Meeting Title: [Eden] Daily Standup Date: 2025-04-29 Meeting participants: Annie Yu, Mitesh Patel, Demilade Agboola, Bobby Caruso, Robert Tseng, Josh, Rob, Awaish Kumar
WEBVTT
1 00:02:15.240 ⇒ 00:02:16.399 Mitesh Patel: Hey, Robert.
2 00:02:17.620 ⇒ 00:02:18.490 Robert Tseng: Hey, Mattesh.
3 00:02:19.290 ⇒ 00:02:38.320 Mitesh Patel: Hey? I know I owe you. You’ve asked me same questions couple of them multiple times. I haven’t gotten back to you on it. I have the windows open. It’s just been there’s been so much going on I haven’t had a chance to work on it, and it’s like, you know, I have to review the reports from Carl before I can
4 00:02:38.420 ⇒ 00:02:52.689 Mitesh Patel: answer your just as an example. So I’m behind on those I’m gonna figure out a way to get to it today. Look, if we run out of the trial we run out of the trial. I don’t, or maybe they’ll extend it. I don’t. I don’t know what else to do. I can’t just make.
5 00:02:52.690 ⇒ 00:02:53.810 Robert Tseng: Extend it? Yeah.
6 00:02:53.990 ⇒ 00:02:54.330 Mitesh Patel: You know.
7 00:02:55.320 ⇒ 00:02:56.880 Robert Tseng: Yeah, no worries.
8 00:02:57.680 ⇒ 00:03:11.106 Robert Tseng: yeah. I mean, I think I kind of clarified it over email and a couple of slack message, yeah, it’s just between that slack channel and the marketing leadership analytics channel. Ask a couple of questions there and then. Via email. If I could just get your your
9 00:03:11.540 ⇒ 00:03:14.829 Robert Tseng: you know, point of view on those things, and I think we can. We can keep going.
10 00:03:15.120 ⇒ 00:03:23.059 Mitesh Patel: Okay, alright, I’ll I’ll try to get to it today or tomorrow. Really, just been a lot going on with the new product launches and so on. So.
11 00:03:23.500 ⇒ 00:03:33.939 Robert Tseng: Yeah, yeah, no worries. I I caught up with Josh yesterday, so he told me to, just, you know, just remind you again, but also like, be aware that there’s a lot going on there. So.
12 00:03:33.940 ⇒ 00:03:34.760 Mitesh Patel: Yeah, yeah.
13 00:03:35.630 ⇒ 00:03:42.650 Mitesh Patel: So cool, are you? Are you headed? Are you headed to Detroit, too, with the team? Or
14 00:03:42.650 ⇒ 00:03:53.259 Mitesh Patel: no, I was. I was, gonna I have so much I was gonna go. But and I’m sure I’m gonna join them remotely for some of the discussions. But I just have other stuff to do right now.
15 00:03:53.780 ⇒ 00:03:56.720 Robert Tseng: Yeah, yeah, no, I I get it, I think traveling
16 00:03:56.950 ⇒ 00:04:05.089 Robert Tseng: also disrupts like your your routine and like you’re I mean, at least, I’m more productive in my, in my, in my home office. So.
17 00:04:05.300 ⇒ 00:04:06.400 Mitesh Patel: Yeah, yeah.
18 00:04:06.400 ⇒ 00:04:07.010 Robert Tseng: Yeah.
19 00:04:07.350 ⇒ 00:04:08.739 Mitesh Patel: Hey, Rob! How are you?
20 00:04:09.220 ⇒ 00:04:11.220 rob: Hey? Man, good morning, guys.
21 00:04:15.900 ⇒ 00:04:17.140 Mitesh Patel: There he is!
22 00:04:18.970 ⇒ 00:04:21.070 rob: Always forget to turn my camera on.
23 00:04:24.249 ⇒ 00:04:29.510 Robert Tseng: I’m a camera on in the mornings, and then in the afternoon I’m back to camera off.
24 00:04:31.440 ⇒ 00:04:34.070 rob: If anything, I’m the opposite. But.
25 00:04:34.550 ⇒ 00:04:35.390 Robert Tseng: Oh, yeah.
26 00:04:35.650 ⇒ 00:04:36.380 rob: Okay.
27 00:04:40.940 ⇒ 00:04:42.550 Robert Tseng: Hey! Devotee! Nanny.
28 00:04:43.660 ⇒ 00:04:44.646 Demilade Agboola: Hi. Roberts.
29 00:04:45.680 ⇒ 00:04:46.550 Mitesh Patel: Hello!
30 00:04:50.560 ⇒ 00:04:54.785 Robert Tseng: Okay, I think we can start hopefully, which joins. He joins.
31 00:04:56.980 ⇒ 00:05:00.330 Robert Tseng: yeah, I mean, I guess I mean, I’ll share this. But
32 00:05:00.840 ⇒ 00:05:15.365 Robert Tseng: for Mattesh, I mean, I think, we just we just talked about like what we’re waiting on from you. But you you can feel free to stick around and and look what else we’re working on. But otherwise, like I I think you, you don’t have to stay on
33 00:05:15.770 ⇒ 00:05:18.585 Robert Tseng: And then for Rob, I think.
34 00:05:19.180 ⇒ 00:05:22.069 Robert Tseng: yeah, I know you, you like to pop in and out. So just wanna
35 00:05:22.180 ⇒ 00:05:33.169 Robert Tseng: you know, this is still, if you wanted to kind of just check in on the couple of things that we asked about yesterday. Maybe that getting some updates there would be helpful. And then you can. You can jump off if you need to.
36 00:05:33.170 ⇒ 00:05:39.005 rob: Yeah, I don’t really have updates. Man, I got the stuff to Jonah that he wanted.
37 00:05:39.920 ⇒ 00:05:44.980 rob: I had work on another contract, so I didn’t really work much yesterday, but.
38 00:05:45.150 ⇒ 00:05:45.790 Robert Tseng: Okay.
39 00:05:46.426 ⇒ 00:05:52.719 Robert Tseng: I know that. Ryan threw us all into a channel. So I just also wanted to kind of call that out.
40 00:05:52.720 ⇒ 00:05:56.180 rob: Yeah. Well, I don’t even know what Embeddables is.
41 00:05:58.957 ⇒ 00:06:04.842 Robert Tseng: I do. So I guess, Tim, a lot. I think this is where the gap is. So basically
42 00:06:05.510 ⇒ 00:06:11.569 Robert Tseng: well, I guess, Rob, I’m assuming you must have said helped Ryan set up.
43 00:06:11.680 ⇒ 00:06:21.299 Robert Tseng: Actually, maybe you don’t like that. Somehow, he has in edible is like the new intake form software. It’s like something that happens
44 00:06:21.750 ⇒ 00:06:27.660 Robert Tseng: before the patient is routed back into bask, and that you know that way. We get all that questionnaire data
45 00:06:29.210 ⇒ 00:06:36.480 Robert Tseng: somehow. He’s pushing that data into customer. I/O. He doesn’t know how he’s doing it. Which is why we’re not able to bring it into the data Warehouse.
46 00:06:36.970 ⇒ 00:06:50.269 rob: Yeah, I talked to him about that. He asked me, and I was like, I have no idea, like he was wondering if something happens at the data layer and web flow. I don’t know that’s possible, but I have no idea how it’s getting in a customer. I/O.
47 00:06:51.450 ⇒ 00:07:08.409 Robert Tseng: Yeah, I don’t think Bobby’s doing anything with it, anyway. So I I don’t even know if I necessarily believe that. What would I had said about it being a customer. I/OI do understand that Embeddables, you know it. It kind of operates similarly to like Google Tag, manager, or like
48 00:07:08.680 ⇒ 00:07:12.970 Robert Tseng: it fires events into the data layer in the same in the same fashion. So.
49 00:07:12.970 ⇒ 00:07:13.370 rob: Okay.
50 00:07:14.710 ⇒ 00:07:26.110 Robert Tseng: yeah, I mean, it’s not hard for us to go and retrieve it also, and segmented to pull into the data warehouse. I just think there was some confusion on, like what was actually set up, and what what was it? So I don’t know, Mattesh.
51 00:07:26.110 ⇒ 00:07:27.860 rob: Don’t know who would have seen.
52 00:07:27.860 ⇒ 00:07:28.270 Robert Tseng: On, that.
53 00:07:28.270 ⇒ 00:07:30.629 rob: Yeah, Mattesh, do you know who would have done that?
54 00:07:31.840 ⇒ 00:07:53.710 Mitesh Patel: I think if Sebastian may have helped set that up, and before that, maybe actually, Zack Casey, what you know, because he was responsible for the intakes right? And Zack had essentially, initially started to set it up. I don’t know how far he got, but I believe Sebastian confirmed for us that
55 00:07:53.710 ⇒ 00:08:07.350 Mitesh Patel: it’s going into segment that this data from the the intakes is going into segment. And then that’s where Bobby is getting it into from. It’s from there that Bobby is getting into customer. I/O. There is.
56 00:08:07.350 ⇒ 00:08:10.729 rob: What’s weird is. I don’t see it in segment. Do you guys.
57 00:08:11.050 ⇒ 00:08:12.180 Robert Tseng: It’s not a segment. Yeah.
58 00:08:12.180 ⇒ 00:08:12.530 Demilade Agboola: We’re trying.
59 00:08:12.530 ⇒ 00:08:13.390 Mitesh Patel: No. Okay.
60 00:08:13.390 ⇒ 00:08:26.090 Demilade Agboola: That’s exactly kind. That’s exactly the problem. So I can’t see any segments. I see the customerio connection, but it’s not part of the schema that is being passed through in customerio.
61 00:08:26.370 ⇒ 00:08:36.379 Demilade Agboola: So I’m trying to figure out if it’s a thing of like customer. Dio has it, and we’re just. We’re not just getting it, or it’s actually not just in customer, the I/O at all. And we need to like figure that out.
62 00:08:37.200 ⇒ 00:08:37.580 Mitesh Patel: So
63 00:08:38.120 ⇒ 00:08:55.440 Mitesh Patel: that’s the part that yeah, that’s the part that we have to figure out is, you know, once the data is in the data layer. We’re not doing anything with it, and that’s what I think. Bobby wanted to make sure. Got into Customer I/O, and there was some confusion about what is the status of it?
64 00:08:56.950 ⇒ 00:08:57.580 Robert Tseng: Okay.
65 00:08:57.880 ⇒ 00:09:04.850 rob: I think you helped us solve one of the mysteries, which is, I didn’t know Embeddables had been around long enough for Zach Casey.
66 00:09:04.850 ⇒ 00:09:15.131 Mitesh Patel: No, no, Embeddables has not Embeddables has not been, but they were using the this. They, I think, are using the same mechanism that
67 00:09:17.600 ⇒ 00:09:23.439 Mitesh Patel: We set up for our, you know, the our intakes, the Basque intakes.
68 00:09:23.590 ⇒ 00:09:25.400 rob: Oh, Gotcha!
69 00:09:26.110 ⇒ 00:09:28.800 rob: So they might just be flowing through that same.
70 00:09:29.200 ⇒ 00:09:29.860 Mitesh Patel: Yeah.
71 00:09:32.530 ⇒ 00:09:43.639 Robert Tseng: Yeah, actually, I just mess with Bobby. I’m gonna bring him into this call. So when he jumps on like I’ll I’ll ask him how he’s because he’s the he’s the end user of this. So hopefully, he has a different perspective.
72 00:09:44.030 ⇒ 00:09:44.470 Mitesh Patel: Alright. Let’s
73 00:09:46.500 ⇒ 00:09:55.719 Robert Tseng: Okay. Well, when he joins I’ll kind of mention it. But just for the rest of my team I’ll just kind of keep going through this? Yeah, okay.
74 00:09:55.880 ⇒ 00:09:57.920 Robert Tseng: there he is. Alright. Hey? Hey, Bobby.
75 00:09:58.180 ⇒ 00:09:59.339 Bobby Caruso: Hey? How are you guys.
76 00:10:00.100 ⇒ 00:10:05.518 Robert Tseng: Good. How are you? Sorry for the last minute pull in. We were just talking about.
77 00:10:05.990 ⇒ 00:10:21.529 Robert Tseng: yeah, we’re a bit confused on how Embeddables data is getting into customer. I/O, somehow it is. And so I’m assuming you’re using it somehow. So curious like, if you know who set that up for you. Forgotten customer I/O and we’re trying to also kind of get that data into the data warehouse. So.
78 00:10:22.290 ⇒ 00:10:25.014 Bobby Caruso: Yeah, I think that Ryan.
79 00:10:26.110 ⇒ 00:10:28.767 Bobby Caruso: we met last week. He had
80 00:10:29.450 ⇒ 00:10:35.173 Bobby Caruso: the only area that we talked about. But I’m I’m I’m sure this is embeddables related is
81 00:10:36.020 ⇒ 00:10:43.720 Bobby Caruso: 2 events. There was one for like products available and and one for like page.
82 00:10:43.870 ⇒ 00:10:55.110 Bobby Caruso: essentially like every page in a intake that someone completes to capturing that data and sending it as an event. There was a name for it. I don’t think it was just, page. Viewed, it might have been
83 00:10:56.590 ⇒ 00:11:01.069 Bobby Caruso: And then there was a second one specific to med care. It’s like, basically as people
84 00:11:02.400 ⇒ 00:11:15.650 Bobby Caruso: like filter out of eligibility for certain med kits like once they’re done with those qualifying questions, setting an event that tells me which med kits they remain eligible for. So we don’t like. Tell them about my form, and if they’re not eligible for it.
85 00:11:16.840 ⇒ 00:11:32.680 Robert Tseng: Yeah, yeah, I think you’re. You’re right. There’s there’s 3 events in the data layer that I’ve seen. There’s an identify event, obviously. And then there’s the page completed, and then the products available. So you can always know, like what question they answered and like what the answer was, and then also like what
86 00:11:33.358 ⇒ 00:11:45.019 Robert Tseng: like, when a user is disqualified from a particular product, I guess so those are, I mean, those are the only ones I saw. But I just I don’t know how he’s bringing them into customer. I am so.
87 00:11:45.620 ⇒ 00:11:51.766 Bobby Caruso: It looks like via segment. I mean, I like I don’t know much, but if I’m looking at
88 00:11:52.580 ⇒ 00:11:55.009 Bobby Caruso: and customer, you have access right.
89 00:11:55.900 ⇒ 00:11:57.130 Robert Tseng: Yes.
90 00:11:57.780 ⇒ 00:11:58.520 Bobby Caruso: Yeah, if you just go.
91 00:11:58.520 ⇒ 00:11:59.070 Robert Tseng: Yeah, I do.
92 00:12:00.290 ⇒ 00:12:04.379 Bobby Caruso: Yeah, if you go like. There, you can see I can’t share my screen. It’s not letting me. But
93 00:12:04.950 ⇒ 00:12:12.930 Bobby Caruso: If you go there, it just shows you like source segment. Any requests.
94 00:12:12.930 ⇒ 00:12:15.259 Robert Tseng: Screen share. Just so I could, you know, or like.
95 00:12:15.590 ⇒ 00:12:17.929 Bobby Caruso: Oh, now it’s letting me.
96 00:12:18.850 ⇒ 00:12:19.194 Robert Tseng: Okay.
97 00:12:20.080 ⇒ 00:12:20.866 Bobby Caruso: Let’s see.
98 00:12:25.050 ⇒ 00:12:26.340 Bobby Caruso: Can you see my screen.
99 00:12:27.040 ⇒ 00:12:27.590 Robert Tseng: Yeah.
100 00:12:27.950 ⇒ 00:12:31.850 Bobby Caruso: Cool. Yeah, so basically, it’s just it’s like this vague
101 00:12:32.100 ⇒ 00:12:36.919 Bobby Caruso: source to segment. Or maybe if I click this, this might mean something more to you.
102 00:12:38.489 ⇒ 00:12:42.579 Bobby Caruso: But yeah, I I guess it’s passing through segment first.st
103 00:12:44.060 ⇒ 00:12:57.550 Robert Tseng: Okay, yeah, it’s interesting, because we don’t see anything in segment. But okay, I think we need to keep looking into it. Maybe. Well, yeah, if anything, Demo, I guess we’ll we compare on this later, I think. We’re probably not gonna figure it out on this call.
104 00:12:59.100 ⇒ 00:13:03.199 Robert Tseng: But yeah, no, I do see it as a segment action. So that’s interesting. But.
105 00:13:03.520 ⇒ 00:13:07.780 Bobby Caruso: Call anything else you need from me on the CIO side.
106 00:13:08.410 ⇒ 00:13:13.178 Robert Tseng: No, that was it. Yeah. So thank you. And see, talk to you later.
107 00:13:13.510 ⇒ 00:13:14.719 Bobby Caruso: Awesome talk to you soon.
108 00:13:14.720 ⇒ 00:13:24.100 Robert Tseng: Okay, alright. So we’ll keep kind of figuring that out. Yeah.
109 00:13:24.290 ⇒ 00:13:29.586 Robert Tseng: I guess I’m just gonna keep going through the rest of the stand up. Then.
110 00:13:32.120 ⇒ 00:13:39.049 Robert Tseng: let’s talk about yeah, I wish
111 00:13:39.850 ⇒ 00:13:51.240 Robert Tseng: we you know, we need to follow up with corral today. But I don’t know any updates on either. The modeling that you did here like kind of yeah. Any for the tickets that you were working on that are assigned to you.
112 00:13:52.380 ⇒ 00:13:58.360 Awaish Kumar: Yeah, like, that’s the one which is assigned to me. And I’m waiting for coral to
113 00:13:58.760 ⇒ 00:14:03.259 Awaish Kumar: to like, send. Like I, we have shared the sample data. So I’m just waiting.
114 00:14:03.550 ⇒ 00:14:04.919 Awaish Kumar: If they.
115 00:14:04.920 ⇒ 00:14:07.340 Robert Tseng: Yeah, they haven’t brought the data in yet. Yeah.
116 00:14:07.710 ⇒ 00:14:11.939 Awaish Kumar: So still they somehow like get it to into bigquery, so I can work on it.
117 00:14:13.780 ⇒ 00:14:19.959 Robert Tseng: Okay, yeah. Mattesh, this is relevant to you, because on this, on the marketing dashboard for the mer section.
118 00:14:19.960 ⇒ 00:14:20.370 Mitesh Patel: Yep.
119 00:14:20.370 ⇒ 00:14:23.739 Robert Tseng: We are going direct with. I mean, corral
120 00:14:23.930 ⇒ 00:14:40.200 Robert Tseng: basically set up the direct connections with these ad platforms saved us from having to do all that modeling ourselves. And then we’ve already given them the schema for like how we want that data to be kind of brought back into our warehouse. So we’re just kind of waiting on them to get into that format.
121 00:14:40.320 ⇒ 00:14:40.800 Mitesh Patel: Okay.
122 00:14:41.590 ⇒ 00:14:42.380 Robert Tseng: Yes.
123 00:14:44.640 ⇒ 00:14:54.089 Robert Tseng: oh, wish I guess. Maybe on the customer side. So yeah, we talked about yeah, location, not really available. So I’m just gonna put this as blocked.
124 00:14:54.300 ⇒ 00:15:00.040 Robert Tseng: But I think you know, the cutter and around have already been
125 00:15:00.400 ⇒ 00:15:05.880 Robert Tseng: they, you know, they’ve been using the dashboard as is. So I don’t think that’s as big of a priority anymore.
126 00:15:08.900 ⇒ 00:15:18.179 Robert Tseng: yeah, maybe we’ll just move on to get them a lot of we had a couple of follow ups. I know you wanted to close the investigation on the order and transaction stuff.
127 00:15:19.038 ⇒ 00:15:23.199 Robert Tseng: And then, yeah, obviously, we just talked about the intake pipeline.
128 00:15:24.590 ⇒ 00:15:37.509 Demilade Agboola: Yeah. So for the investigation on the orders data stuff, basically, we just have noticed that. I mean, I also flagged this yesterday, but we’ve noticed that the order is coming in
129 00:15:38.560 ⇒ 00:15:45.219 Demilade Agboola: about 30 to 40% of them don’t have transaction ids
130 00:15:45.410 ⇒ 00:15:48.580 Demilade Agboola: on some days that goes as high as 50%
131 00:15:49.778 ⇒ 00:15:55.520 Demilade Agboola: so ultimately, what that just means for us is if we’re gonna use it for
132 00:15:56.251 ⇒ 00:16:17.330 Demilade Agboola: tracking the Tran, the customer journey flow, and trying to figure out like the transaction when it happened, and you know, when it was delivered, I mean, we still know the dates. The order happened, so we can use the dates instead. But in terms of like that level of granularity with like the actual transaction, we currently cannot do that for about 30 to 40% of all orders.
133 00:16:17.931 ⇒ 00:16:22.550 Demilade Agboola: So if it’s something we push, we bask, we can.
134 00:16:22.680 ⇒ 00:16:28.920 Demilade Agboola: If it’s something that we’re fine doing without, we can just continue with our current workflow that just excludes that.
135 00:16:32.370 ⇒ 00:16:39.600 Robert Tseng: Yeah, so I mean, basically, Josh, this is kind of like, an it’s a long thread coming. But
136 00:16:41.070 ⇒ 00:16:49.819 Robert Tseng: yeah, I in summary, like I showed Zack Mask here all these orphaned transactions. I called them transactions that don’t have
137 00:16:50.050 ⇒ 00:17:18.449 Robert Tseng: orders are connected to them. He came back 2 weeks later, and he was like, Hey, they’re there like screw you. And it’s like, Okay, sure, there is a delay, and every transaction eventually does get matched to an order, but then it’s kind of 2 sides of the same coin. But from the other side we have orphaned orders that don’t ever get transactions, so it’s not. There’s a delay in generating orders from transactions that’s about a 2 week period. But then there’s also a
138 00:17:18.550 ⇒ 00:17:22.470 Robert Tseng: orders never end up having transactions problem that
139 00:17:22.640 ⇒ 00:17:27.790 Robert Tseng: I feel like, we just yeah, that’s that’s kind of that’s what we’re.
140 00:17:27.790 ⇒ 00:17:28.960 Josh : What does that happen?
141 00:17:30.400 ⇒ 00:17:40.799 Robert Tseng: 30 to 40% after since since February 11, th when I when I when we called it out, they updated by sending some new some new web hooks.
142 00:17:41.030 ⇒ 00:17:48.649 Demilade Agboola: So by before February 11, th we used to be about 7%, 7 to 10%, which I mean, it’s still like
143 00:17:49.240 ⇒ 00:17:57.328 Demilade Agboola: manageable, I guess. But after the 11 it just kind of spiked to 30, 40, and some days 50% of all orders will not have
144 00:17:57.940 ⇒ 00:17:59.139 Demilade Agboola: transaction ids.
145 00:18:01.660 ⇒ 00:18:07.749 Robert Tseng: So the implication here is that like, okay, we are. I mean for most of our reporting.
146 00:18:08.856 ⇒ 00:18:12.419 Robert Tseng: Yeah, like, product, roast ltp, whatever we’re using orders.
147 00:18:12.870 ⇒ 00:18:19.330 Robert Tseng: Great product sale like a wish. Correct me if I’m wrong, but product sales summary by trans, oh, no, it’s by transaction.
148 00:18:19.610 ⇒ 00:18:35.029 Robert Tseng: So yeah, we have a risk of what? Under reporting some of the orders? Right? If for any model that’s using transactions only we could, we could be under reporting like what the orders like our actual orders. That that’s that’s the risk here, right.
149 00:18:36.560 ⇒ 00:18:43.390 Awaish Kumar: Actually we, which we call it by transaction. But we are counting on order number like order number.
150 00:18:43.660 ⇒ 00:18:47.990 Awaish Kumar: So if the order is there, then, yeah, we it is counted.
151 00:18:48.740 ⇒ 00:18:54.979 Robert Tseng: Okay, never mind. Then, yeah. Well, whe, wherever we are using transaction. So they might, maybe we need to figure we need to just.
152 00:18:54.980 ⇒ 00:18:56.640 Demilade Agboola: I’d have to like already on that. Yeah.
153 00:18:56.640 ⇒ 00:18:59.160 Demilade Agboola: So in terms of this, in terms of like the actual risk.
154 00:18:59.600 ⇒ 00:19:07.309 Demilade Agboola: it’s more of in this actual ticket. Because I’m I’m talking about these actual tickets. It’s we just don’t know the transactions that tied to it.
155 00:19:07.440 ⇒ 00:19:10.500 Demilade Agboola: And so if we’re looking at the customer journey flow.
156 00:19:10.930 ⇒ 00:19:26.879 Demilade Agboola: What we can just do is we can map it to. We’ll have to map it to the order. The date, the order happened. Not necessarily the date, like when the transaction itself happened. So, for instance, if you want, like an hourly flow from like, how long in terms of hours does it take from
157 00:19:27.538 ⇒ 00:19:30.820 Demilade Agboola: the transaction happening to? It’s been shipped.
158 00:19:31.000 ⇒ 00:19:35.090 Demilade Agboola: But we we don’t necessarily. We can’t necessarily do that. We could probably do the day.
159 00:19:35.210 ⇒ 00:19:43.129 Demilade Agboola: So it took 0 days, took one day for it to happen or 2 days to happen, so that kind of level of granularity will be missing. That kind of like.
160 00:19:43.130 ⇒ 00:19:43.690 Robert Tseng: Yeah.
161 00:19:43.690 ⇒ 00:19:45.889 Demilade Agboola: The the huge things to point out.
162 00:19:46.880 ⇒ 00:19:47.480 Robert Tseng: Okay.
163 00:19:48.430 ⇒ 00:19:59.569 Robert Tseng: that’s okay. I mean, I just, I want to call out that Annie, the dashboard that you’ve been working on the customer journey dashboard. We defined the order journey as order completed.
164 00:19:59.810 ⇒ 00:20:12.910 Robert Tseng: like when the order is actually placed to when it’s delivered. So we aren’t using the transaction as the 1st as the as the as the start. So yeah, I think we are kind of already doing what you’re suggesting.
165 00:20:13.910 ⇒ 00:20:14.470 Demilade Agboola: Okay.
166 00:20:14.470 ⇒ 00:20:16.910 Josh : So how do you wanna broach this topic with?
167 00:20:17.150 ⇒ 00:20:25.149 Josh : And, Mr. Zad, what are the business implications of not getting these pieces of data in a timely fashion?
168 00:20:25.970 ⇒ 00:20:33.640 Robert Tseng: Yeah, the the the implication. Josh, is just that like the transaction to order, drop off
169 00:20:33.780 ⇒ 00:20:41.679 Robert Tseng: or like, it’s just still unclear. It’s he kind of sent some intermediary web hooks that are supposed to tell us like
170 00:20:41.810 ⇒ 00:20:49.950 Robert Tseng: transaction completed. And then whatever but like those didn’t actually solve, they didn’t actually give us more visibility into
171 00:20:50.840 ⇒ 00:21:03.060 Robert Tseng: the trans, yeah, into that, into the gap between the transaction and the order. Because we’re still not. It’s not firing for all of all of the orders. Right? So I don’t. I? I guess.
172 00:21:03.060 ⇒ 00:21:03.420 Demilade Agboola: Yeah.
173 00:21:03.420 ⇒ 00:21:04.330 Robert Tseng: I don’t know.
174 00:21:05.000 ⇒ 00:21:05.490 Josh : So go ahead.
175 00:21:05.490 ⇒ 00:21:06.149 Robert Tseng: I didn’t want it.
176 00:21:06.150 ⇒ 00:21:10.300 Demilade Agboola: I was. Gonna say that. Yeah, it’ll be hard to find the drop off if we don’t know all
177 00:21:11.100 ⇒ 00:21:13.299 Demilade Agboola: orders that came from
178 00:21:13.550 ⇒ 00:21:26.229 Demilade Agboola: the transactions like we could just do an approximate, for instance, and say, Oh, these are all transactions that happened yesterday. These are the total number of quarters that happened yesterday. Here is the drop off. But it’s hard to say, hey, this transaction
179 00:21:26.580 ⇒ 00:21:35.799 Demilade Agboola: actually conveyed into this order. If 40% or 30 40% of all orders were seen on that day. Don’t have any transactions like it’s hard to tie them together.
180 00:21:41.350 ⇒ 00:21:45.440 Josh : Well, like, what are the business implications like? What can we not do because of this.
181 00:21:48.310 ⇒ 00:21:50.967 Demilade Agboola: So if we’re trying to figure out the
182 00:21:52.280 ⇒ 00:21:59.720 Demilade Agboola: the drop off like the transaction, drop off in terms of like the level of granular granularity that we desire like. We can’t do that
183 00:22:00.340 ⇒ 00:22:04.960 Demilade Agboola: like, I said. We can do like high numbers like, oh, this is the count of all transactions yesterday.
184 00:22:05.080 ⇒ 00:22:15.669 Demilade Agboola: and this is accountable orders yesterday. So therefore this, but like in terms of like what transactions, and if we want to take any actions on it, or if we want to see what’s going on.
185 00:22:16.425 ⇒ 00:22:18.999 Demilade Agboola: It’s hard to do that.
186 00:22:19.190 ⇒ 00:22:23.219 Robert Tseng: Or not. Yeah, yeah, give me a moment. Let me put a different way. So like.
187 00:22:23.450 ⇒ 00:22:29.929 Robert Tseng: you know, like the whole point of adding the additional web hooks that bash pushing that up bash pushed in that update in February.
188 00:22:29.930 ⇒ 00:22:30.570 Josh : Hurry!
189 00:22:30.570 ⇒ 00:22:42.059 Robert Tseng: Was so that we could investigate this theoretical drop off of like, hey? Why are transactions not converting into orders? But they added a couple more web hooks that and it.
190 00:22:42.190 ⇒ 00:23:01.900 Robert Tseng: We went down this like rabbit hole, like trying to see if that those those additional web hooks actually helped us answer that question. The answer is still no like we we don’t know. Like we still only have order level, like granularity. We trust everything from when the order is completed onwards. But we can’t really do a a like A, very.
191 00:23:02.040 ⇒ 00:23:07.619 Robert Tseng: We can’t do a full trace back to the transaction for 30 to 40% of the orders.
192 00:23:07.810 ⇒ 00:23:09.250 Robert Tseng: That’s thank you.
193 00:23:09.250 ⇒ 00:23:10.359 Josh : That’s that’s the problem.
194 00:23:10.360 ⇒ 00:23:10.980 Josh : Sure.
195 00:23:11.110 ⇒ 00:23:18.159 Robert Tseng: Which you know, it’s it’s it. Yeah. I mean, it doesn’t impact our core bi reporting because we’ve structured it around it being order 1st
196 00:23:18.759 ⇒ 00:23:25.890 Robert Tseng: but we can’t do the we yeah, we’re just not able to do the the transaction to order drop off analysis
197 00:23:26.390 ⇒ 00:23:27.830 Robert Tseng: with confidence.
198 00:23:28.400 ⇒ 00:23:33.299 Josh : The business impact again, I’m trying to help you get. I’m trying to steal me in your argument.
199 00:23:33.660 ⇒ 00:23:36.400 Josh : So the business impact is we cannot do one.
200 00:23:36.530 ⇒ 00:23:40.630 Josh : We cannot have a debt. Better understanding, like actual orders.
201 00:23:42.540 ⇒ 00:23:49.789 Robert Tseng: We don’t have a good understanding of, like the transactions that are converting to orders like, we don’t. Yeah, we don’t like it.
202 00:23:49.790 ⇒ 00:23:54.430 Josh : So why does that? Why is that important side.
203 00:23:56.380 ⇒ 00:24:08.759 Robert Tseng: Well, I think just traditionally, from like a financial reporting perspective like Danny’s reports when he recognizes revenue. It’s when the transaction is. It come comes through right? So it’s just like
204 00:24:08.980 ⇒ 00:24:27.839 Robert Tseng: from OP business operations perspective. You look at everything from the order perspective. But like, I guess now, with Jonah and Danny, and whatever, when we’re looking at from like financial metrics, they’ll want to look at it to the transaction level. So we don’t have that level the level of granularity that the finance team would typically want to see in a business like this.
205 00:24:32.990 ⇒ 00:24:39.429 Josh : And that’s because we can’t use the order. Ids. We have to use transaction ids.
206 00:24:39.850 ⇒ 00:24:50.480 Robert Tseng: That. That’s because we can’t map transaction like not every order can be mapped to a transaction. Which is to me is like this is, this should be table states. This is like.
207 00:24:50.480 ⇒ 00:24:55.789 Josh : Yeah, yeah, no, I’m just. I’m trying to. I’m trying to make it so like we can deliver some sort of report.
208 00:24:56.050 ⇒ 00:24:59.870 Josh : Zack, and be like. Look, here’s what we’ve observed.
209 00:25:00.210 ⇒ 00:25:05.059 Josh : You know. What are your thoughts? Because, as of right now, we don’t have a good idea of how to do. X.
210 00:25:05.320 ⇒ 00:25:09.090 Josh : What would you recommend like? That’s what I’m trying to get this through, because that way
211 00:25:09.350 ⇒ 00:25:13.740 Josh : it’s not coming off to him or his, you know. He’s calling his baby ugly.
212 00:25:14.020 ⇒ 00:25:25.639 Josh : and then we’re able to get him to acknowledge like, Hey, we’ve identified these things. Do you have any idea of workarounds or things that we can do so that this this team can go and do what?
213 00:25:25.820 ⇒ 00:25:34.189 Josh : Because, as of right now, the team cannot do. Why, therefore, it is inhibiting their ability to perform blank analysis.
214 00:25:34.360 ⇒ 00:25:41.719 Josh : You see what I mean like that. I’m just trying to get this into a very simple business sheet. So we can. We can adjust it and make it work.
215 00:25:43.690 ⇒ 00:25:49.109 Robert Tseng: Yeah, no, I I hear you. I mean, we’re trying to. We’re trying to push it towards that. So yeah, I mean, Dave, a lot is kind of like what I was.
216 00:25:49.380 ⇒ 00:26:13.241 Robert Tseng: I’ve been like trying to push us as well like this past week of like, okay, to conclude this investigation, we need to be able to like if we say, be able to explain, like, basically, what what Josh is saying here. So I think we’re there. They just kind of clarifying the narrative of like what this is stopping us from doing. And then I mean, I I can. I can help with the messaging there. So.
217 00:26:13.770 ⇒ 00:26:17.519 Demilade Agboola: Sounds good. So we can. We can tie this up today. And that’s good.
218 00:26:18.250 ⇒ 00:26:18.720 Robert Tseng: So.
219 00:26:22.110 ⇒ 00:26:31.930 Robert Tseng: let’s yeah, Annie. I know we haven’t given you any air time yet, and you’ve been working on something. So you want to talk about the customer journey, dash.
220 00:26:33.010 ⇒ 00:26:35.140 Annie Yu: Yeah. So I did some updates
221 00:26:35.400 ⇒ 00:26:48.459 Annie Yu: from yesterday’s discussion. And so that pharmacy filter for Boothwyn is merged. But I think it’s still a good idea to consolidate that from the upstream for future use.
222 00:26:48.820 ⇒ 00:27:07.359 Annie Yu: And then the order status chart. I know we talk about the stages change so there are only 3 stages now in that chart from order completed to send to pharmacy and sent to pharmacy to shipped
223 00:27:07.550 ⇒ 00:27:26.160 Annie Yu: as well as order shipped to delivered, and just one thing I know that there are multiple columns already calculated in the model, and I noticed there’s no like order to deliver days. There’s order to delivery, but not delivered. So I
224 00:27:26.160 ⇒ 00:27:37.750 Annie Yu: had to. Do manual calculation. If delivery status equals delivered, and then we get that date difference. So I’m not sure if we want to add that
225 00:27:38.210 ⇒ 00:27:46.139 Annie Yu: calculations in the model for future use, or or if not, we can just keep it in tableau.
226 00:27:46.620 ⇒ 00:28:01.360 Robert Tseng: Yeah, no, I mean, thank you for doing the happy solution. I think that that’s fine, like we we didn’t, I mean, if it comes up again. Then we’ll consider pushing to the model. But like, I think that’s fine, like you’re you’re using what we have in the model to like. Get to the
227 00:28:01.460 ⇒ 00:28:10.239 Robert Tseng: get to the status like the field that you want, and I think that’s that’s great. I appreciate that you did that. We don’t not. Everything is a bottle. Change immediately.
228 00:28:10.620 ⇒ 00:28:13.561 Annie Yu: And then that 3rd chart.
229 00:28:14.600 ⇒ 00:28:20.560 Annie Yu: I added this one. So now we can see you’ll see that like light
230 00:28:20.710 ⇒ 00:28:32.439 Annie Yu: blue is the orders that was sent to pharmacy in that day, and then the dark blue shows the orders that were shipped within 3 days.
231 00:28:32.750 ⇒ 00:28:35.980 Annie Yu: and when you hover you can see 47%.
232 00:28:36.530 ⇒ 00:28:37.180 Robert Tseng: Yeah.
233 00:28:37.718 ⇒ 00:28:45.350 Annie Yu: And then with that line there, that’s the average time.
234 00:28:45.350 ⇒ 00:28:46.799 Robert Tseng: Average time in days. Right?
235 00:28:46.800 ⇒ 00:28:51.729 Annie Yu: Pharmacy got it, and then to when they ship. I’m not sure if this is clear right now.
236 00:28:52.470 ⇒ 00:28:58.680 Robert Tseng: No, I think this is this is great. I’ll I’ll share. Share this with Rebecca. I think my question.
237 00:28:59.100 ⇒ 00:29:05.439 Robert Tseng: I know that I mean, these numbers look higher than what we saw in the previous version, which maybe. Yeah, I guess.
238 00:29:05.440 ⇒ 00:29:13.020 Annie Yu: Yeah, I know you mentioned that. I I think the one thing that I noticed was just that when we saw the average
239 00:29:13.530 ⇒ 00:29:23.710 Annie Yu: hours previously she was using order to send to pharmacy, so was a a different.
240 00:29:25.710 ⇒ 00:29:30.330 Robert Tseng: Got it? Yeah, that’s not really pharmacy turnaround. Okay? Got it?
241 00:29:30.490 ⇒ 00:29:31.230 Robert Tseng: Yeah.
242 00:29:32.490 ⇒ 00:29:47.170 Robert Tseng: So let’s be clear. So the set to pharmacy to ship. Yeah, that’s the that’s the pharmacy turnaround window, and that needs to be under 3 days. That’s that’s that’s Rebecca’s sla. So I guess what this is telling me is, you know, maybe last 2 weeks we didn’t hit it this week. We’re hitting it.
243 00:29:47.540 ⇒ 00:29:52.679 Robert Tseng: Okay, no, I think this is much clearer. But I guess, Josh, maybe you’ve seen.
244 00:29:52.990 ⇒ 00:29:58.580 Josh : No, no, this, this, this is much clearer. Yeah. I think that there’s also one other thing missing.
245 00:29:58.850 ⇒ 00:30:01.979 Josh : which is that 1st metric that you talked about
246 00:30:02.796 ⇒ 00:30:17.960 Josh : which is, you know, order completed to order shipped total time, because that way we understand how long the doctors are taking to review stuff, and then we also understand we can back end our way into this exact chart, too. Just how long
247 00:30:18.530 ⇒ 00:30:22.720 Josh : pharmacy is taking the ship. Once it gets the the subscription.
248 00:30:23.480 ⇒ 00:30:24.100 Robert Tseng: Yeah.
249 00:30:24.330 ⇒ 00:30:31.090 Robert Tseng: Okay. So yeah, Annie, I think maybe this is like.
250 00:30:31.240 ⇒ 00:30:41.809 Robert Tseng: I don’t know if it should be. This might be too clutter with one more line here, but it’s basically the the full, the full journey right, the completed to to to to shift.
251 00:30:42.840 ⇒ 00:30:45.090 Annie Yu: Wait to shipped, or to delivered.
252 00:30:46.271 ⇒ 00:30:49.170 Robert Tseng: We broke this up from order to yeah. Wait, Josh, go ahead.
253 00:30:50.090 ⇒ 00:30:56.720 Josh : It’s just the ship. I care more about the time that cause like shipping, can, you know? Vary? It depends.
254 00:30:57.400 ⇒ 00:31:01.039 Josh : But as soon as it leaves the door. That’s what I care about.
255 00:31:01.430 ⇒ 00:31:01.860 Annie Yu: Okay.
256 00:31:01.860 ⇒ 00:31:02.220 Robert Tseng: Okay.
257 00:31:02.530 ⇒ 00:31:03.600 Annie Yu: Then.
258 00:31:03.850 ⇒ 00:31:10.700 Annie Yu: So in our model, there is already something we do have order to delivery that just mean
259 00:31:11.550 ⇒ 00:31:14.240 Annie Yu: wait, order to shift. Now we do have.
260 00:31:14.240 ⇒ 00:31:14.840 Robert Tseng: Yeah.
261 00:31:14.840 ⇒ 00:31:15.390 Josh : Ops.
262 00:31:15.390 ⇒ 00:31:23.120 Annie Yu: So I guess my question is, then we don’t want to see delivered right. We just care up until.
263 00:31:23.120 ⇒ 00:31:31.090 Josh : I mean you can. We can. I mean, if it’s if it’s there, it’s cool. I’m just talking about things that we have actual control over with our
264 00:31:31.440 ⇒ 00:31:39.420 Josh : really like this is good, the one that you have this is really good. The only thing that I don’t know based on this is, how long are the doctors taking to prescribe.
265 00:31:43.420 ⇒ 00:31:51.320 Robert Tseng: Yeah, ordered, is that, and that’s you. Can you measure that from order completed to shipped.
266 00:31:52.090 ⇒ 00:32:06.239 Josh : Yeah. So like the the customer entered their credit card details. Order has been processed, basically the order. And then it’s the order time from like that point the doctor has released the prescription to the pharmacy.
267 00:32:07.180 ⇒ 00:32:10.199 Robert Tseng: Oh, okay, so order to set the pharmacy. That’s what you’re saying.
268 00:32:10.200 ⇒ 00:32:10.870 Josh : Yes.
269 00:32:11.290 ⇒ 00:32:13.469 Robert Tseng: Okay? So that is there
270 00:32:17.970 ⇒ 00:32:20.399 Robert Tseng: like this chart here is like.
271 00:32:20.590 ⇒ 00:32:24.930 Robert Tseng: if you just take the order complete, maybe we make
272 00:32:25.490 ⇒ 00:32:41.390 Robert Tseng: order. Yeah. Order completed to delivered, and it’s broken up into order to set the pharmacy sent to pharmacy to ship. So that’s the pharmacy. So this is the doctor’s window, the pharmacies window, and then the the delivery window.
273 00:32:45.080 ⇒ 00:32:45.930 Josh : Correct.
274 00:32:46.360 ⇒ 00:32:46.940 Robert Tseng: Yeah.
275 00:32:49.040 ⇒ 00:32:54.539 Josh : So yeah, there’s 3 main actions right? There’s the time the time customer takes place. The order
276 00:32:54.830 ⇒ 00:33:00.350 Josh : that’s 1. Then, order being complete. To doctor writing prescription is 2.
277 00:33:00.480 ⇒ 00:33:08.199 Josh : Then, prescription being complete, sent to pharmacy and released from pharmacy, is 3, and then there’s the shipping time after that.
278 00:33:08.450 ⇒ 00:33:11.860 Josh : So, like all of those things are part of that overall customer journey.
279 00:33:12.940 ⇒ 00:33:15.159 Robert Tseng: Yeah. So I guess kind of
280 00:33:15.280 ⇒ 00:33:19.440 Robert Tseng: to tie it back a bit to what we were saying with the limitation of the transaction.
281 00:33:19.550 ⇒ 00:33:28.789 Robert Tseng: so we wouldn’t be able to do. Order makes the payment to when the order is completed like that, like the transaction to completed, that we would not trust.
282 00:33:28.790 ⇒ 00:33:38.849 Josh : See, that’s a great, that’s and that’s great. So take that. Include that in your report, for that like as of right now, we cannot hold our partners accountable because we don’t get this data.
283 00:33:39.552 ⇒ 00:33:45.599 Robert Tseng: For 2 weeks. You see what I’m saying. Those are the business implications that make your argument much more powerful.
284 00:33:45.880 ⇒ 00:33:56.810 Josh : Otherwise it’s just us saying, Oh, dude, we’re not getting this data, and he’s like, Oh, fuck you, you know what I mean, but it’s like, no having a material impact on the business’s ability to run. Then it becomes a bigger conversation.
285 00:33:57.370 ⇒ 00:33:57.970 Robert Tseng: Yeah.
286 00:33:58.580 ⇒ 00:33:59.540 Josh : Okay.
287 00:34:01.900 ⇒ 00:34:17.339 Robert Tseng: Yeah, it’s noted, I think, yeah, I know how to put the story together. Maybe last thing on this. So yeah, I feel like. Yeah, this this order list already looks better to me. I haven’t even looked at the details itself any but like it looks
288 00:34:17.489 ⇒ 00:34:23.309 Robert Tseng: like it’s more than what I saw before, so that makes sense. It makes more sense to me. And then.
289 00:34:23.310 ⇒ 00:34:26.230 Annie Yu: I haven’t touched that one so.
290 00:34:26.239 ⇒ 00:34:26.889 Robert Tseng: Oh, really.
291 00:34:26.889 ⇒ 00:34:28.109 Annie Yu: Still the same.
292 00:34:29.330 ⇒ 00:34:31.269 Robert Tseng: Okay, let’s just take a quick peek.
293 00:34:32.739 ⇒ 00:34:34.769 Annie Yu: But I will into that.
294 00:34:35.290 ⇒ 00:34:41.820 Robert Tseng: Somehow it’s more than 10 now, so I believe this more than what it was yesterday we were selected.
295 00:34:42.219 ⇒ 00:34:50.460 Annie Yu: On only a few products or pharmacy. So I’m not sure that Costa, the wheel.
296 00:34:51.199 ⇒ 00:34:57.010 Robert Tseng: So maybe something to still look into. And then I yeah, I know that you’re still working on the tile that I guess.
297 00:34:57.010 ⇒ 00:35:05.930 Annie Yu: Yeah, I those scorecards. And I think one question I do have is, I think this dashboard is getting like quite large, and I think.
298 00:35:05.930 ⇒ 00:35:06.420 Robert Tseng: Yeah.
299 00:35:06.420 ⇒ 00:35:15.659 Annie Yu: Idea if we can split them into 2 pages, and one can just focus on turnaround time of stages and one covers ticket and agent level details.
300 00:35:16.702 ⇒ 00:35:21.220 Robert Tseng: I agree, yeah, like the stuff ticket and agent stuff can move into a different page.
301 00:35:21.480 ⇒ 00:35:22.086 Annie Yu: Yeah, yeah.
302 00:35:22.690 ⇒ 00:35:23.576 Robert Tseng: Yeah. Okay.
303 00:35:26.874 ⇒ 00:35:37.265 Robert Tseng: Okay. I know we’re at time anything else that I didn’t cover, for now I know we have some ad hoc, or at Async stuff that we should follow up on. But
304 00:35:40.500 ⇒ 00:35:45.770 Josh : Nope, I will. I’ll be out most of the rest of this week doing some in person stuff.
305 00:35:46.328 ⇒ 00:35:49.990 Josh : So just wanted to make sure that all those follow ups we talked about last night.
306 00:35:50.520 ⇒ 00:35:52.430 Josh : Just give me up the loop. Async. Okay.
307 00:35:53.030 ⇒ 00:35:53.900 Robert Tseng: Yeah, we’ll do.
308 00:35:54.180 ⇒ 00:35:56.700 Josh : Cool. Awesome thanks. Man.
309 00:35:56.700 ⇒ 00:35:57.350 Robert Tseng: Thanks.
310 00:35:58.498 ⇒ 00:36:14.070 Robert Tseng: Tim Lade, I think the last thing from you is just the product data model. I know that we are. We’re like, I was in that thread with Christiana. So yeah, they’re launching like something like 20 plus products next week. So I think.
311 00:36:14.190 ⇒ 00:36:18.620 Robert Tseng: yeah, like, I think this is gonna be very relevant to the the launch next week
312 00:36:24.926 ⇒ 00:36:29.210 Robert Tseng: you might be needed in here. Didn’t? Didn’t get anything from me.
313 00:36:32.850 ⇒ 00:36:51.050 Robert Tseng: Okay, maybe he did. I’ll follow up later. Alright. Yeah, no worries. We don’t have to keep this running. I think we’re we’re good some of the new tickets I’ve been putting together. I’ll share it out more with the team throughout the day. But otherwise yeah, just we’ll keep. We’ll keep talking on slack. Alright, thanks, team.
314 00:36:51.050 ⇒ 00:36:51.880 Annie Yu: Thank you.
315 00:36:52.040 ⇒ 00:36:54.000 Robert Tseng: Sorry bye.