But like, that’s that’s what I’m saying. Like, this view is different from
286 00:31:24.710 ⇒ 00:31:28.749 Robert Tseng: from this team, right? Like or like. Or if, like, there’s
287 00:31:29.570 ⇒ 00:31:33.360 Robert Tseng: well, yeah, like, that’s that’s what that’s that’s what I’m trying to say.
288 00:31:34.140 ⇒ 00:31:35.760 Annie Yu: Yeah, okay, I
289 00:31:36.550 ⇒ 00:32:06.419 Annie Yu: I think I’ll have a better way to explain it. Once I start building it. But I think, my, I I do believe we already have what we need to build that based on the model structure. And I think one thing about that is just because we are aggregating things by month and by product. We have to for kind of force people to filter on one product only, but not overall.
290 00:32:10.030 ⇒ 00:32:10.680 Robert Tseng: Yeah.
291 00:32:11.660 ⇒ 00:32:19.280 Annie Yu: Yeah, I. But yeah, I’m just thinking out loud. And I’ll have a better explanation as I go.
292 00:32:21.190 ⇒ 00:32:26.329 Robert Tseng: Okay, I know we’re running a little over time. But let me just close this out. So
293 00:32:27.250 ⇒ 00:32:29.419 Robert Tseng: yeah, I do think that this is.
294 00:32:29.800 ⇒ 00:32:36.550 Robert Tseng: Yeah, I I agree with the short term. If you want, I mean, I can try to. I’ll get get a
295 00:32:37.540 ⇒ 00:33:02.239 Robert Tseng: I basically need to tell Josh, like, Hey, like this is how we were doing. Ltv. Before. This is how I think we should be looking at product level. So there’s a gap in that kind of translation. I can draft the 1st message, or you can draft the first.st How about you draft the 1st message on like? Why, you think the solution is better than what we currently use. And then I can kind of tweak it and send that to him before we start actually building it.
296 00:33:02.520 ⇒ 00:33:18.529 Annie Yu: Okay, yeah. And I would say, like, look at that. Like he map chart you like. If you rotate it, 90 90 degree. You will see like that’s kind of like the bar chart that we have over time. But people don’t know that. Okay, for.
297 00:33:18.970 ⇒ 00:33:27.049 Annie Yu: like January 25 cohort. We only have 4 months of data now, but for this one we can see like each month.
298 00:33:27.190 ⇒ 00:33:28.970 Annie Yu: if that makes sense at all. But.
299 00:33:28.970 ⇒ 00:33:32.999 Robert Tseng: Oh, I see. Yeah, yeah, no. I agree. This is misleading that like.
300 00:33:33.190 ⇒ 00:33:39.669 Robert Tseng: you’re not really comparing apples to apples here like this one only has, like month 0 for people.
301 00:33:39.670 ⇒ 00:33:40.080 Annie Yu: Yeah.
302 00:33:43.240 ⇒ 00:33:49.018 Robert Tseng: Okay? So yeah, no. If you’re just like this is the better way to do it. Like, I think, I I think that’s totally valid.
303 00:33:50.180 ⇒ 00:33:50.930 Robert Tseng: Okay.
304 00:33:51.390 ⇒ 00:33:58.159 Robert Tseng: cool. Sorry, I know. Wish we didn’t really talk about anything. I know that you were working on some stuff, though. So I mean, I can stay on.
305 00:33:58.350 ⇒ 00:34:08.859 Robert Tseng: I just pushed my meeting back. I have. I have another 10 min. But I guess if you guys, if you guys, if you want to stay on. Stay on, but I wish I do want to kind of chat through some things for you.
306 00:34:19.009 ⇒ 00:34:20.889 Robert Tseng: Okay? So
307 00:34:27.399 ⇒ 00:34:34.949 Robert Tseng: yeah, you want to tell me, like, kind of where you’re at and like what what I know. We kind of had a conversation yesterday, and then
308 00:34:35.069 ⇒ 00:34:45.779 Robert Tseng: I mean, there was a handoff where I gave something over to Sebastian to track. I basically said, we don’t need to, you know, do the tagging and tracking ourselves?
309 00:34:45.969 ⇒ 00:34:47.072 Robert Tseng: Yeah. And it
310 00:34:47.589 ⇒ 00:34:52.339 Robert Tseng: did you, did you watch the same video that I sent or like? Did you read through the doc that I sent him
311 00:34:52.829 ⇒ 00:34:54.239 Robert Tseng: like I guess.
312 00:34:55.219 ⇒ 00:34:56.119 Awaish Kumar: I just wanna
313 00:34:57.060 ⇒ 00:35:14.450 Awaish Kumar: I viewed the video you shared on the loom and also the doc. And I, I agree with what you suggested. But yeah, like they, they are the one who who can easily add the tag because they know the structure of the website and how this
314 00:35:14.840 ⇒ 00:35:38.599 Awaish Kumar: like, what’s happening behind when you are running this questionnaire. So yeah, after they add these actions events. So we capture them. And then I can start working on the modeling. So because right now, I don’t know what’s going to be there, like, what properties, and how this the Json structure will look like, because these properties are going to be in some Json inside Json or something like that. So
315 00:35:38.800 ⇒ 00:35:47.019 Awaish Kumar: without actually being that data in the bigquery, I cannot write a model like.
316 00:35:48.350 ⇒ 00:35:54.530 Robert Tseng: Yeah, so on that note, I decided not to.
317 00:35:54.820 ⇒ 00:36:03.280 Robert Tseng: Yeah. I didn’t end up sending him our account traditionally. What I would have done for a tracking plan. I just sent him like a tech spec kind of Doc, that I put together.
318 00:36:03.570 ⇒ 00:36:16.929 Robert Tseng: Yeah. And this, these are all the fields that I went over with Ryan that he wanted to get out of the data layer. It’s a lot of stuff. But I think, he, you know, we we just that’s what we agreed on.
319 00:36:17.050 ⇒ 00:36:22.720 Robert Tseng: And yeah, I kinda just had some stuff drafted up here. So
320 00:36:23.130 ⇒ 00:36:37.250 Robert Tseng: yeah, I guess this is not kind of not in our hands right now. I’m kind of just waiting for Sebastian to to track it. But yeah, after that it should. It should land in bigquery, and we’ll be able to work with him there. So I think that closes. Yeah, I think we’re on the same page on that. Now.
321 00:36:37.640 ⇒ 00:36:40.459 Robert Tseng: Anything else that was outstanding for you.
322 00:36:41.270 ⇒ 00:36:46.679 Awaish Kumar: No, because I have the marketing data marked, and it is done. It’s in the
323 00:36:47.310 ⇒ 00:36:48.950 Awaish Kumar: production already, so I don’t know
324 00:36:49.320 ⇒ 00:36:54.120 Awaish Kumar: hasn’t had a chance to work on it. But yeah, that’s basically done. And
325 00:36:54.680 ⇒ 00:37:04.250 Awaish Kumar: I saw this regular weekly sync, I just will sync the data for Zendesk from polytomic. Now, Peace, goodbye.
326 00:37:04.250 ⇒ 00:37:04.620 Robert Tseng: Okay.
327 00:37:04.620 ⇒ 00:37:06.240 Awaish Kumar: From that. Yeah, there’s nothing.
328 00:37:07.600 ⇒ 00:37:16.430 Robert Tseng: Okay, yeah, I mean, I could use your help more on the segment side. So I mean, we’re kind of like stuck on the tracking thing, but also just figuring out like the whole
329 00:37:17.646 ⇒ 00:37:19.310 Robert Tseng: but I guess yeah,
330 00:37:24.790 ⇒ 00:37:39.629 Robert Tseng: the web hooks that we have. I guess we’re gonna talk about that with Rob today. Within a lot of so maybe, yeah, you know what? I okay. Anyway, I don’t. We don’t need to decide right now. I’m just like pulling on the fly. So okay, no, I mean, that’s that’s fine. I think that’s that’s clear.
331 00:37:39.800 ⇒ 00:37:53.000 Awaish Kumar: And yeah, also for that deletion task like date delete data for user. So I have, I created the task like the I finished the task already, but like I didn’t like had any
332 00:37:53.420 ⇒ 00:37:59.260 Awaish Kumar: real users, or like something which I can test on that my script right?
333 00:38:00.430 ⇒ 00:38:06.809 Awaish Kumar: But like we didn’t know, like, if there are some test user already inside which I can just test my
334 00:38:09.090 ⇒ 00:38:15.320 Awaish Kumar: script on it because or like in the also, I had a like
335 00:38:15.870 ⇒ 00:38:23.669 Awaish Kumar: in the mix panel. I can create a user if if you can allow, like, I can create a user myself and then delete it.
336 00:38:23.790 ⇒ 00:38:26.559 Awaish Kumar: So at least I test the script is working.
337 00:38:27.930 ⇒ 00:38:34.660 Robert Tseng: Okay? Yeah. I mean, you could just delete my, my, you can just delete meetings fine
338 00:38:35.710 ⇒ 00:38:41.720 Robert Tseng: like my, can. I just give you my like, you didn’t email you just delete me from the system or use that as a test.
339 00:38:42.810 ⇒ 00:38:48.809 Robert Tseng: No, I I mean the users inside the like in the when we open the mix panel. There are a lot of users.
340 00:38:49.370 ⇒ 00:38:54.800 Awaish Kumar: So that data like, I don’t know if you are, you are as a customer, are you in there?
341 00:38:55.590 ⇒ 00:38:56.050 Robert Tseng: I.
342 00:38:56.050 ⇒ 00:39:00.359 Awaish Kumar: You’re using as a mix panel account, right? Not the as a customer. You’re not in.
343 00:39:02.080 ⇒ 00:39:03.229 Robert Tseng: Let’s see.
344 00:39:07.420 ⇒ 00:39:09.210 Robert Tseng: Guess I guess I’m not.
345 00:39:09.920 ⇒ 00:39:14.770 Awaish Kumar: Yep, but we can add like on the top. You can see it says, add profile.
346 00:39:17.190 ⇒ 00:39:17.610 Robert Tseng: Yeah.
347 00:39:17.610 ⇒ 00:39:20.039 Awaish Kumar: You can add user from here and then delete it.
348 00:39:20.600 ⇒ 00:39:21.270 Robert Tseng: Okay.
349 00:39:28.580 ⇒ 00:39:30.940 Robert Tseng: Alright. Well, that will that do?
350 00:39:31.950 ⇒ 00:39:41.430 Awaish Kumar: Yeah, I that will test the mix panel. But yeah, we had customer, dot I/O, and the that was well, so that’s why I was. I was asking like, if there is anything.
351 00:39:41.880 ⇒ 00:39:48.319 Awaish Kumar: If there are any users in there, I can test it otherwise we can leave it right now. If whenever we get a request from it in
352 00:39:48.700 ⇒ 00:39:51.650 Awaish Kumar: team we can just test them and then
353 00:39:52.000 ⇒ 00:39:55.340 Awaish Kumar: start our script as on a schedule.
354 00:39:55.830 ⇒ 00:39:56.420 Robert Tseng: Okay.
355 00:39:57.080 ⇒ 00:40:10.309 Robert Tseng: yeah. So I guess the wish, like, I haven’t flushed out. I was. These are both on me. But I’ll just kind of give you heads up like. So I wanted to customer. I’ve been working with Bobby’s the lifecycle guy, like I’ve been.
356 00:40:10.650 ⇒ 00:40:21.990 Robert Tseng: Yes, I looked into all the different segments he does. I’m trying to make a decision on, like what data he should, what cohorts he should continue to build in
357 00:40:22.250 ⇒ 00:40:27.920 Robert Tseng: in customer, I own, which ones you should let us build in the warehouse.
358 00:40:28.050 ⇒ 00:40:53.850 Robert Tseng: I feel like, maybe that’s you know, that’s a decision I should loop you into. You know. I think we’re we’re having this tension right now. We’re like, you know. Rob is like circumventing this team, setting some stuff up directly in customer I/O, and then. But then everything that we do uses the data warehouses like the control center. And so, like, you know, even running to some issues right now, because, like, there’s a new
359 00:40:54.490 ⇒ 00:41:04.670 Robert Tseng: thing that he had set well, given a lot, and I just discovered something that he has set up with directly with customer. I/O, that didn’t actually go through the warehouse. So
360 00:41:04.990 ⇒ 00:41:14.840 Robert Tseng: anyway, like just to have, we need a data, we need a data team to have more control on like how to activate customer segment data in
361 00:41:14.980 ⇒ 00:41:20.989 Robert Tseng: like customer cohort data in in the lifecycle tool, which is customer. I/O, right? So
362 00:41:21.950 ⇒ 00:41:34.330 Robert Tseng: I guess I don’t know if you have too much experience with that. But like, that’s the whole purpose of like doing this segment kind of like re retooling situation like we might move off segment like that’s
363 00:41:34.920 ⇒ 00:41:42.279 Robert Tseng: I have like a month of lead time to figure this out. But like, I guess if it’s just on me like I’m moving pretty slow on it. So
364 00:41:43.049 ⇒ 00:41:58.959 Robert Tseng: maybe we could talk a bit more clearly to see what questions you have and like what we can do to get you more involved there. But like, yeah, I don’t want to be the only one driving. This this this investigation, because I think it’s it’s too slow this by me myself.
365 00:42:00.730 ⇒ 00:42:06.299 Awaish Kumar: Okay, I I can be part of that like I. But I need to understand, like the whole thing, how
366 00:42:06.610 ⇒ 00:42:09.770 Awaish Kumar: different people work these different tools. And then.
367 00:42:09.940 ⇒ 00:42:13.630 Awaish Kumar: like in what orders and things like that. Maybe we
368 00:42:13.870 ⇒ 00:42:17.170 Awaish Kumar: like after we have. I have the context. I can like
369 00:42:17.830 ⇒ 00:42:21.819 Awaish Kumar: have more questions, and and maybe some some feedback.
370 00:42:22.150 ⇒ 00:42:39.040 Robert Tseng: Yeah, okay, so I’ll I’ll try to. I mean, I want to close these 2 out today. These are both kind of on me to finish out. So, as I’m doing that, I’ll probably record a loom and send it to you, or we’ll we’ll do a meeting. I’ll try to do a loom, because it’s a bit more concise that way and then we can get on a call afterwards.
371 00:42:40.550 ⇒ 00:42:46.689 Robert Tseng: Okay, cool. Sorry. I know that this. There’s a lot moving lot of moving pieces this week like.
372 00:42:47.100 ⇒ 00:42:48.213 Awaish Kumar: Yeah, it’s.
373 00:42:48.900 ⇒ 00:42:58.380 Robert Tseng: Yeah. But I think overall we’re we’re doing. We’re doing well. This is, this is just like a crunch time for them. They launched a bunch of new products and want to figure some stuff out. So
374 00:42:59.340 ⇒ 00:43:01.899 Robert Tseng: yeah, thanks everyone. Yeah.
375 00:43:01.900 ⇒ 00:43:12.649 Annie Yu: One question is about tomorrow. Stand up. I don’t know if Hannah reach out to you, but I got assigned to host. Tomorrow’s Friday meeting.
376 00:43:12.650 ⇒ 00:43:18.359 Robert Tseng: Oh, yeah, I was gonna reschedule this. Yeah. So I will reschedule this stand up. Yeah.
377 00:43:18.810 ⇒ 00:43:19.800 Annie Yu: Nice, alright!
378 00:43:19.800 ⇒ 00:43:20.470 Robert Tseng: Okay.
379 00:43:20.910 ⇒ 00:43:22.199 Robert Tseng: Alright. Thank you.
380 00:43:23.110 ⇒ 00:43:23.700 Annie Yu: Bye.
381 00:43:23.910 ⇒ 00:43:24.580 Robert Tseng: Bye.