Meeting Title: [Eden] Daily Standup Date: 2025-04-28 Meeting participants: Annie Yu, Demilade Agboola, Robert Tseng, Rob, Awaish Kumar
WEBVTT
1 00:03:48.690 ⇒ 00:03:49.659 Demilade Agboola: Power of it.
2 00:03:51.500 ⇒ 00:03:52.419 Robert Tseng: They don’t want it.
3 00:03:58.550 ⇒ 00:04:00.930 Robert Tseng: You in Minnesota now.
4 00:04:01.210 ⇒ 00:04:06.690 Demilade Agboola: Yes, I’m in Minnesota right now. Was a bit of a hectic flight, but you know we’re here now.
5 00:04:07.460 ⇒ 00:04:08.120 Robert Tseng: Nice.
6 00:04:11.890 ⇒ 00:04:14.500 Robert Tseng: Where? Where are you in Minneapolis, or.
7 00:04:15.086 ⇒ 00:04:20.910 Demilade Agboola: Saint Paul. But like, yeah, Minneapolis is literally like a 20 min drive from from where I’m at.
8 00:04:21.430 ⇒ 00:04:22.079 Robert Tseng: Nice.
9 00:04:22.350 ⇒ 00:04:22.870 Demilade Agboola: Okay.
10 00:04:24.680 ⇒ 00:04:26.180 Demilade Agboola: Where do you stay in? New York?
11 00:04:27.783 ⇒ 00:04:31.740 Robert Tseng: I’m in Columbus Circle.
12 00:04:33.250 ⇒ 00:04:38.850 Robert Tseng: yeah, I don’t know if you’re familiar with the streets, but it’s like 59th and 7.th
13 00:04:40.390 ⇒ 00:04:43.649 Demilade Agboola: So I’m not familiar with the streets. I’m more familiar with like boroughs.
14 00:04:45.010 ⇒ 00:04:49.440 Robert Tseng: Boroughs. Oh, yeah. Well, it’s in Manhattan. It’s like it’s next to Central Park.
15 00:04:49.650 ⇒ 00:04:53.089 Demilade Agboola: Gotcha gotcha. Oh, I have been to Central Park. I went to Central Park like last year.
16 00:04:53.760 ⇒ 00:04:57.279 Robert Tseng: Yeah, yeah. I’m in like the southwest corner of Central Park.
17 00:04:59.270 ⇒ 00:05:00.190 Demilade Agboola: That’s pretty cool.
18 00:05:00.790 ⇒ 00:05:07.260 Robert Tseng: Yeah, no, it’s it’s been a great area. We just moved here last month. So
19 00:05:07.660 ⇒ 00:05:14.239 Robert Tseng: I yeah, I mean, I’m getting ready to sell my car. Everything’s very walkable. Yeah.
20 00:05:14.480 ⇒ 00:05:17.923 Demilade Agboola: And then, you know, far from, you know, far from like the
21 00:05:18.630 ⇒ 00:05:21.720 Demilade Agboola: subway, like subways, are really close to that in that area.
22 00:05:22.180 ⇒ 00:05:31.319 Robert Tseng: Oh, yeah, yeah. The a there’s I mean, most most lines kind of run through this part of town. So it’s it’s it’s been easy to kind of get anywhere.
23 00:05:34.920 ⇒ 00:05:36.199 Demilade Agboola: Hi! Rob! Hi! I wish.
24 00:05:36.820 ⇒ 00:05:37.980 rob: And guys.
25 00:05:37.980 ⇒ 00:05:38.720 Awaish Kumar: Hello!
26 00:05:42.280 ⇒ 00:05:44.635 Robert Tseng: Hello, Hello! Okay.
27 00:05:47.470 ⇒ 00:05:58.450 Robert Tseng: I’ll open this up. We can jump into it. Yeah, Rob, I think, only thing that I had outstanding was things still waiting on you for influencer stuff.
28 00:05:59.126 ⇒ 00:06:02.990 Robert Tseng: But yeah, I don’t really know. Kind of yeah, I feel like.
29 00:06:02.990 ⇒ 00:06:06.053 rob: Ask me to store it in a different place. So
30 00:06:06.360 ⇒ 00:06:06.910 Robert Tseng: Okay.
31 00:06:07.080 ⇒ 00:06:12.356 rob: Yeah, I’ll I’ll get it to you, I guess. With that change happening at the end of the month with
32 00:06:13.100 ⇒ 00:06:16.670 Robert Tseng: Drip. Yeah. So she’s not gonna be in that Google sheet right?
33 00:06:16.670 ⇒ 00:06:21.890 rob: Yeah, cause we’re relying on one that they were. They managed. So
34 00:06:22.090 ⇒ 00:06:29.609 rob: yeah, I mean, they told me that. And then I had to pivot to get something for that new Vp of finance. So yeah.
35 00:06:29.610 ⇒ 00:06:31.660 rob: okay, haven’t done it yet. But I’ll let you know.
36 00:06:31.660 ⇒ 00:06:39.239 Robert Tseng: Yeah, I’m talking to the Vp finance today. I just what are, what is he having you do versus like I want to.
37 00:06:39.240 ⇒ 00:06:58.189 rob: Well, he wants just a bunch of one off reports. So I did tell him to go to you for anything that you know he wants to be able to run like ongoing in tableau. He just said he wanted a list of customers right now. With the historical purchases. But yeah, it’s a 1 off.
38 00:06:58.910 ⇒ 00:07:07.370 Robert Tseng: Okay, cool. Good to know. Yeah. And then, like, yesterday, Maladi mentioned, we had like a issue with Customer Sec and Customer I/O and segment.
39 00:07:07.490 ⇒ 00:07:09.049 Robert Tseng: I guess.
40 00:07:09.680 ⇒ 00:07:11.970 Robert Tseng: Do you? Can you? Can you look into that, Rob? Or do you.
41 00:07:11.970 ⇒ 00:07:15.140 rob: Yeah, I hadn’t heard about that. Yeah. Well.
42 00:07:15.490 ⇒ 00:07:27.440 Demilade Agboola: Yeah, so it it does appear like, it’s deactivated. So I was just curious if it was an intentional thing. And also if it’s set up for the embeddables as well. Like the embeddables tracking.
43 00:07:27.950 ⇒ 00:07:35.730 rob: I’ll look. I’m a little bit nervous, because I I think I know who was messing with that segment stuff. I I don’t think that was on purpose.
44 00:07:35.980 ⇒ 00:07:37.289 Demilade Agboola: Okay. Alright. Sounds good.
45 00:07:37.690 ⇒ 00:07:45.369 rob: Thanks for finding that, I think. Adam Po Oma was working in there on Friday.
46 00:07:46.000 ⇒ 00:07:46.920 Robert Tseng: Okay.
47 00:07:47.450 ⇒ 00:07:54.469 Robert Tseng: I mean, I literally was in the segment yesterday. So like I didn’t really notice anything. But I didn’t look at the customer I/O side. So.
48 00:07:57.328 ⇒ 00:08:01.209 Robert Tseng: okay, so we need to figure out the embeddables like.
49 00:08:01.390 ⇒ 00:08:22.979 Robert Tseng: yeah, well, I guess we can. We can get into that. But I think Embeddables is the launches this week, so gotta make sure that we’re ready to to get that data in. I know we were kind of like unsure how to get the test data didn’t look didn’t look right or something. So anything that I can do to help push on that like I can go click into stuff.
50 00:08:25.650 ⇒ 00:08:26.570 Demilade Agboola: Sounds good.
51 00:08:28.247 ⇒ 00:08:34.249 Robert Tseng: Okay, let me just kind of zoom out. I guess, Rob, you can stay off your if not, I think that’s all the main things we wanted to get.
52 00:08:34.250 ⇒ 00:08:37.050 rob: Yeah, I’ll jump off. Yeah, I got it. Okay.
53 00:08:37.210 ⇒ 00:08:39.440 rob: do something. So thanks, guys, see, ya.
54 00:08:39.440 ⇒ 00:08:40.490 Robert Tseng: Yeah, see? Ya.
55 00:08:42.177 ⇒ 00:08:54.680 Robert Tseng: yeah. So I guess a couple of changes for this team broadly. So one we have winded down Sahana for now I think we’re just gonna try to run it with the team that we have here.
56 00:08:55.435 ⇒ 00:08:56.979 Robert Tseng: I’m a bit
57 00:08:57.260 ⇒ 00:09:19.025 Robert Tseng: anxious about it, to be honest, so if it doesn’t go well, then, I’ll probably bring her back next week. But for now she’s just sitting on the bench is kind of the way we’ve put it so. I think for Annie that probably impacts you the most. There’s something that someone has been working on that handing off to you. It’s this thing that’s just been stuck for so long. So
58 00:09:19.650 ⇒ 00:09:25.339 Robert Tseng: yeah, I think I’ll I’ll probably find time with you to just make sure we know how to do the handoff there.
59 00:09:25.856 ⇒ 00:09:32.033 Robert Tseng: Yeah, I think, anyway. So we’ll we’ll I can spend more time with you on that.
60 00:09:32.690 ⇒ 00:09:34.510 Robert Tseng: and then otherwise.
61 00:09:35.170 ⇒ 00:09:43.579 Robert Tseng: yeah, the priorities still look pretty much the same as last week. Unfortunately, we didn’t close out these 2 with the marketing stuff.
62 00:09:43.780 ⇒ 00:09:54.049 Robert Tseng: Dashboard. Got paused. The marketing team has just kind of been in chaos. They like lost their performance marketing agency, or whatever. So I think that’s just just sitting there until we’re ready there.
63 00:09:54.648 ⇒ 00:10:06.220 Robert Tseng: and then on the pharmacy kind of agent performance dashboard. Maybe there’s a bit of consolidation that needs to happen here. I’m gonna just kind of
64 00:10:06.560 ⇒ 00:10:17.960 Robert Tseng: they are. They did start off as 2 separate dashboards. But really, this is, we’re just talking about one at this point. So I’ll like figure out how to rearrange this. But
65 00:10:18.090 ⇒ 00:10:19.790 Robert Tseng: so actually think about it.
66 00:10:20.342 ⇒ 00:10:33.529 Robert Tseng: The only one really iterating on is this customer journey? One, I think, this publish dashboards. Zendesk.
67 00:10:35.690 ⇒ 00:10:37.780 Robert Tseng: Yeah. So
68 00:10:39.040 ⇒ 00:10:46.049 Robert Tseng: so this is the agent performance Stash. It didn’t look like there was anything else we needed to do here. So actually, we’ll just close.
69 00:10:46.860 ⇒ 00:10:49.709 Robert Tseng: I will close this out. I think this is fine.
70 00:10:50.500 ⇒ 00:10:52.320 Robert Tseng: So I’ve waited.
71 00:10:57.000 ⇒ 00:11:07.110 Robert Tseng: yeah. So the order Sla performance, which is really just this customer journey dashboard. So I might actually rename this right now. And just kind of make it a bit more consistent.
72 00:11:10.240 ⇒ 00:11:12.410 Robert Tseng: Order customer.
73 00:11:20.370 ⇒ 00:11:21.230 Robert Tseng: Okay.
74 00:11:23.060 ⇒ 00:11:38.840 Robert Tseng: yeah. So that’s really what this project is. Elt business reporting there isn’t like a net new thing, I think. Annie already kind of made some changes. We added, the pacing look like they like that. We haven’t added a forecast or anything yet. I’m talking to the
75 00:11:39.020 ⇒ 00:11:52.840 Robert Tseng: Vp. Of finance today. He’s gonna be working on some forecasting and seeing how we could bring that into the same reporting. But I don’t really see that happening for some time. So I’m gonna deprioritize this for the week.
76 00:11:53.542 ⇒ 00:12:16.060 Robert Tseng: The new product launch. Thank you, Annie, for for shipping that I mean look great. I left some feedback and looks like the slacks been popping people have been clicking into it so I guess I guess we’ll just wait on the feedback there. But otherwise I feel like, if anything. It’s just, you know, a few touch ups here and there to get that over the finish line.
77 00:12:17.920 ⇒ 00:12:25.551 Robert Tseng: Yeah, I would say. Embeddables is probably highest priority this week marketing data. Mart as well.
78 00:12:27.140 ⇒ 00:12:39.829 Robert Tseng: I think, for the wish the the I know corral hasn’t sent you new model with, like the data to model yet, but we’re expecting them to early this week, so we can hopefully get unblocked there
79 00:12:40.450 ⇒ 00:12:47.199 Robert Tseng: and then, as far as, like strategic finance and planning. I don’t think there’s actually anything in here yet. I think I just put that there
80 00:12:47.749 ⇒ 00:12:58.060 Robert Tseng: tableau sales purchase history data. Yeah, okay, cool. So, yeah, this is just with the Vp finance stuff. So there’s no rush there.
81 00:13:00.140 ⇒ 00:13:04.593 Robert Tseng: But yeah, so I think this is kind of the order priority.
82 00:13:06.290 ⇒ 00:13:11.379 Robert Tseng: yeah, now, we can kind of drill into, I guess, before I go into the specific issues.
83 00:13:11.580 ⇒ 00:13:16.970 Robert Tseng: does do kind of like the high level project priorities kind of like make sense to everyone.
84 00:13:20.220 ⇒ 00:13:21.390 Demilade Agboola: Yes, makes sense to me.
85 00:13:21.390 ⇒ 00:13:22.640 Awaish Kumar: Yeah, it. Looks like.
86 00:13:23.170 ⇒ 00:13:35.680 Robert Tseng: Okay, cool. So hopefully, this is helpful for me to at least calibrate at the beginning of the week what I see coming. So okay, so let’s drill into this. I know there’s some stuff as pending feedback. So I guess I’ll kind of
87 00:13:36.850 ⇒ 00:13:42.169 Robert Tseng: so well, yeah, we’ll we’ll start here.
88 00:13:42.660 ⇒ 00:13:51.347 Robert Tseng: So there’s a few things. I, yeah, just to kind of share out with the team. What? I kind of put together.
89 00:13:53.440 ⇒ 00:13:56.800 Robert Tseng: so machines.
90 00:13:57.120 ⇒ 00:14:05.840 Robert Tseng: Yeah, so there’s just like, kind of even notion, page that we put together. We used to have this. I just kind of cleaned it up. And then, I guess
91 00:14:06.160 ⇒ 00:14:20.980 Robert Tseng: it seems like and kind of kind of put together the stated platform documentation. So I think this is nearly at a good place to to send to them. I think I just have to go and fill in some of these details. Not a big deal.
92 00:14:21.371 ⇒ 00:14:45.530 Robert Tseng: But yeah, I think this is kind of what I was. We were hoping to get ready for them all the live metrics, how they’re calculated, the data sources. And then, from like a data tooling perspective, like, what are all the active tools they have? And then, yeah, this is just for me to have, like a more strategic conversation with with the leadership team on like where we’re headed, you know, 3 months from now.
93 00:14:46.010 ⇒ 00:14:49.669 Robert Tseng: So some things I was thinking about. So
94 00:14:49.770 ⇒ 00:14:56.121 Robert Tseng: I think I know that we rely on segment right now. But if we look at worse, the the situation with segments.
95 00:14:59.350 ⇒ 00:15:01.469 Robert Tseng: yeah, I think they’re
96 00:15:02.150 ⇒ 00:15:21.010 Robert Tseng: we? I’ve already kind of helped them update like kind of do do some negotiations on the tool. But I actually think that I would like us to replace segment. In a couple of months. So this isn’t gonna translate to any project yet. But I just wanted to share that with the team.
97 00:15:21.409 ⇒ 00:15:32.260 Robert Tseng: My perspective is that we only use segment for custom web hooks with bask right now. So you can see like if you look at, we have web flow and bask data only
98 00:15:32.694 ⇒ 00:15:46.729 Robert Tseng: they’re kind of building the erp right now. That’s gonna help them migrate off of bask. I think, by the end of Q 2, supposedly. So. Once, that happens like, we don’t really need segment anymore for data connectors.
99 00:15:47.382 ⇒ 00:16:01.360 Robert Tseng: You all know that we already use polytomic or 5 chat for other situations. So yeah, as a connector tool segment is very expensive, you know. They charge you 35,000 a year. And they lock into an annual contract.
100 00:16:01.878 ⇒ 00:16:27.969 Robert Tseng: But anyway, I think a couple of other functionality in segment that we currently use. One is computed traits. So this is like, kind of how you get customer level segmentation all these properties it’s just using, like, just taking. You know, some of these data points from the data layer similar to a Google Tag manager. What would have done? I think we use this somewhat in some of our customer journey reporting.
101 00:16:30.890 ⇒ 00:16:32.390 Robert Tseng: I
102 00:16:33.590 ⇒ 00:16:43.800 Robert Tseng: yeah, I think there were some tickets that we had about like wanting. Oh, yeah, for, like the product, drill down dashboard. Right. This is how we have gender. This is how we have anything at the at the customer level.
103 00:16:44.713 ⇒ 00:16:55.720 Robert Tseng: We don’t have location or anything yet, because it wasn’t instrumented here, but you know, we don’t really need segment to build this out. We could kind of just kind of
104 00:16:56.490 ⇒ 00:17:09.210 Robert Tseng: make this a warehouse native like, give customers. Kind of like table that’s using like sequel to to extract the data that we need. So I think this is like
105 00:17:09.329 ⇒ 00:17:26.520 Robert Tseng: I I it’s not an easy project, I’m not saying, but I think this is like one capability we would need to be able to replace, and then the other one is like identity resolution. So based on different data sources, how customers are kind of identified, you know, in some sources they give you a user. Id, somebody give you an email, whatever
106 00:17:26.796 ⇒ 00:17:49.709 Robert Tseng: they’re just doing some stitching. So we can do this stitching ourselves in the data warehouse as well. So I think I’m kind of evaluating, like the cost of the bill versus buy. And I need to talk to to Josh about that like whether or not he wants our team to spend the time to to rebuild this functionality. But if we do, you know, I think it’s not super. It doesn’t seem like it’s a super hard problem to solve.
107 00:17:50.028 ⇒ 00:18:09.639 Robert Tseng: At least I don’t know. I’ll or I mean, I actually, I do know, I have seen this built in custom ways before, like but maybe for Dame a lot, in a way, since this would probably be end up like kind of work on your plate. I’m curious. Have you guys worked on something like this before? Like, yeah, I mean, any kind of
108 00:18:09.880 ⇒ 00:18:13.859 Robert Tseng: any perspective on kind of on on this right now.
109 00:18:16.742 ⇒ 00:18:22.177 Demilade Agboola: Yes, I have worked on something like this where you’re trying to like stitch together the customer information.
110 00:18:22.480 ⇒ 00:18:22.930 Robert Tseng: Yup!
111 00:18:22.930 ⇒ 00:18:29.590 Demilade Agboola: It’s definitely doable as long as the data does exist from the sources. And like, we’re actually getting that coming in.
112 00:18:29.930 ⇒ 00:18:32.830 Demilade Agboola: But yeah, it’s it’s definitely doable.
113 00:18:33.560 ⇒ 00:18:35.840 Robert Tseng: Okay, cool
114 00:18:36.562 ⇒ 00:18:40.969 Robert Tseng: and then I wish I don’t know. Maybe. Yeah. You have any any other thought you wanted to share.
115 00:18:42.600 ⇒ 00:18:43.860 Awaish Kumar: No, like, I
116 00:18:45.650 ⇒ 00:18:53.150 Awaish Kumar: haven’t worked on on something like this. But yeah, like, obviously, we can get, I’ve worked on G, 4 data or
117 00:18:53.950 ⇒ 00:18:57.269 Awaish Kumar: so like, I think it’s possible to do that. But yeah.
118 00:18:57.820 ⇒ 00:19:05.471 Robert Tseng: Yeah, yeah, if you’ve done J, 4 data modeling before, that’s kind of similar to what this this piece is with the computer traits.
119 00:19:05.950 ⇒ 00:19:15.230 Robert Tseng: So okay, cool. I think this is something to keep in mind, I think. I’m I’m having that conversation with them. And then on the mixed panel side. I haven’t added it to our roadmap.
120 00:19:15.820 ⇒ 00:19:17.369 Robert Tseng: I know that none of you have
121 00:19:17.790 ⇒ 00:19:21.030 Robert Tseng: really work with mixed panel and like kind of event
122 00:19:22.290 ⇒ 00:19:25.553 Robert Tseng: data and a product, analytics fashion. So
123 00:19:26.980 ⇒ 00:19:30.890 Robert Tseng: I don’t. I’m not gonna spend too much time on this. I I think this is just like another
124 00:19:31.568 ⇒ 00:19:43.280 Robert Tseng: conversation we’re having with the team about because Josh wanted to kill this tool. But I actually think that it’s it’s worth keeping around. So anyway, I think that’s kind of at a high level where
125 00:19:43.990 ⇒ 00:19:48.979 Robert Tseng: I like. How I reviewed some of the product analytics work here.
126 00:19:51.490 ⇒ 00:20:04.560 Robert Tseng: okay, so that’s kind of the updates for me other stuff that is blocked for you guys. I know this one’s been sitting here for a while and Dave, a lot of we’ve kind of talked back and forth about like.
127 00:20:06.396 ⇒ 00:20:12.199 Robert Tseng: yeah, like, can we actually come up with any heuristics about like what happens to
128 00:20:12.640 ⇒ 00:20:21.686 Robert Tseng: like Mo, you know multiple payment intents like, how do they actually, at what point do they actually turn to orders? Did we come to a conclusion here?
129 00:20:27.458 ⇒ 00:20:29.450 Demilade Agboola: Oh, okay. So I
130 00:20:30.070 ⇒ 00:20:36.511 Demilade Agboola: I still need to finish up the investigation. But like, I’m drilling down right now into like setting criteria. So like, if it’s successful.
131 00:20:37.734 ⇒ 00:20:52.080 Demilade Agboola: so like successful without, if they created dates, successful without any failure or cancellation, and then like trying to see if those ones also. Then don’t appear in our orders. Table
132 00:20:52.603 ⇒ 00:20:56.010 Demilade Agboola: so that would allow me to drill down and kind of see what criteria
133 00:20:56.350 ⇒ 00:21:04.800 Demilade Agboola: the different things. Different missing values appearing also, I’m drilling down on the count of
134 00:21:05.070 ⇒ 00:21:10.130 Demilade Agboola: missing transaction ids for our orders, but only looking at the
135 00:21:10.740 ⇒ 00:21:15.380 Demilade Agboola: orders that are that are not in a canceled or error state.
136 00:21:16.080 ⇒ 00:21:18.100 Demilade Agboola: should I also add, abandoned as well.
137 00:21:19.300 ⇒ 00:21:19.990 Robert Tseng: Yeah.
138 00:21:19.990 ⇒ 00:21:36.329 Demilade Agboola: Okay, so I’ll look at that to not know, abandon, or canceled, and not in an error states. And just see the breakdown of how many orders that is, percentage of the orders that are without transaction ids, because ultimately, if we don’t have transaction ids on those orders, we can’t then tie them back to any transactions.
139 00:21:40.150 ⇒ 00:21:49.369 Robert Tseng: Okay. So I think what I’m hearing is this is really what we’re working on here. Do you still need me to? Manually trace 10 to 20 sessions with multiple.
140 00:21:49.802 ⇒ 00:21:56.569 Robert Tseng: Hey, man, I I feel like I don’t. I feel like we already tested this before. So I feel like this is redundant. But.
141 00:21:58.088 ⇒ 00:22:12.749 Demilade Agboola: Potentially not I. I think. I’ll just. I’ll I should be able to like I I have the capacity to work on this today. So I should be today and just be like, okay, here. These are the, these are the disparities I’m I’m noticing.
142 00:22:13.070 ⇒ 00:22:20.999 Demilade Agboola: And these are like my, these are like, my, this is what my hypothesis is on like. Why we have this disparity.
143 00:22:21.670 ⇒ 00:22:30.529 Demilade Agboola: and potentially, we can then just decide if this is how it’s always just going to be, or if it’s something we can push to the Basques team and just be like, Hey.
144 00:22:30.710 ⇒ 00:22:33.690 Demilade Agboola: is this something that can be fixed. Or, you know.
145 00:22:33.900 ⇒ 00:22:38.800 Demilade Agboola: like we can talk to Josh about it and just be like, Hey, this is our limitations based on the data we have.
146 00:22:39.100 ⇒ 00:22:43.149 Demilade Agboola: and if it’s something that you’re fine with cool, if not, we, we keep it pushing.
147 00:22:46.170 ⇒ 00:22:52.349 Robert Tseng: Okay, cool. So I’m gonna just, you know, hopefully, we can close this one out. Yeah.
148 00:22:53.700 ⇒ 00:23:00.909 Robert Tseng: cool. So that’s that. Yeah. I mean, this is somewhat similar.
149 00:23:03.490 ⇒ 00:23:07.439 Robert Tseng: Documenting how it went to. Okay, the same same thing. Stop issue. Okay?
150 00:23:10.650 ⇒ 00:23:16.189 Robert Tseng: Yeah. So I think you’re still blocked here. So basically, rob just mentioned.
151 00:23:17.735 ⇒ 00:23:22.880 Robert Tseng: yeah, like, influencer marketing or affiliates data
152 00:23:23.180 ⇒ 00:23:28.320 Robert Tseng: needs to be moved from the current Google sheet to another.
153 00:23:28.800 ⇒ 00:23:36.980 Robert Tseng: That’s probably gonna be another spreadsheet that isn’t maintained by drip Bill blocked.
154 00:23:37.990 ⇒ 00:23:38.950 Robert Tseng: Okay,
155 00:23:42.850 ⇒ 00:23:53.569 Robert Tseng: yeah. So there isn’t really anything else in progress. Currently. So we can start to add some stuff back in, I believe. Oh, I just kidding. I was using the wrong view. I was wondering why it was so. Light.
156 00:23:54.220 ⇒ 00:23:59.000 Robert Tseng: Okay.
157 00:24:01.020 ⇒ 00:24:08.740 Robert Tseng: I guess, Annie, since I we haven’t talked too much about. Is there anything that’s on your plate that you want to talk through.
158 00:24:11.118 ⇒ 00:24:13.209 Annie Yu: I would say. It’s mainly that
159 00:24:13.380 ⇒ 00:24:20.440 Annie Yu: customer journey. I haven’t looked at it, so I will have to just familiarize myself with it.
160 00:24:21.380 ⇒ 00:24:24.099 Robert Tseng: Okay, I’ll kind of just do, yeah, go ahead.
161 00:24:24.100 ⇒ 00:24:30.629 Demilade Agboola: Notice that. So Hannah also dropped like some comments on some things I I should do about like consolating things on the back end.
162 00:24:32.260 ⇒ 00:24:33.090 Robert Tseng: Yeah.
163 00:24:33.850 ⇒ 00:24:42.940 Demilade Agboola: I don’t know. Should I? Should I work on that? I mean, you did mention some of them like precision, like we have multiple precision, and, like both main pharmacies, just like consolidating that
164 00:24:43.260 ⇒ 00:24:43.890 Demilade Agboola: awesome.
165 00:24:43.890 ⇒ 00:24:44.450 Robert Tseng: Yeah.
166 00:24:44.720 ⇒ 00:24:53.840 Demilade Agboola: But then I think I need to just like go through, and if she has any other things to add, or if there anything such things you can deprioritize or prioritize. Just let me know.
167 00:24:54.330 ⇒ 00:25:11.759 Robert Tseng: Okay, yeah, I would say, don’t yeah, we’ll we’ll. I’ll review this with Annie. And now this is Annie’s gonna kind of take this over once. She’s clear on, like what help she needs from you. Then we’ll just kind of batch these requests together to you. So I would say, this isn’t like, yeah, you don’t have to action this right away.
168 00:25:12.430 ⇒ 00:25:13.260 Demilade Agboola: Sounds good.
169 00:25:14.460 ⇒ 00:25:19.130 Robert Tseng: Okay, but yeah, Annie, I think if you just look into this ticket,
170 00:25:19.930 ⇒ 00:25:32.119 Robert Tseng: yeah, you can start from the figma board where it started. I know things kind of like changed a lot. Along along the way I recorded a loom of like my take on the current state, and where it was at
171 00:25:32.625 ⇒ 00:25:42.359 Robert Tseng: and then I would say, if you watch that and kind of just understand the dashboard. I can kind of repeat kind of like some of the feedback here.
172 00:25:42.490 ⇒ 00:25:49.800 Robert Tseng: I I don’t think Sahana fully got it like I think there were. She didn’t really change much. Honestly, after I I sent this video? So
173 00:25:51.220 ⇒ 00:25:52.560 Annie Yu: Yeah, I think.
174 00:25:52.860 ⇒ 00:25:56.622 Robert Tseng: I think this would be the best place for you to just kind of catch up where it is.
175 00:25:56.970 ⇒ 00:25:57.710 Robert Tseng: yeah.
176 00:25:58.130 ⇒ 00:25:59.460 Annie Yu: Okay. Yeah.
177 00:26:00.870 ⇒ 00:26:08.629 Robert Tseng: Cool alright. So next things that we gotta.
178 00:26:09.230 ⇒ 00:26:16.129 Annie Yu: Oh, this to clarify! But we will still get time right. Me and you, Robert.
179 00:26:16.540 ⇒ 00:26:17.740 Robert Tseng: Yes, we will.
180 00:26:20.040 ⇒ 00:26:26.939 Robert Tseng: Yeah, I’ll probably grab time later this afternoon. I’m gonna jump to a couple of other things the next few hours. But yeah.
181 00:26:32.670 ⇒ 00:26:35.950 Robert Tseng: yeah, any more progress on this stimulating.
182 00:26:39.858 ⇒ 00:26:43.290 Demilade Agboola: I have reached out to Christine. Christina.
183 00:26:43.510 ⇒ 00:26:45.129 Demilade Agboola: Okay, not kidding.
184 00:26:45.300 ⇒ 00:26:53.179 Demilade Agboola: and she hasn’t responded. I bumped it again today. This morning. Just. It’s on the channel, the.
185 00:26:53.180 ⇒ 00:26:53.820 Robert Tseng: Okay.
186 00:26:54.380 ⇒ 00:26:56.540 Demilade Agboola: External analytics channel, I believe.
187 00:26:56.930 ⇒ 00:26:57.410 Demilade Agboola: Yes.
188 00:26:57.410 ⇒ 00:26:58.000 Robert Tseng: Yeah.
189 00:26:59.960 ⇒ 00:27:04.960 Demilade Agboola: So it’s right there. I’m just trying to see like what the configuration of each med kit is.
190 00:27:05.170 ⇒ 00:27:10.179 Demilade Agboola: So if it’s also fixed, or cause I can see my kids wants to one to 5,
191 00:27:10.855 ⇒ 00:27:17.110 Demilade Agboola: so just being able to figure out like, what does make it? 1, 2, 3, 4, 5. What do they stand for? What? What makes up a med kit?
192 00:27:17.220 ⇒ 00:27:29.454 Demilade Agboola: And then, once we have the idea of what each bundle is, we can then decide how we want to split each thing into like, once we have met kids, one, how does that spread across the different individual
193 00:27:30.390 ⇒ 00:27:35.180 Demilade Agboola: pieces like Sema, or whatever piece it is.
194 00:27:35.850 ⇒ 00:27:43.250 Robert Tseng: Okay, got it. So that’s clear.
195 00:27:46.000 ⇒ 00:28:03.699 Robert Tseng: Yeah, I feel like we’re at a place now where we’re really just kind of like, these are all small changes of like maintenance, and whatever. So I do want to queue up some more advanced work for us to go after. Seems like we have capacity like, I, I think, like the things that we’re doing that are are all in progress, like either, just
196 00:28:03.910 ⇒ 00:28:15.670 Robert Tseng: things that are just pushing on as much as we can close out, you know, today would be great. I’m gonna be spending more time like thinking, thinking through what those, what the next problems to solve are.
197 00:28:16.385 ⇒ 00:28:18.020 Robert Tseng: yeah, I think
198 00:28:18.380 ⇒ 00:28:30.199 Robert Tseng: a wish. I know we didn’t really talk too much about your involvement right now, I think. Just staying on top of whatever corral is gonna send us, and then being able to build out that marketing data mark for
199 00:28:31.263 ⇒ 00:28:33.380 Robert Tseng: for to be able to
200 00:28:33.720 ⇒ 00:28:36.760 Robert Tseng: for the marketing dashboard is probably the biggest priority.
201 00:28:38.180 ⇒ 00:28:49.679 Robert Tseng: but other than that yeah, I think I it seems like I I just need to pull more things into cycle. So yeah, I I mean, this is kind of all we have right now.
202 00:28:52.050 ⇒ 00:28:52.860 Robert Tseng: yeah.
203 00:28:56.670 ⇒ 00:28:59.700 Robert Tseng: am I missing anything else here?
204 00:29:10.160 ⇒ 00:29:13.750 Robert Tseng: Okay? Yeah. I mean, I I think that’s all I have.
205 00:29:15.160 ⇒ 00:29:27.800 Robert Tseng: yeah, if anyone else has any questions feel free to reach out. Actually, Annie, I don’t know if you have another meeting after this, but I can just spend more time with you and just talk through that that ticket. Now, if that’s more helpful.
206 00:29:29.940 ⇒ 00:29:32.553 Robert Tseng: Okay, give me just a second to check.
207 00:29:37.980 ⇒ 00:29:39.539 Annie Yu: Yeah, I can stay on.
208 00:29:40.100 ⇒ 00:29:46.019 Robert Tseng: Okay, cool, alright? Well, then, I think for the rest of you, I think we’re. I think we’re clear. We’re good.
209 00:29:47.560 ⇒ 00:29:49.379 Demilade Agboola: Okay, sounds good talk to you later.
210 00:29:50.760 ⇒ 00:29:51.380 Robert Tseng: Steal.
211 00:29:55.410 ⇒ 00:29:55.990 Awaish Kumar: Okay.
212 00:29:58.020 ⇒ 00:29:58.890 Robert Tseng: Okay.
213 00:30:06.890 ⇒ 00:30:10.750 Robert Tseng: yeah. Let’s see, do the best way.
214 00:30:11.470 ⇒ 00:30:12.840 Robert Tseng: What about this?
215 00:30:16.460 ⇒ 00:30:27.650 Robert Tseng: Yes, I’ll just kind of talk through the the fixes here. So you’ll kind of get the follow up through the loom. But basically, one is like, Okay, whatever we’re using in
216 00:30:29.640 ⇒ 00:30:31.999 Robert Tseng: the pharmacy filter here.
217 00:30:32.720 ⇒ 00:30:39.562 Robert Tseng: yeah, there’s like some duplicates here. So this is part of life, I think what we needed to ask didn’t want it to.
218 00:30:39.970 ⇒ 00:30:42.370 Robert Tseng: it’s good Consolidate, I guess.
219 00:30:43.242 ⇒ 00:30:52.650 Robert Tseng: And then I think there’s an analysis request here, too. Where? Okay. Sorry. I’ll
220 00:30:53.150 ⇒ 00:31:06.190 Robert Tseng: I’ll take a step back. I’m not gonna necessarily go through the order like the order. This outline mirrors what I say in the video. But I don’t have to say the same thing. Otherwise it’s not like that productive. So really, if I were to just better
221 00:31:06.640 ⇒ 00:31:14.410 Robert Tseng: through the long time I would say we could get rid of this these 2 top tiles. It’s not necessary. It’s kind of confusing, complete.
222 00:31:14.410 ⇒ 00:31:20.019 Annie Yu: Post charts. I mean, yeah, this section doesn’t need to be there. Yeah.
223 00:31:23.430 ⇒ 00:31:41.120 Robert Tseng: yeah. And instead, we need to have some top tiles. The top tiles will be 7 day 30 day metrics or order complete to set the pharmacy and the pharmacy to ship and order completed to delivered so like the main order, kind of miles, like
224 00:31:41.570 ⇒ 00:32:08.200 Robert Tseng: the main slice of the journey that we’re looking at is from when an order is completed to when it’s delivered. There are some naming things that Sahana did here that are not correct. So I kind of call that out in the video as well like these measure names is not how they look at it. It should like sign up doesn’t make any sense like no one talks about signups and order to shift is kind of it’s like it’s not precise enough. We need to like.
225 00:32:08.270 ⇒ 00:32:25.010 Robert Tseng: The full journey is order completed to delivered, and then we need to break it up into the different sections. It’s like order completed to sent to pharmacy, sent to pharmacy, to order, shipped, and then order shipped to order delivered. Those are like those are really the 4 measures that we need to have here.
226 00:32:25.893 ⇒ 00:32:36.799 Robert Tseng: Payment status is fine. There’s like nothing really to say there. But yeah. So once we have that like line like that trend view. Here we also need, like
227 00:32:37.010 ⇒ 00:32:45.159 Robert Tseng: some tiles, kind of like high level, higher level metrics similar to what we do
228 00:32:45.280 ⇒ 00:32:54.769 Robert Tseng: again, when the Asian performance dashboard, or actually like the one that comes to mind. That, I think does a good job is the.
229 00:32:56.960 ⇒ 00:32:59.259 Robert Tseng: And I never know what these things are named.
230 00:33:03.100 ⇒ 00:33:08.169 Robert Tseng: Yeah, like what we have here. Kind of what you built where the for the product drill down right? We have like a.
231 00:33:08.810 ⇒ 00:33:26.550 Robert Tseng: it’s like a 30 like a 30 a 7 day version, like a 7 day, like rolling 7 day snapshot of, or completed to send to pharmacy and then the percentage change from the previous week. And then we have, like, you know, the 7 day metric for all 3
232 00:33:26.710 ⇒ 00:33:28.520 Robert Tseng: or August 4. It’s like.
233 00:33:29.160 ⇒ 00:33:38.522 Robert Tseng: Yeah, I’ve already gone through those stages and then 30 days. So those are like kind of the top level metrics that are missing from the dashboard. It should just replace the section.
234 00:33:39.419 ⇒ 00:33:47.000 Annie Yu: Those bullets under 7 day and 30 day on the left side order completed to send to pharmacy. So those 4 are the
235 00:33:49.570 ⇒ 00:33:52.250 Annie Yu: the ones that we should align
236 00:33:52.760 ⇒ 00:33:55.509 Annie Yu: like how we, I guess. What’s that
237 00:33:55.660 ⇒ 00:34:00.970 Annie Yu: that line chart for for the line chart, how we how to categorize them. Okay.
238 00:34:01.240 ⇒ 00:34:01.840 Robert Tseng: Yeah.
239 00:34:06.260 ⇒ 00:34:10.990 Robert Tseng: Script, receipt, ticket number.
240 00:34:11.699 ⇒ 00:34:14.039 Annie Yu: So that would be 5 stage.
241 00:34:14.829 ⇒ 00:34:15.669 Annie Yu: Is that? Oh.
242 00:34:15.954 ⇒ 00:34:21.350 Robert Tseng: It’s actually just 3 stages. Cold tracking, maybe, is a bit confusing. So we’ll just kind of replace that.
243 00:34:21.520 ⇒ 00:34:29.810 Robert Tseng: So right, this is like the like the full journey. And then this is all the different milestones.
244 00:34:30.719 ⇒ 00:34:33.789 Annie Yu: Okay, okay, I’ll I’ll look into that.
245 00:34:34.270 ⇒ 00:34:42.060 Annie Yu: And when you say 30 day and 7 day, does that mean we want
246 00:34:43.670 ⇒ 00:34:47.699 Annie Yu: like 2 tiles for each of the stage.
247 00:34:48.730 ⇒ 00:35:00.529 Robert Tseng: Yeah, I guess the honest point was that you should just be able to do a filter. So if there’s a way that we could just do it once, and then you can toggle between the 7 day and the 30 day. I think that’s perfectly fine as well.
248 00:35:02.030 ⇒ 00:35:02.900 Robert Tseng: yeah.
249 00:35:38.260 ⇒ 00:35:50.550 Robert Tseng: yeah. And then I think the second section here. I think this chart needs to be better. I kind of called it out in this video. But basically, once again, the measure names need to be consistent. We should be using.
250 00:35:57.600 ⇒ 00:36:18.479 Robert Tseng: I actually think that maybe we should rethink. This is just like sheriff orders that are less than a 3 day. Average pharmacy, turnaround time and a a pharmacy turnaround time. You’ll be able to look at the definition that she had, but I’m pretty sure it’s just like when it was when an order was shipped to pharmacy to when sent to pharmacy to when it’s it’s shipped out.
251 00:36:18.996 ⇒ 00:36:28.200 Robert Tseng: That’s the sla they look at. So I don’t know if you’ve built operational dashboards before, but I would just try to like, maybe yeah, just like, kind of think through.
252 00:36:30.150 ⇒ 00:36:39.989 Robert Tseng: yeah, like, how would how would Amazon present this data? Right? If they’re trying to host like hold their warehouses accountable to an order. Sla like, yeah, they they.
253 00:36:40.430 ⇒ 00:36:48.679 Robert Tseng: you know their their sla. Time is not 3 days. It’s probably, like, you know, under 24 h. But like being able to
254 00:36:49.010 ⇒ 00:36:52.499 Robert Tseng: look at that number. And like.
255 00:36:53.740 ⇒ 00:37:00.099 Robert Tseng: Hey, maybe it is 72% of orders are under 3 day average.
256 00:37:02.460 ⇒ 00:37:03.320 Robert Tseng: But
257 00:37:04.040 ⇒ 00:37:11.199 Robert Tseng: yeah, if you were to actually go and look at. Okay, what are the orders that have exceeded that? That’s what this like next
258 00:37:11.690 ⇒ 00:37:34.509 Robert Tseng: table is for? Right? So I think this can be cleaned up. It doesn’t make sense to have percentage and like hold numbers to have be on the same access as you can tell like it looks kind of confusing. But I think there’s also like a question mark on the data doesn’t really make sense to me like I look at this, for example, March week of March 30.th It’s telling me
259 00:37:34.750 ⇒ 00:37:38.719 Robert Tseng: 12.9% of orders are under 3 days.
260 00:37:39.680 ⇒ 00:37:46.240 Robert Tseng: But the average or pharmacy like time like this. It’s like.
261 00:37:46.760 ⇒ 00:37:52.310 Robert Tseng: how can it be? Only 12.9% met this Sla.
262 00:37:52.620 ⇒ 00:37:53.320 Annie Yu: And.
263 00:37:53.320 ⇒ 00:37:54.010 Robert Tseng: Like
264 00:37:54.380 ⇒ 00:37:59.650 Robert Tseng: the average is 31 h, which is just over a day, right like that, to me, seems kind of
265 00:37:59.880 ⇒ 00:38:00.730 Robert Tseng: weird.
266 00:38:00.940 ⇒ 00:38:06.590 Robert Tseng: So I think there is like some underlying data issue around like
267 00:38:07.680 ⇒ 00:38:11.800 Robert Tseng: this table as well like. If I look at
268 00:38:12.140 ⇒ 00:38:22.860 Robert Tseng: more than 3 days I don’t think this is right. I feel like they shouldn’t be having more than just this. So trying to like, understand
269 00:38:24.750 ⇒ 00:38:27.616 Robert Tseng: where those orders are missing.
270 00:38:29.840 ⇒ 00:38:40.400 Robert Tseng: yeah, I can. I can assist by asking like, How do I actually like? What’s the source of what’s the what’s the source of truth for for orders? That are
271 00:38:42.560 ⇒ 00:38:44.480 Robert Tseng: like I I just
272 00:38:44.960 ⇒ 00:38:52.270 Robert Tseng: I I don’t. I don’t know what the source of truth is to be honest like I think that’s part of the open endedness of the investigation. But all I know is that, like.
273 00:38:52.860 ⇒ 00:39:01.800 Robert Tseng: there’s no way that only 10 orders, or whatever are more than 3 days like, it must be hundreds like, we definitely process more than 10
274 00:39:01.910 ⇒ 00:39:11.650 Robert Tseng: 10 orders. And this is even from the last 3 months. Like I, I just like, I don’t believe that this is, this is true. So I don’t know what
275 00:39:12.310 ⇒ 00:39:23.286 Robert Tseng: like. Maybe it’s about asking the right question or figuring out like, what do we actually need to go look into. But there’s just something about this data that just doesn’t seem right to me.
276 00:39:24.430 ⇒ 00:39:28.389 Annie Yu: Something that will require modeling right.
277 00:39:29.080 ⇒ 00:39:35.999 Robert Tseng: Yeah, i i i yeah, I think there’s part of it is we have to go and
278 00:39:36.160 ⇒ 00:39:46.889 Robert Tseng: figure out, where is the true source? Like, how do I actually validate that hunch that I have like this? This is this must be wrong. And I I don’t know how to validate that. So
279 00:39:47.630 ⇒ 00:40:05.669 Robert Tseng: sometimes just looking at the model, and then like kind of tracing back to the original source tables figuring out like, okay? Well, what conditions were used in the model. You’re kind of like reverse engineering it a bit to to see like where the logic may have
280 00:40:06.340 ⇒ 00:40:12.240 Robert Tseng: broken down. Yeah, I I just
281 00:40:13.150 ⇒ 00:40:19.260 Robert Tseng: I. I’m not familiar with this model like I haven’t personally built any reporting
282 00:40:19.660 ⇒ 00:40:30.469 Robert Tseng: off of it, so I wasn’t able to assist here as much as I was with other reports. I was kind of hoping Sahana would own it, and like really
283 00:40:30.570 ⇒ 00:40:44.730 Robert Tseng: work with the clients work with our engineers like kind of own the quality of this data, but I don’t think she ever did. So I this is kind of like a black box to me, like I don’t really know what’s going on here.
284 00:40:45.000 ⇒ 00:40:46.090 Annie Yu: Okay. Okay.
285 00:40:46.420 ⇒ 00:40:56.799 Robert Tseng: Yeah, I think that’s the main. Yeah, those are the main things. Yeah. So just to summarize
286 00:40:57.010 ⇒ 00:41:05.980 Robert Tseng: work data quality issue of this model. I don’t think I trust it. The presentation of this data doesn’t make sense to me.
287 00:41:08.030 ⇒ 00:41:09.070 Robert Tseng: Yeah, I mean
288 00:41:09.620 ⇒ 00:41:16.699 Robert Tseng: the performance we’re measuring here is that pharmacies are meeting a certain sla, which is under 3 day turnaround time
289 00:41:17.213 ⇒ 00:41:17.920 Robert Tseng: and so.
290 00:41:18.770 ⇒ 00:41:25.629 Annie Yu: From order to complete, to delivered? Is it delivered.
291 00:41:26.527 ⇒ 00:41:31.519 Robert Tseng: No, it’s not even. It’s just specifically just from sent to pharmacy to when it was shipped out.
292 00:41:31.520 ⇒ 00:41:32.410 Annie Yu: Okay.
293 00:41:36.110 ⇒ 00:41:37.610 Robert Tseng: Yeah, I.
294 00:41:40.920 ⇒ 00:41:46.229 Robert Tseng: In a. In a sense, it’s a different view of this order status metrics. But it’s focused
295 00:41:46.340 ⇒ 00:41:49.299 Robert Tseng: not even on the order journey. It’s.
296 00:41:50.250 ⇒ 00:41:51.889 Annie Yu: But like the pharmacy.
297 00:41:52.330 ⇒ 00:41:53.520 Robert Tseng: Yeah, just the pharmacy.
298 00:41:53.970 ⇒ 00:41:54.420 Annie Yu: For.
299 00:41:54.420 ⇒ 00:42:14.500 Robert Tseng: Exactly so. I don’t know if we need to break out the section. Make it a little bit more clear that like up here, it’s order completed to like delivered overall, like there’s like overall order journey. And then this is just the pharmacy journey and sla there like I I think there’s something that we could do to make that clearer.
300 00:42:15.137 ⇒ 00:42:33.860 Robert Tseng: And then, yeah, I like the idea. Here was good to be able to have a table so that you could go and filter like, okay? Well, which orders did not actually meet. Sla. These are the ones that we need to escalate and go take action on. But I just that the data is not trustworthy. So.
301 00:42:34.270 ⇒ 00:42:34.910 Annie Yu: Okay.
302 00:42:37.470 ⇒ 00:42:44.549 Robert Tseng: Yeah. So I know this is like, kind of multifaceted like, you kind of have to redesign it a bit. You kind of have to go, and.
303 00:42:44.960 ⇒ 00:42:56.549 Robert Tseng: you know, unfamiliar yourself with the data and like change, some of the naming and how it’s presented. And then you’re kind of also investigating the the quality here. But yeah, I think it’s just.
304 00:42:56.690 ⇒ 00:43:01.149 Robert Tseng: you know, we started this a month ago, and it’s just been sitting in in these.
305 00:43:01.460 ⇒ 00:43:05.869 Robert Tseng: Actually, it started more than a month ago. It’s kind of embarrassing. Yeah.
306 00:43:07.050 ⇒ 00:43:20.229 Annie Yu: Okay, alright, I’ll do my best, but I will go through the video again. So maybe, do I need to go to the 1st one that you’re sure, or I should just focus on the second one.
307 00:43:21.691 ⇒ 00:43:32.020 Robert Tseng: Yeah, I would just focus on the second one. Yeah, I’m I’m not expecting you to pick it up right away, like I think you can kind of break it down and figure out how you wanna
308 00:43:32.290 ⇒ 00:43:48.850 Robert Tseng: approach this? Hopefully, we can get it resolved by end of week. But yeah, I will. I will help wherever I can, if you, if you need help, like investigating something, and you know, just don’t. Don’t get stuck like I’ll I will! I’ll I’ll be able to jump in.
309 00:43:49.490 ⇒ 00:43:54.710 Annie Yu: Okay, all right, do that.
310 00:43:55.935 ⇒ 00:43:56.620 Annie Yu: Yeah.
311 00:43:56.760 ⇒ 00:44:04.410 Annie Yu: So I think, so far, so good. I, I just need to spend time on this video.
312 00:44:04.530 ⇒ 00:44:09.099 Annie Yu: And then, yeah, I’ll I’ll go from there and ping you if I have questions.
313 00:44:09.980 ⇒ 00:44:17.659 Robert Tseng: Cool. I mean, yeah, I feel like you picked up the product launch stuff fairly quickly, like you. Yeah, I mean, you did in like a week, so like I
314 00:44:17.970 ⇒ 00:44:20.450 Robert Tseng: feel confident that you’ll be able to figure it out.
315 00:44:20.450 ⇒ 00:44:24.831 Annie Yu: I was. I was miserable that day. Just so, you know.
316 00:44:25.230 ⇒ 00:44:26.210 Robert Tseng: Oh, you were!
317 00:44:26.210 ⇒ 00:44:42.130 Annie Yu: No, no, I’m just exaggerating. No, but that one was, I think, the the engineering of that one was like also like I did it. That was my 1st time. Did that like the trailing? So it was cool, but like learning curve, too.
318 00:44:42.570 ⇒ 00:44:47.309 Robert Tseng: Oh, yeah, yeah, I mean, it looks great. So I think this is really great.
319 00:44:48.240 ⇒ 00:44:48.960 Annie Yu: Cool.
320 00:44:50.880 ⇒ 00:44:55.730 Robert Tseng: Okay. Yeah. Alright, I will. I’ll talk to you later.
321 00:44:55.730 ⇒ 00:44:56.900 Annie Yu: Yeah, thank you. Robert.
322 00:44:56.900 ⇒ 00:44:57.970 Robert Tseng: Okay. Bye, Annie.
323 00:44:57.970 ⇒ 00:44:58.520 Annie Yu: Okay.