Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-05-07 Meeting participants: Annie Yu, Demilade Agboola, Robert Tseng, Awaish Kumar
WEBVTT
1 00:00:48.270 ⇒ 00:00:49.520 Annie Yu: Hello!
2 00:00:50.720 ⇒ 00:00:51.690 Robert Tseng: Hey! Annie!
3 00:00:56.790 ⇒ 00:00:59.630 Annie Yu: Robert, you’re using a math book, too. Right?
4 00:01:00.250 ⇒ 00:01:00.920 Robert Tseng: I am.
5 00:01:01.280 ⇒ 00:01:07.869 Annie Yu: Does your device like shut down, or like Restart, all of a sudden.
6 00:01:08.880 ⇒ 00:01:11.716 Robert Tseng: Yeah, if you don’t update it for like a month.
7 00:01:12.000 ⇒ 00:01:14.620 Annie Yu: I I do update it, I think
8 00:01:15.100 ⇒ 00:01:21.730 Annie Yu: for some reason, I, my laptop, just keeps shutting down and restarts itself. And
9 00:01:22.500 ⇒ 00:01:32.630 Annie Yu: yeah, couldn’t figure out. Why, like I, I copied and pasted the kind of the report thing to like, chat, gpt, and say, it’s like kernel panic.
10 00:01:33.460 ⇒ 00:01:34.390 Robert Tseng: What?
11 00:01:34.890 ⇒ 00:01:35.410 Annie Yu: Yeah.
12 00:01:35.410 ⇒ 00:01:36.090 Robert Tseng: Is, your.
13 00:01:36.090 ⇒ 00:01:37.080 Annie Yu: That means.
14 00:01:38.648 ⇒ 00:01:41.719 Robert Tseng: I’m assuming you’re using a personal computer.
15 00:01:41.720 ⇒ 00:01:42.290 Annie Yu: Yeah.
16 00:01:42.290 ⇒ 00:01:47.190 Robert Tseng: And and you’re using a macbook, I guess, is it?
17 00:01:47.690 ⇒ 00:01:58.179 Robert Tseng: I mean, I mean, are you doing a lot of analysis stuff? So maybe, like your computers. Not really, if you don’t have. I don’t know that much about computers, but maybe your your compute power is not very high.
18 00:01:58.560 ⇒ 00:02:06.590 Annie Yu: Hmm, yeah. Say, like, Dcp, I don’t even know what that is like. Display Coprocessor.
19 00:02:08.180 ⇒ 00:02:15.950 Annie Yu: I did bring it back to the store a while ago, and they did run all the like diagnostic.
20 00:02:16.210 ⇒ 00:02:22.620 Annie Yu: and doesn’t seem like it’s a like a hardware issue. So.
21 00:02:22.970 ⇒ 00:02:24.190 Robert Tseng: Interesting.
22 00:02:25.170 ⇒ 00:02:35.839 Robert Tseng: Well, yeah, I I stopped working on my macbook. I actually got like, an Imac, or like.
23 00:02:35.840 ⇒ 00:02:36.300 Annie Yu: Hmm.
24 00:02:36.300 ⇒ 00:02:38.600 Robert Tseng: What do they call it? Like a Mac home, or something?
25 00:02:38.600 ⇒ 00:02:39.370 Annie Yu: Yeah.
26 00:02:39.990 ⇒ 00:02:43.559 Robert Tseng: It’s actually pretty good. It’s like 500 bucks or whatever, and like.
27 00:02:43.560 ⇒ 00:02:43.940 Annie Yu: I just.
28 00:02:43.940 ⇒ 00:02:53.979 Robert Tseng: Plug it up to a a, a screen? Yeah. I mean, if that’s something that you know we we can. We can talk about like getting you an upgrade if you’re having.
29 00:02:54.394 ⇒ 00:03:06.815 Annie Yu: I think this this happened. This started happen happening. But even before I joined Brandforge, but I just feel like it. Ha! It’s happening more and more so it’s annoying.
30 00:03:07.970 ⇒ 00:03:09.040 Robert Tseng: Interesting.
31 00:03:10.990 ⇒ 00:03:19.730 Robert Tseng: Yeah, I I’m sorry I don’t. I don’t know. I guess I’m lazy, and I I also ran into like random restarts on my macbook, and then I was like
32 00:03:20.320 ⇒ 00:03:28.700 Robert Tseng: I asked you, Tom, what he was doing using, and he got this Mac put home thing, or whatever, and I I just I just upgraded so.
33 00:03:28.940 ⇒ 00:03:34.069 Annie Yu: Nice. Nice. Yeah, it sounds like it’s cheaper, too.
34 00:03:35.010 ⇒ 00:03:45.009 Robert Tseng: Yeah, I mean, it’s it’s, you know, it’s just the computer. And you have to get your own monitor own camera and all the other accessories, but works works for me, for for an At home setup.
35 00:03:45.190 ⇒ 00:03:47.240 Annie Yu: Nice got it?
36 00:03:50.240 ⇒ 00:03:58.003 Robert Tseng: Everyone sorry about the the link. I we’ll update it later.
37 00:04:03.840 ⇒ 00:04:11.919 Robert Tseng: alright, I’m just gonna jump into it because Josh wants to join today, and I’m pretty sure he’ll derail it with like just random things that he wants to talk about, so
38 00:04:13.070 ⇒ 00:04:15.890 Robert Tseng: might as well get into it before he joins.
39 00:04:20.630 ⇒ 00:04:25.350 Robert Tseng: Okay, let’s see.
40 00:04:32.540 ⇒ 00:04:41.250 Robert Tseng: right? I know I’m probably missing some messages that you’ll tag me into. But we’ll just kind of run through this. And yeah, just make sure that we’re
41 00:04:41.350 ⇒ 00:04:42.840 Robert Tseng: missing things along.
42 00:04:43.060 ⇒ 00:04:46.349 Robert Tseng: Everything pending client feedback. I won’t mention there.
43 00:04:47.018 ⇒ 00:04:52.530 Robert Tseng: Order journey review. I did review this already. I think it looks fine. I guess
44 00:04:56.260 ⇒ 00:04:59.559 Robert Tseng: I guess this is actually not relevant to this team. This is more for
45 00:04:59.710 ⇒ 00:05:04.180 Robert Tseng: Rob. But I guess basically what this is showing is like.
46 00:05:04.707 ⇒ 00:05:21.569 Robert Tseng: maybe it’s relevant to the farm Ops dashboard kind of like, what’s the order for the of the order events? I don’t think we’re really showing like a full funnel right now. So maybe not that important. I guess this is something Annie probably should be aware of.
47 00:05:23.920 ⇒ 00:05:35.518 Robert Tseng: yeah, from our dashboard is very limited to just right. From order completed to order delivered. I think there’s more conversation now of like wanting to do a full, patient lifecycle.
48 00:05:36.430 ⇒ 00:05:43.330 Robert Tseng: you know, set of reports and analysis. So I think we’re gonna have to iterate on this like this is helpful for
49 00:05:43.800 ⇒ 00:05:45.570 Robert Tseng: just the operations team.
50 00:05:47.160 ⇒ 00:05:48.890 Robert Tseng: But yeah, just
51 00:05:49.100 ⇒ 00:05:55.930 Robert Tseng: that’s something that I’ve been looking to add to our our roadmap where this become necessary to implement
52 00:05:58.090 ⇒ 00:06:15.290 Robert Tseng: yeah. Event data model was just on the call previously, I think think the guys got it. Answer some questions around how the event data redesign is. Gonna answer. Give him the level of granularity. He needs to do funnel reporting on all intake data.
53 00:06:15.954 ⇒ 00:06:22.285 Robert Tseng: Yeah, I just gotta figure out how the events are being tracked.
54 00:06:23.610 ⇒ 00:06:27.869 Robert Tseng: I may. I may end up handing this one off. I think.
55 00:06:29.100 ⇒ 00:06:35.780 Robert Tseng: Yeah, between Dave a lotta and a wish. I know neither of you have used segment or Google tag manager before, but
56 00:06:36.150 ⇒ 00:06:42.109 Robert Tseng: still seems like a way she work with G 4 data more. Would you be able to help with this.
57 00:06:46.120 ⇒ 00:06:52.629 Awaish Kumar: So like, we have to work like, bring in data work on it. Or like, we have to implement, like
58 00:06:52.790 ⇒ 00:06:54.829 Awaish Kumar: the Google Tag manager as well.
59 00:06:55.844 ⇒ 00:07:03.404 Robert Tseng: So there is already Google tag manager there. So it’s kind of just like going through their Google tag manager and segment figuring out.
60 00:07:04.500 ⇒ 00:07:16.760 Robert Tseng: so like the. This is how I’ve redesigned their event data model. And I want this to be implemented. It seems like the person who owns their web flow and bask. We kind of like their
61 00:07:17.438 ⇒ 00:07:41.819 Robert Tseng: their site engineer. Whatever has doesn’t actually do any of the tracking, it looks like everything is tracked in Google tag manager and segment alone. So I think there’s kind of a we need to figure out what’s being tracked in Google tag manager versus segment. How much of what I’ve put here is being tracked correctly in this way. And what do we need to change?
62 00:07:42.040 ⇒ 00:07:57.000 Robert Tseng: I’ve already kind of looked at both platforms and did my 1st pass. I shut off events that we didn’t need stuff like page scroll, or whatever like, you know, that was 30% of the events that were being fired. And I, you know, shut all those off.
63 00:07:57.508 ⇒ 00:08:02.889 Robert Tseng: But I don’t think that the events are necessarily like in this format yet. So
64 00:08:03.434 ⇒ 00:08:09.100 Robert Tseng: I think that’s there’s kind of like a tracking implementation piece to this that I need help with.
65 00:08:12.230 ⇒ 00:08:14.090 Awaish Kumar: Okay, I can work on it.
66 00:08:14.590 ⇒ 00:08:23.369 Robert Tseng: Okay, so I will. You know, I’ll just do a quick review. Segments.
67 00:08:25.230 ⇒ 00:08:30.499 Robert Tseng: Yeah, for how events are being tracked.
68 00:08:33.150 ⇒ 00:08:35.009 Robert Tseng: So this is like a spike.
69 00:08:40.530 ⇒ 00:08:43.770 Robert Tseng: Just gonna hand that to wish on
70 00:08:54.010 ⇒ 00:09:00.740 Robert Tseng: and then align tracking our events.
71 00:09:01.790 ⇒ 00:09:07.600 Robert Tseng: backing to new event data design.
72 00:09:11.720 ⇒ 00:09:12.780 Robert Tseng: It’s department.
73 00:09:13.560 ⇒ 00:09:15.320 Robert Tseng: Send that torish
74 00:09:20.280 ⇒ 00:09:23.269 Awaish Kumar: So the that figma sheet is the new event design, like.
75 00:09:23.990 ⇒ 00:09:24.880 Robert Tseng: Correct.
76 00:09:25.400 ⇒ 00:09:25.830 Awaish Kumar: Okay.
77 00:09:25.830 ⇒ 00:09:29.000 Robert Tseng: And I can. Oh, yeah, well, yeah.
78 00:09:31.356 ⇒ 00:09:35.190 Robert Tseng: I will make sure that I share the same.
79 00:09:35.870 ⇒ 00:09:37.909 Robert Tseng: You know what. I’m just gonna go and do this. Now,
80 00:09:56.540 ⇒ 00:09:57.980 Robert Tseng: a ticket. Where’d it go?
81 00:09:58.510 ⇒ 00:09:59.456 Robert Tseng: Okay.
82 00:10:07.950 ⇒ 00:10:09.940 Robert Tseng: yeah. So there’s a loom here.
83 00:10:10.820 ⇒ 00:10:18.250 Robert Tseng: and also the design. If you need it. Translated into Google sheet format. I can. But otherwise.
84 00:10:18.510 ⇒ 00:10:23.710 Robert Tseng: you know, I I don’t feel like you need it. Anyway, I’ll just let you kind of run with that?
85 00:10:25.820 ⇒ 00:10:26.620 Robert Tseng: Yeah.
86 00:10:27.710 ⇒ 00:10:29.878 Robert Tseng: Okay, so that’s that.
87 00:10:32.680 ⇒ 00:10:34.179 Robert Tseng: I think this is good.
88 00:10:36.860 ⇒ 00:10:39.860 Robert Tseng: Okay, marketing dashboard. So
89 00:10:40.030 ⇒ 00:10:46.279 Robert Tseng: I saw some model. I saw some Prs that were pushed. Yeah, I don’t know. Maybe let’s talk about this.
90 00:10:47.836 ⇒ 00:10:53.030 Awaish Kumar: So yeah, like the for the, I have pushed a model which has the spend
91 00:10:53.300 ⇒ 00:11:00.469 Awaish Kumar: online and offline both in a in the same model it’s called a channel spend summary
92 00:11:01.089 ⇒ 00:11:10.730 Awaish Kumar: but now the second thing is that we want the revenue data as well, because some of the charts require revenue by channel
93 00:11:11.294 ⇒ 00:11:16.820 Awaish Kumar: as well. And like this can help with some of the charge.
94 00:11:16.950 ⇒ 00:11:21.990 Awaish Kumar: And but the other part I’ve I’ve been looking at, and I’ve shared the
95 00:11:22.410 ⇒ 00:11:29.650 Awaish Kumar: excel like a like Google sheets link in in the slack channel where I’m trying to map like Utm
96 00:11:29.910 ⇒ 00:11:35.549 Awaish Kumar: sources to the names which I find in the marketing
97 00:11:35.810 ⇒ 00:11:43.719 Awaish Kumar: data like this spend data. And I have tried to map some. If, like, you agree, I can put it in.
98 00:11:44.928 ⇒ 00:11:46.452 Awaish Kumar: Yeah, this sheet
99 00:11:47.250 ⇒ 00:11:52.920 Awaish Kumar: And then we then I can like, we’ll be able to
100 00:11:53.170 ⇒ 00:12:01.340 Awaish Kumar: categorize these, the orders based on channel. And then we can basically try to join it on the channel
101 00:12:01.450 ⇒ 00:12:04.009 Awaish Kumar: at a channel level as we do for
102 00:12:04.734 ⇒ 00:12:10.199 Awaish Kumar: product level. Yeah, like production summary, we can have channel sales summary something like that.
103 00:12:10.840 ⇒ 00:12:19.080 Robert Tseng: Okay, let me just give a quick shout out to.
104 00:12:52.370 ⇒ 00:12:55.030 Robert Tseng: Okay, I that’s.
105 00:13:12.990 ⇒ 00:13:18.979 Annie Yu: So for this new marketing model, just to double check it will have
106 00:13:19.240 ⇒ 00:13:26.359 Annie Yu: revenue and spend by channel. Is that correct? And that also include non-paid channel revenue.
107 00:13:26.650 ⇒ 00:13:28.940 Annie Yu: That’s the the goal. Is that correct?
108 00:13:33.280 ⇒ 00:13:34.080 Robert Tseng: Yes.
109 00:13:36.270 ⇒ 00:13:47.170 Robert Tseng: So we already had channel or revenue by channel across all Utm sources.
110 00:13:48.870 ⇒ 00:13:57.249 Robert Tseng: you know, Utms are just like tags that are fired based on the traffic that’s coming in. But you know not all of them were formatted in the correct
111 00:13:57.350 ⇒ 00:14:17.980 Robert Tseng: way. You know. GG. Google, what the heck is this, you know, stuff like that? That I guess oasis normalizing to make the revenue reporting clear. We did not have spend data before, which is why we needed the new model. That wish joined in, I guess. So, yeah, we’re kind of having.
112 00:14:18.150 ⇒ 00:14:26.720 Robert Tseng: Now. We now we should have both assuming, I think we should just execute on this. But yeah, like, that’s that’s what happened the past
113 00:14:27.080 ⇒ 00:14:28.430 Robert Tseng: couple days. I guess.
114 00:14:28.820 ⇒ 00:14:29.360 Annie Yu: Okay.
115 00:14:29.682 ⇒ 00:14:43.549 Awaish Kumar: Yeah. So like that right now, the that model which I’ve shared is is basically has only the spend but I will put a like. I will like add a new model which is going to combine both of them, anyway. Spend.
116 00:14:44.080 ⇒ 00:14:44.933 Annie Yu: Nice nice.
117 00:14:46.260 ⇒ 00:14:52.190 Robert Tseng: Okay, great. And then any, I know you had something here, anything you wanted to just start out. Yeah.
118 00:14:52.190 ⇒ 00:15:17.950 Annie Yu: Yeah. So regarding the Ltv. The reason that it was all the same, we filter on the product is because now in this table we calculate Ltv. At product level across all dates. And I did find that there’s another model sales data joined monthly Ltv. Cohorts. I think it does have all the fields that we need, but we might need like a weekly version of that.
119 00:15:18.450 ⇒ 00:15:20.770 Annie Yu: because we’re showing week by week.
120 00:15:20.870 ⇒ 00:15:24.960 Annie Yu: But unless there’s an existing table that I’ve missed.
121 00:15:26.700 ⇒ 00:15:31.759 Robert Tseng: Yeah. So let’s talk about that. So Ltv
122 00:15:31.960 ⇒ 00:15:40.750 Robert Tseng: shouldn’t really change at a weekly level. It should really change at monthly level. I mean, maybe you’re you’re cohorting it by week. But
123 00:15:41.437 ⇒ 00:15:45.419 Robert Tseng: right, you’re just trying to track like historical
124 00:15:46.071 ⇒ 00:15:57.730 Robert Tseng: purchases by group and basically seeing like, Hey, did a group from, you know, May 7th of 2024 as that group of like. How much did
125 00:15:59.530 ⇒ 00:16:05.516 Robert Tseng: somewhat, how much revenue did we get from them up to this point right? And
126 00:16:06.110 ⇒ 00:16:25.380 Robert Tseng: you know that that could inform like projection like a benchmark for this week. You know, a year later, what we expect, you know we expect to do better than last year, or at least, you know, keep have have parity. It? Yeah. So I I mean, I think you know people are not buying that much
127 00:16:25.500 ⇒ 00:16:40.160 Robert Tseng: right? It’s, you know, at most they’re making one purchase a month, one purchase a quarter, one purchase every 6 months, one purchase every 12 months, so I don’t really expect to see like change a week to week. So I think monthly is fine.
128 00:16:41.490 ⇒ 00:16:49.910 Annie Yu: So. And with that, should I just use that sales data, join monthly, which already has Otb and and CAD.
129 00:16:51.080 ⇒ 00:16:59.179 Robert Tseng: Yeah, I mean, do you know why it was shown weekly like, is it just because the rest of the charts in that view were shown weekly.
130 00:16:59.180 ⇒ 00:17:00.170 Annie Yu: Yeah, yeah.
131 00:17:00.170 ⇒ 00:17:00.810 Robert Tseng: Yeah.
132 00:17:01.150 ⇒ 00:17:02.620 Robert Tseng: So I see that.
133 00:17:02.620 ⇒ 00:17:03.310 Annie Yu: Got it
134 00:17:03.964 ⇒ 00:17:12.030 Annie Yu: into like a monthly basis. And just that charge right for the others. We still keep on weekly level.
135 00:17:12.290 ⇒ 00:17:31.110 Robert Tseng: Yeah, I think I’m just trying to think that that would be confusing. I mean, it sounds fine, like I just, I don’t think anybody looks at Ltv. We on a weekly basis at least not yet. So we’re not running like weekly promotions, and all that stuff like like a normal E-com company would like. I don’t think there’s that many pricing
136 00:17:31.320 ⇒ 00:17:43.749 Robert Tseng: exercises that are happening yet. Eventually we get to a point where we’re launching, you know. See like weekly discounts. And like, you know, if that camp, maybe they’ve run a lot more campaigns that could end up
137 00:17:44.180 ⇒ 00:17:49.529 Robert Tseng: being justification for doing a weekly view, but for now I think we should just do monthly.
138 00:17:50.040 ⇒ 00:17:50.770 Annie Yu: Okay.
139 00:17:50.930 ⇒ 00:17:59.510 Annie Yu: yeah. And and okay. And I can do that. But just a caveat that only that chart will be connected to connected to that model.
140 00:17:59.750 ⇒ 00:18:04.269 Robert Tseng: Correct? Would that impact the global filters? Then.
141 00:18:04.690 ⇒ 00:18:09.870 Annie Yu: Probably I I can give it like a separate set of filters, but.
142 00:18:10.178 ⇒ 00:18:17.949 Robert Tseng: Wonder if this is like getting a little bit too confusing then I mean, I I guess
143 00:18:18.810 ⇒ 00:18:23.729 Robert Tseng: accuracy wise it’s it just looks weird, right? Like it’s just, but like the the
144 00:18:23.960 ⇒ 00:18:28.799 Robert Tseng: whether you look at it weekly or monthly, like the number should still be about the same.
145 00:18:31.666 ⇒ 00:18:47.430 Annie Yu: Do you mean that product sales? So if we stick to product sales summary by transaction, that would always be the same. But if we do sales data join monthly or TV cohorts, I think it does change month by month.
146 00:18:47.920 ⇒ 00:18:51.260 Robert Tseng: I see. Okay? Then, yeah, we should change it. Let’s just change it.
147 00:18:51.260 ⇒ 00:18:51.890 Annie Yu: Okay.
148 00:18:52.140 ⇒ 00:18:56.120 Robert Tseng: Yeah, okay.
149 00:18:56.560 ⇒ 00:19:01.109 Annie Yu: And then revenue growth rates. Yesterday we had like a a spike
150 00:19:01.340 ⇒ 00:19:14.939 Annie Yu: in the 1st column, and that was because we filter on month, and then that 1st week was only partial. So when we do the comparison, that growth rate was was inflated. So I now just
151 00:19:15.443 ⇒ 00:19:21.439 Annie Yu: the 1st week, if it doesn’t have full week of data, and only for this chart. So
152 00:19:21.610 ⇒ 00:19:25.830 Annie Yu: we can have the more comprehensive comparison.
153 00:19:26.160 ⇒ 00:19:30.390 Annie Yu: And I also like switch that into like a
154 00:19:31.520 ⇒ 00:19:35.479 Annie Yu: bar, so we can see more easily.
155 00:19:36.200 ⇒ 00:19:42.290 Robert Tseng: I agree. This this makes sense. The visual makes sense more. I guess my question is just like, well, what happened here.
156 00:19:44.900 ⇒ 00:19:49.210 Annie Yu: 4, 58 k. To 15.
157 00:19:49.700 ⇒ 00:19:54.369 Robert Tseng: Yeah. August, this is just like a big dip, like, I mean, I don’t know. Maybe that’s fine.
158 00:19:54.850 ⇒ 00:20:10.420 Annie Yu: Yeah, it looks like it. And I tried so this this was on. This example was on, I think, only one product. But I tried all other products. I think they all have like very similar pattern here, so I’m not sure if there was something off there or.
159 00:20:10.420 ⇒ 00:20:17.070 Demilade Agboola: 2 week drop, I know, like josh mentioned that like revenue tends to drop every 2 weeks, or something like that.
160 00:20:19.400 ⇒ 00:20:25.790 Robert Tseng: Yeah, that’s true. People buy. Yeah. Josh mentioned that people buy twice a month.
161 00:20:25.890 ⇒ 00:20:28.099 Robert Tseng: But that is kind of weird that like.
162 00:20:30.120 ⇒ 00:20:38.656 Robert Tseng: okay, that’s fine. We’ll just leave it, for now, I think. As long as it’s not like we missed a bunch of orders here, and we didn’t like realize we had to backfill it.
163 00:20:40.770 ⇒ 00:20:43.920 Robert Tseng: I mean, this might have to be a separate analysis. I mean, I’ll
164 00:20:45.060 ⇒ 00:20:50.849 Robert Tseng: it’s okay. I’ll bring it up to the team. And yeah, I need to be kind of just bothering the
165 00:20:51.220 ⇒ 00:20:55.097 Robert Tseng: the stakeholders and asking them to be looking at these reports.
166 00:20:56.260 ⇒ 00:20:59.949 Robert Tseng: anyway. Okay, that’s that’s fine. I think the visual makes sense
167 00:21:00.270 ⇒ 00:21:04.000 Robert Tseng: in progress. Mar, model, or yep. Yep. We already talked to that.
168 00:21:04.000 ⇒ 00:21:04.560 Annie Yu: Yep.
169 00:21:06.000 ⇒ 00:21:09.910 Robert Tseng: Cool. Let’s get going. Yeah. So this one looks good. We’re on track here.
170 00:21:10.620 ⇒ 00:21:13.620 Robert Tseng: Yeah, we’re not using north theme,
171 00:21:15.610 ⇒ 00:21:22.700 Robert Tseng: workspace current attributes. Yeah. Have a call with seg segment later today. Good. There, yeah, I guess
172 00:21:23.310 ⇒ 00:21:24.820 Robert Tseng: I know them. A lot of you were
173 00:21:25.220 ⇒ 00:21:30.730 Robert Tseng: caught up with urban stabs yesterday. But, I don’t know any updates on on that.
174 00:21:30.930 ⇒ 00:21:36.510 Demilade Agboola: To be honest, I have no real updates on my projects. Like. From yesterday.
175 00:21:36.830 ⇒ 00:21:38.000 Robert Tseng: Fair. Okay.
176 00:21:38.625 ⇒ 00:21:42.539 Robert Tseng: Yeah. Let me know how I can support there. Yeah.
177 00:21:45.440 ⇒ 00:21:52.130 Robert Tseng: we’ll kind of keep going for mop stuff I’m assuming. You haven’t touched this yet, Annie.
178 00:21:52.962 ⇒ 00:21:54.859 Annie Yu: So that one
179 00:21:55.230 ⇒ 00:22:06.299 Annie Yu: we do have 2 parts that we wanted to add right? So the patient outcome ongoing. And then I did look through the prescription workflow. I think right now, our
180 00:22:07.240 ⇒ 00:22:10.262 Annie Yu: problem is just that we don’t have that
181 00:22:13.520 ⇒ 00:22:21.219 Annie Yu: The the things happening from when order placed to send to pharmacy.
182 00:22:23.070 ⇒ 00:22:26.559 Robert Tseng: We don’t have that. Oh, you mean from, yeah.
183 00:22:26.560 ⇒ 00:22:29.000 Annie Yu: Pending, and all that.
184 00:22:29.990 ⇒ 00:22:30.770 Robert Tseng: Okay.
185 00:22:40.250 ⇒ 00:22:43.910 Robert Tseng: yeah, I’ll write a message to.
186 00:22:55.250 ⇒ 00:22:59.400 Robert Tseng: I’m pretty sure it’s coming in from the lovebook. So just gotta figure out where it is.
187 00:23:00.600 ⇒ 00:23:10.329 Annie Yu: Okay, okay, and let me know if you want me to look into any models or so, I just don’t know where I can help
188 00:23:10.440 ⇒ 00:23:12.179 Annie Yu: to to get those things.
189 00:23:12.830 ⇒ 00:23:20.050 Robert Tseng: Okay, yeah, you know what I mean. I’m gonna like, tag you. And like, or you know, I’m just gonna I don’t think you’re in here so
190 00:23:21.910 ⇒ 00:23:25.519 Robert Tseng: so stuff like that. I would. I would just go to Rob
191 00:23:25.650 ⇒ 00:23:30.609 Robert Tseng: first, st if you can, next time. But you know, obviously I’m only looking at.
192 00:23:31.264 ⇒ 00:23:31.810 Robert Tseng: I’m
193 00:23:32.320 ⇒ 00:23:42.734 Robert Tseng: at best. I I look at ticket updates twice a day here. Otherwise I’m only reviewing it once a day. So I want you to be blocked by me here.
194 00:23:43.260 ⇒ 00:23:48.550 Robert Tseng: yeah. So now you have this access to the channel, and then I’ll ask Rob to.
195 00:24:25.880 ⇒ 00:24:30.870 Robert Tseng: Okay, cool.
196 00:24:31.270 ⇒ 00:24:33.779 Robert Tseng: No, we’re coming up on here. So
197 00:24:34.790 ⇒ 00:24:44.489 Robert Tseng: yeah, you’re already working on the ad spend. This is pretty much done or testing, I guess. Pr review, possibly. And then a lot of is still doing the product data model stuff.
198 00:24:45.000 ⇒ 00:24:49.339 Robert Tseng: Yeah, I haven’t translated into tickets yet. But let me with the remaining time.
199 00:24:49.732 ⇒ 00:24:55.279 Robert Tseng: so anything else that you guys wanted to bring up, or I can talk about some, some other things that are coming.
200 00:24:59.300 ⇒ 00:25:00.660 Robert Tseng: Okay, sounds like not.
201 00:25:00.660 ⇒ 00:25:01.170 Awaish Kumar: Okay.
202 00:25:01.170 ⇒ 00:25:02.490 Robert Tseng: Yes, I put oh, go ahead!
203 00:25:02.490 ⇒ 00:25:10.400 Awaish Kumar: Can you just share between the the Sigma, the figma link you? You showed like about the events.
204 00:25:10.720 ⇒ 00:25:19.389 Robert Tseng: Yeah, sorry. It’s not actually a figma. It’s actually whimsical. This is before I started working with your Tom. And he put me into figma.
205 00:25:19.510 ⇒ 00:25:22.789 Robert Tseng: But yeah, I’ll just act through real quick.
206 00:25:25.820 ⇒ 00:25:26.260 Robert Tseng: No.
207 00:25:26.700 ⇒ 00:25:28.339 Annie Yu: What’s that called, then.
208 00:25:29.070 ⇒ 00:25:29.700 Robert Tseng: This.
209 00:25:30.060 ⇒ 00:25:30.800 Annie Yu: Yeah.
210 00:25:30.800 ⇒ 00:25:40.629 Robert Tseng: This is called whimsical. It’s basically free figma. So yeah, it’s not. It’s not a great product. Figma is better. But you know I was. I’m not always the
211 00:25:40.770 ⇒ 00:25:43.719 Robert Tseng: the fastest to adopt new tools. Let’s just put it out.
212 00:25:46.210 ⇒ 00:26:12.420 Robert Tseng: yeah. So a couple of things coming, I think, like the main thing I’ll talk about is this idea where I’m working with the head of finance. Now we’re trying to talk about full, patient lifecycle reporting. So you know, up to this point, we’ve done order level stuff in the, you know, the farm Ops is really at the order level. We have a dim orders table. We kind of have a dim, patient table. We haven’t really built out that model. So yeah, I think now, there’s a lot more
213 00:26:12.780 ⇒ 00:26:20.270 Robert Tseng: interest in like doing patient level analysis. And so I’m excited because I feel like this will kind of get us into more interesting stuff.
214 00:26:20.664 ⇒ 00:26:41.039 Robert Tseng: So I mean, I’m just kind of writing up like kind of some specs here, and just trying to create a mental model for how we should approach this. But the idea of like what I think this will entail one we’re gonna have to define like what an active patient is and build out that model. So whether it’s like a Den patients model or not, like I don’t know. Well, tbd, I think
215 00:26:41.330 ⇒ 00:26:50.559 Robert Tseng: so. Just thinking through like, what are those milestones? We wanna keep track of. Obviously, it’s like plan type. You know, quantity of like, whatever drug that they’re buying
216 00:26:51.107 ⇒ 00:26:53.600 Robert Tseng: and then, like some other interesting things like that.
217 00:26:54.440 ⇒ 00:27:02.939 Robert Tseng: And yeah, I think the goal is really just to help the finance, like the new head of like Cfo, whatever to figure out like
218 00:27:03.600 ⇒ 00:27:04.429 Robert Tseng: like.
219 00:27:06.680 ⇒ 00:27:23.570 Robert Tseng: you know, these are the sample insights. These are not actually true. But anyway, so I I’ll I’m I’m kind of filling out this, doc like I have a few other things like that I want to think of, like strategically, of how we can impact the business. I’m gonna you know, share this with Josh leadership soon. And
220 00:27:24.490 ⇒ 00:27:31.389 Robert Tseng: yeah, hopefully, we’ll start to Orient kind of like our work around these initiatives rather than just like, you know, building.
221 00:27:31.570 ⇒ 00:27:47.340 Robert Tseng: we’re not. I don’t want to measure us by number of dashboards, reports built. But you know, we’re actually impacting these initiatives. So yeah, I think this is kind of work in progress. Takes me some time to put it together but wanted to at least kind of share with you guys like
222 00:27:47.500 ⇒ 00:27:54.300 Robert Tseng: like what strategic work I’ve been doing the past day to kind of get us ready for the next next batch of work.
223 00:27:59.500 ⇒ 00:28:06.339 Robert Tseng: Cool? Yeah. I don’t expect any questions yet. Eventually, I’m gonna share this out hopefully. End of day. I’ll send this to the team.
224 00:28:06.520 ⇒ 00:28:09.250 Robert Tseng: you know, would want you to kind of just review it kind of
225 00:28:09.480 ⇒ 00:28:15.129 Robert Tseng: think about what’s coming, especially for the more the more senior folks who are thinking about like what
226 00:28:15.520 ⇒ 00:28:22.190 Robert Tseng: you know, what things we need to do to get there? You know. We’ll we’ll we’ll talk. We’ll talk more on this soon.
227 00:28:23.000 ⇒ 00:28:32.429 Annie Yu: Okay? And one question. So when you say patient lifecycle, does that mean it actually means like a
228 00:28:33.090 ⇒ 00:28:36.739 Annie Yu: order journey, or it’s a different thing.
229 00:28:36.740 ⇒ 00:28:56.019 Robert Tseng: So a different level of abstraction. So order, journey, I mean. So probably some overlap, I think. Order journey. We kind of already have that minus the doctor prescription stuff that you just mentioned. But we have everything from order completed all the way to order delivered right? That’s like sent to pharmacy, you know.
230 00:28:56.730 ⇒ 00:29:03.750 Robert Tseng: processed, or whatever you know, shipped out of pharmacy and then delivered so patient is like from
231 00:29:04.130 ⇒ 00:29:32.410 Robert Tseng: the moment, like from the checkout flow. So we have some limitations around the transactions there. But yeah, I think it’s just a different orientation of like not doing order, level profitability or product level profitability. But we want to like view from the patient perspective. Yeah. So it’s we do kind of have to define like a different set of milestones, I guess. For for the patient journey there’s probably some overlap. But yeah.
232 00:29:32.810 ⇒ 00:29:33.600 Annie Yu: Okay.
233 00:29:36.930 ⇒ 00:29:53.960 Robert Tseng: Cool. So yeah, it’ll be stuff more like initial plan that they came in on refills. If they’re on an annual plan. Are they actually picking up their full dosage amount, or are they dropping off after 2 months, you know, stuff stuff like that? I think that’s those are all like patient centric questions.
234 00:29:54.660 ⇒ 00:29:56.090 Annie Yu: Okay. Yeah.
235 00:29:56.090 ⇒ 00:29:56.670 Robert Tseng: Yeah.
236 00:29:58.030 ⇒ 00:30:05.009 Robert Tseng: Okay. Yeah, we will talk more soon. Thank you. And yeah. Okay. Talking slack.
237 00:30:05.530 ⇒ 00:30:06.390 Annie Yu: Thank you.
238 00:30:07.046 ⇒ 00:30:07.839 Awaish Kumar: Thank you.