Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-06-05 Meeting participants: Fireflies.ai Notetaker Awaish, Robert Tseng, Awaish Kumar, Josh , Annie Yu, Demilade Agboola
WEBVTT
1 00:01:39.270 ⇒ 00:01:40.060 Awaish Kumar: Hello!
2 00:01:43.910 ⇒ 00:01:44.760 Robert Tseng: Hey! Wish.
3 00:01:45.820 ⇒ 00:01:46.930 Awaish Kumar: Hello! How are you?
4 00:01:47.520 ⇒ 00:01:48.570 Robert Tseng: Good! How are you?
5 00:01:49.390 ⇒ 00:01:50.360 Awaish Kumar: I’m good as well.
6 00:01:53.390 ⇒ 00:02:02.940 Robert Tseng: There’s a couple, I think, with the customer I/O questions. If you could take them, I mean, I just I just. I just slacked you, but I’m just reiterating. I’ll let you take the 1st pass at it, and.
7 00:02:02.940 ⇒ 00:02:04.389 Awaish Kumar: I already modeled them.
8 00:02:04.940 ⇒ 00:02:09.659 Awaish Kumar: I already model it, and I’ve shared the updates in the ticket as well.
9 00:02:09.669 ⇒ 00:02:13.609 Robert Tseng: Oh, yeah, yeah, no. I’m I’m just talking about. I mean, there were a couple other
10 00:02:14.449 ⇒ 00:02:29.489 Robert Tseng: like messages about customer I/O data where people are asking, can we do this or that with with Customer I/O. So it’s not necessarily related to the ticket. But since you did a spike on it recently, I’ve just I just want you to to be the 1st line. There.
11 00:02:31.619 ⇒ 00:02:36.389 Robert Tseng: Are you in the I mean, I could tag you. But one, just wanna make sure.
12 00:02:39.459 ⇒ 00:02:40.739 Robert Tseng: Wait.
13 00:02:41.199 ⇒ 00:02:44.269 Robert Tseng: Yeah. So okay, fine.
14 00:02:44.999 ⇒ 00:02:46.229 Robert Tseng: I’ll just tag you
15 00:02:54.559 ⇒ 00:02:57.799 Robert Tseng: cool. Yeah. So there, that that was a question that came from Cutter.
16 00:02:58.378 ⇒ 00:03:02.779 Robert Tseng: Alright, I think we’re mostly here. So we’re just gonna jump into it.
17 00:03:03.530 ⇒ 00:03:03.940 Awaish Kumar: But.
18 00:03:05.460 ⇒ 00:03:06.320 Robert Tseng: Sorry.
19 00:03:07.370 ⇒ 00:03:09.329 Awaish Kumar: Can I ask a question here, if you know?
20 00:03:09.730 ⇒ 00:03:10.270 Awaish Kumar: Thank you.
21 00:03:10.270 ⇒ 00:03:10.640 Robert Tseng: Yeah, I see.
22 00:03:10.640 ⇒ 00:03:16.590 Awaish Kumar: Just click. That means click, link is clicked inside of email, right?
23 00:03:17.690 ⇒ 00:03:22.230 Robert Tseng: Yeah, so I think, yeah, yeah, yeah, email, email, click data.
24 00:03:22.230 ⇒ 00:03:30.959 Awaish Kumar: Email is received and the email is opened. Then there is a click like inside. There’s maybe link something. And when we click on it. We want to track that right.
25 00:03:31.960 ⇒ 00:03:32.690 Robert Tseng: Yeah.
26 00:03:33.440 ⇒ 00:03:37.720 Awaish Kumar: So, yeah, like, our model is based on that. We already have that in our model.
27 00:03:38.570 ⇒ 00:03:42.799 Robert Tseng: Okay. But like the. So I mean, I guess my question would then be
28 00:03:43.650 ⇒ 00:03:50.129 Robert Tseng: so. I mean, there are multiple points that you can click on so we can differentiate on like where they’re clicking within the in the email.
29 00:03:50.460 ⇒ 00:03:55.540 Robert Tseng: like, are we able to do clicks? I mean, obviously, we can associate clicks to the products.
30 00:03:55.830 ⇒ 00:04:03.689 Robert Tseng: then clicks versus opens. I think we get opens like it’s, you know, you open an email. Then you click something. Right? So it’s just I think he’s
31 00:04:03.690 ⇒ 00:04:04.989 Robert Tseng: we just have the.
32 00:04:05.200 ⇒ 00:04:33.589 Awaish Kumar: Like it’s a link clicked right? It’s not all the clicks. It says the we are just making that. So I think that question says that like for the conversion right right now in their Sigma, they have what they do is like email is open. And then within 2 days of that email open. If there is any order, then they select it as a converted email.
33 00:04:33.850 ⇒ 00:04:37.230 Awaish Kumar: So now they want, instead of having
34 00:04:37.550 ⇒ 00:04:43.720 Awaish Kumar: is based on email opened. They want maybe to move forward to email clicked. So if.
35 00:04:43.720 ⇒ 00:04:44.100 Robert Tseng: Right.
36 00:04:44.100 ⇒ 00:04:52.469 Awaish Kumar: Is clicked in the email. And then within 2 days we have a order. Then email is converted so we can. I can easily adjust that in our model.
37 00:04:53.160 ⇒ 00:05:01.199 Robert Tseng: Okay, yeah, that might impact the the model that you built. If that ends up being the definition that they want to move forward with. But I’ll I’ll clarify that
38 00:05:05.580 ⇒ 00:05:10.982 Robert Tseng: cool so great. I think that that model we shared it with cutter and t ground.
39 00:05:11.600 ⇒ 00:05:23.739 Robert Tseng: actually, yeah, I’ll tag them there. So that’s that’s for like email performance, I think we have that data model. Now, we may be adjusting the conversion definitions. But I think other than that we should, that should be done.
40 00:05:24.080 ⇒ 00:05:30.249 Awaish Kumar: And now I did the revenue thing as well. If you want in the future, if you want to, just
41 00:05:30.938 ⇒ 00:05:42.780 Awaish Kumar: like, figure out some other way to associate orders with email, we can update it. But for now we use the same logic which we use for email converted to also get the revenue.
42 00:05:43.800 ⇒ 00:05:44.420 Robert Tseng: Yeah.
43 00:05:47.610 ⇒ 00:05:51.000 Robert Tseng: I think it’s fine for for it to be the same. We can adjust it in the future.
44 00:05:51.620 ⇒ 00:05:58.359 Robert Tseng: They haven’t. They kind of set a different, a better way. So I think I think your logic made sense. We just imitated what was in customer. I/O,
45 00:06:01.649 ⇒ 00:06:11.630 Robert Tseng: okay. Next thing, I’m just going through the messages I sent this live channel today. So yeah, let’s talk about refunds. So so that was asked about
46 00:06:12.850 ⇒ 00:06:21.380 Robert Tseng: yeah, I mean, between Dave a lot, and and Annie I mean, I can pull up that ticket. But we’re talking about this daily report right? So.
47 00:06:23.231 ⇒ 00:06:33.549 Demilade Agboola: Yeah, I think the only question which we have now is, if we need a dashboard for it. But in terms of like the answers to what she needs. We we already have that I know Annie.
48 00:06:34.190 ⇒ 00:06:51.800 Demilade Agboola: In the ticket has sent, like the Csv. To Katie’s request. But we’ve like I I walked through the like the logic of it yesterday with Annie, and so the only question now is, if we need to make a dashboard. I can make a model, and so we can have, like a daily
49 00:06:53.410 ⇒ 00:07:00.270 Demilade Agboola: refund like the like, whatever refunds happened the previous day in a dashboard potentially
50 00:07:00.920 ⇒ 00:07:05.820 Demilade Agboola: But beyond that, like, if we just need the raw data like we can just have the extracts.
51 00:07:06.820 ⇒ 00:07:16.670 Robert Tseng: Yeah. So I mean, I think Annie’s query is just like the raw daily data, right? And we can just maybe we just start with that. And I think if that’s something seems like
52 00:07:16.840 ⇒ 00:07:26.679 Robert Tseng: they want that to be in the in a Daily Refunds channel. So we’ll just start there and then I don’t think I don’t think they know what the dashboard looks like yet. So I don’t want to get stuck in like.
53 00:07:27.220 ⇒ 00:07:38.699 Robert Tseng: sometimes we yeah, like our dashboards just stay in design for for too long. And because we don’t really know what we’re trying to build. And I I’d rather just just do like a daily report of that of that query.
54 00:07:39.590 ⇒ 00:07:43.100 Demilade Agboola: Sure, potentially what might just be easier is if we
55 00:07:43.680 ⇒ 00:07:53.610 Demilade Agboola: have a like dashboard quote, unquote. But dashboard is literally just the table, and so we can set up like the same old tableau rules that we have, and it sounds like a Pdf.
56 00:07:54.340 ⇒ 00:07:54.880 Demilade Agboola: Didn’t.
57 00:07:54.880 ⇒ 00:07:55.200 Robert Tseng: Oh!
58 00:07:55.230 ⇒ 00:08:03.219 Demilade Agboola: That way. We don’t have to like run a query every day and send it like just 1 1 listing off our table of our plates.
59 00:08:03.570 ⇒ 00:08:04.260 Robert Tseng: Okay.
60 00:08:04.530 ⇒ 00:08:11.220 Robert Tseng: wait. Can you just remind me again, do people need to have? I think they need to have tableau access in order to get that Pdf extract.
61 00:08:12.350 ⇒ 00:08:17.779 Demilade Agboola: I mean, I don’t. I don’t think so. Cause like I thought Josh said what he said yesterday. I tried it on my phone.
62 00:08:18.010 ⇒ 00:08:26.653 Demilade Agboola: cause I don’t have. I don’t put like my. I only have my slack just to get messages, but I don’t like have any like confident. I don’t sign in on anything on my phone.
63 00:08:27.200 ⇒ 00:08:29.530 Demilade Agboola: I clicked into.
64 00:08:29.530 ⇒ 00:08:39.950 Josh : It’ll work, it’ll work. It just takes a couple of extra clicks. It would still be better if this is Pdf, yeah, people can access it without tableau access. If they just go through that right process on mobile.
65 00:08:40.630 ⇒ 00:08:46.409 Josh : Okay, you can zoom in on that model in desktop, though.
66 00:08:47.160 ⇒ 00:08:52.570 Josh : So like, if it looks small, it’s really hard to see on slack in desktop.
67 00:08:52.960 ⇒ 00:08:55.630 Josh : So just calling these things out. So you guys know.
68 00:08:57.040 ⇒ 00:08:57.909 Robert Tseng: Let’s see.
69 00:08:58.280 ⇒ 00:09:01.439 Robert Tseng: Yeah, I mean, it’s not ideal. I?
70 00:09:04.080 ⇒ 00:09:30.949 Robert Tseng: Okay? Well, I mean, for now let’s just imitate that. We’ll do the same thing in the Pdf export. I think that makes sense. Yeah. My, my thing is, if we just start. If we share only a tableau report in the refunds channel that I’m gonna get like 5 more people asking me for tableau access that don’t necessarily need it if they’re only looking at one report. So I’d rather just do these Pdf exports for now. And then. Yeah, maybe we should just do a spike to
71 00:09:31.420 ⇒ 00:09:35.810 Robert Tseng: figure out like how we can do this better. I mean, I can take that on. I I have a
72 00:09:36.970 ⇒ 00:09:40.246 Robert Tseng: I’ll do that over the weekend. It’s not urgent.
73 00:09:41.460 ⇒ 00:09:45.931 Robert Tseng: Figure out how to make tableau support. Yes.
74 00:09:46.800 ⇒ 00:09:51.809 Robert Tseng: smoothly. I might even just ask for tableau wrap to see if they have a recommendation, because
75 00:09:52.220 ⇒ 00:09:54.260 Robert Tseng: that could be helpful.
76 00:09:56.140 ⇒ 00:09:59.440 Robert Tseng: Okay, cool.
77 00:09:59.890 ⇒ 00:10:02.650 Robert Tseng: But that is that clear on that, on what we’re gonna do there.
78 00:10:07.380 ⇒ 00:10:17.300 Annie Yu: Yeah. Katie gave me feedback yesterday. I gave her more like a aggregated level of refund amount, and she said she wanted individual
79 00:10:17.929 ⇒ 00:10:24.479 Annie Yu: order row level. So I’ll I’m providing another version to her just to get a confirmation.
80 00:10:24.870 ⇒ 00:10:37.490 Robert Tseng: Okay, cool. Yeah. So then, once we get the confirmation, we can go. We can do the quote unquote dashboard, where it’s just a table and a tableau report. We schedule the Pdf. Into a channel that I will create called Daily Refunds.
81 00:10:40.310 ⇒ 00:10:45.770 Robert Tseng: and I just add the Eden at Brainforge, Email and then we should be able to schedule that right. And then Marty.
82 00:10:46.459 ⇒ 00:10:57.900 Demilade Agboola: Yeah, once you add the Internet brain forge. Well, the only thing I would need like you need to create like a filter. So like, he knows the particular email that comes in to forward to the particular channel. But that’s
83 00:10:59.450 ⇒ 00:11:18.679 Demilade Agboola: that’s different. Like to be fair. I could actually just create the loom. So in just in case maybe I’m out of office, and anyone needs to do it and to be fair, anyone wanted me to show her how to do it. So I guess we could combine that. But yeah, it’s just basically like an email filter role, so that it just goes to the particular email
84 00:11:19.175 ⇒ 00:11:22.930 Demilade Agboola: and it doesn’t send unnecessary things to the wrong place.
85 00:11:23.510 ⇒ 00:11:28.490 Robert Tseng: Okay, yeah, please send that loom to the team. So we can just like, put it in our documentation for.
86 00:11:28.690 ⇒ 00:11:29.500 Robert Tseng: yeah.
87 00:11:32.390 ⇒ 00:11:36.920 Robert Tseng: Okay, then, next thing will be.
88 00:11:37.780 ⇒ 00:11:49.960 Robert Tseng: Yeah. So I just want to confirm on Annie. This is for you, on the on the patience thing. So I I saw the whole like difference between transactions and shipments. But I guess my question was just
89 00:11:50.360 ⇒ 00:12:00.780 Robert Tseng: well, I mean, I’ll I’ll verify, make sure that. But we were saying that these patients. They look like they had valid transactions. But then we don’t actually have real shipments from them
90 00:12:01.970 ⇒ 00:12:09.970 Robert Tseng: like that’s, I mean, this goes back to the whole transaction to shipment delay thing. So I’m sure part of that is, they are valid.
91 00:12:11.850 ⇒ 00:12:29.569 Robert Tseng: patience! And they probably have orders and valid shipments. They just haven’t shown up yet. So it’s I think that’s that’s something I can explain to Jonah. We expect that within 10 to 14 days, or whatever I forgot what the range was that day Malade discovered. That
92 00:12:30.080 ⇒ 00:12:40.349 Robert Tseng: that that will, that any any valid transaction that would have had a real shipment like that data will have already come in within 10 to 14 days.
93 00:12:40.510 ⇒ 00:12:45.650 Robert Tseng: and then anything else that’s not there is not a real, not a real patient.
94 00:12:47.058 ⇒ 00:13:02.399 Annie Yu: But in the example that I provided, I went through all the historical order number of that one specific customer. And I I just can’t find any shipments tied to their order number.
95 00:13:02.540 ⇒ 00:13:14.308 Robert Tseng: Yeah, I mean for that one. It was like an older customer like, yeah. So I would actually just think that I think Rob just gave him wrong data, like I just, I think that that would not be a valid patient, and for us,
96 00:13:15.650 ⇒ 00:13:20.809 Robert Tseng: and that and it. But it does show up in our dim customers model. So that’s kind of misleading, too. Right?
97 00:13:22.100 ⇒ 00:13:26.993 Robert Tseng: So I think that’s kind of like, maybe this is a follow up for us.
98 00:13:29.830 ⇒ 00:13:32.190 Robert Tseng: wait Aish! Did you make dim customers.
99 00:13:34.954 ⇒ 00:13:35.389 Awaish Kumar: Yep.
100 00:13:35.820 ⇒ 00:13:36.530 Robert Tseng: Okay.
101 00:13:37.822 ⇒ 00:13:41.810 Robert Tseng: Yeah, I’ll I’ll connect with the wish on this. So we’ll do
102 00:13:46.900 ⇒ 00:13:54.639 Annie Yu: So are you saying that it makes sense that they show up, in fact, transactions, but they shouldn’t be showing up in them customers.
103 00:13:54.640 ⇒ 00:14:00.914 Robert Tseng: I don’t think they should show up in them customers unless we’re 100. Sure that there’s a valid, you know. Shipment
104 00:14:02.900 ⇒ 00:14:03.470 Annie Yu: Okay.
105 00:14:04.360 ⇒ 00:14:09.204 Robert Tseng: Sorry that probably was too strong. I don’t think that matches my level of certainty, I think.
106 00:14:11.490 ⇒ 00:14:17.564 Robert Tseng: I just think we should be more conservative with who we put into dim customers.
107 00:14:19.100 ⇒ 00:14:27.270 Robert Tseng: that said, like, I am getting requests from other teams trying to do some retargeting on
108 00:14:27.370 ⇒ 00:14:56.079 Robert Tseng: patients that haven’t even placed an order with us yet, like if they’ve only filled out the the intake form partially, and then they dropped out. They’re trying to retarget them. I have some issues with that. I’m not really sure if that’s compliant, or whatever. So I need to go figure out like where that request is. Who authorized that request. But anyway, I think some of this, like what’s a valid patient to retarget customer like? I think this to me is all the same a similar theme. So I I think
109 00:14:56.330 ⇒ 00:15:03.410 Robert Tseng: I don’t think I can give you. I can give a clear answer on this right. In this moment I I need. I need to talk to a couple of people to figure it out.
110 00:15:04.430 ⇒ 00:15:04.990 Annie Yu: Yeah.
111 00:15:05.360 ⇒ 00:15:17.569 Robert Tseng: Okay, address, who actually belongs in customers versus and we can actually
112 00:15:18.320 ⇒ 00:15:25.411 Robert Tseng: alright. So I think that’s on me. You guys are blocked. I need to have. I need to be super clear on that.
113 00:15:33.610 ⇒ 00:15:34.920 Robert Tseng: okay.
114 00:15:39.010 ⇒ 00:15:39.940 Robert Tseng: Great.
115 00:15:40.100 ⇒ 00:15:41.430 Robert Tseng: Then.
116 00:15:41.930 ⇒ 00:15:49.339 Robert Tseng: Yeah. Do we want to talk about the sorry I’m moving on, but like any other questions on that or we that that’s clear. Right?
117 00:15:51.755 ⇒ 00:15:52.240 Annie Yu: No.
118 00:15:52.710 ⇒ 00:15:56.029 Robert Tseng: Yeah, okay, so, moving on to the
119 00:15:56.160 ⇒ 00:16:13.810 Robert Tseng: the issue with Ltv being too low compared to what they think they’ve worked with. Yeah, I mean, I understand that. You know, Joanna’s been using the outdated looker studio model that we don’t maintain. Yada. Yadda. So yeah, is it? I think our what you’re saying, Annie, is just that we need to
120 00:16:15.400 ⇒ 00:16:19.759 Robert Tseng: Well, tell her that like, she’s just been using wrong data. So I think
121 00:16:20.090 ⇒ 00:16:24.709 Robert Tseng: if it’s if it’s just like an education thing versus like.
122 00:16:25.070 ⇒ 00:16:28.900 Robert Tseng: are we actually making any model changes? I wasn’t clear from following that thread.
123 00:16:33.200 ⇒ 00:16:34.620 Annie Yu: Yeah. And I, just
124 00:16:35.030 ⇒ 00:16:42.880 Annie Yu: what I know is that the one that they used was connected to another data source which I’m not sure
125 00:16:43.330 ⇒ 00:16:45.520 Annie Yu: how that was structured.
126 00:16:46.390 ⇒ 00:16:51.689 Awaish Kumar: They were kind of using a table that was created when Bo was here.
127 00:16:52.405 ⇒ 00:16:57.909 Awaish Kumar: and he write out some queries and give it to me to like, have him have a table?
128 00:16:59.520 ⇒ 00:17:17.509 Awaish Kumar: but like we have some data which is coming from product sales summary. That should be good. But the data for Stv is coming from the other table, which is based on legacy models and and that is, including all the orders and in our product sales summary we, and in our like new models.
129 00:17:17.690 ⇒ 00:17:22.909 Awaish Kumar: like up to up till 5 transactions we have all the orders.
130 00:17:23.050 ⇒ 00:17:28.340 Awaish Kumar: and we only exclude the orders with status canceled and error
131 00:17:28.790 ⇒ 00:17:32.169 Awaish Kumar: right? But after that, when we move to summary models
132 00:17:32.665 ⇒ 00:17:38.299 Awaish Kumar: like product sales, summary, or any other summary model, we also filter abandoned orders.
133 00:17:39.100 ⇒ 00:17:45.580 Robert Tseng: Sure. But we didn’t update the Roas and Ltv dashboard after we, you know, moved to summary tables.
134 00:17:47.020 ⇒ 00:17:50.599 Annie Yu: Not yet, not that. The plan is to
135 00:17:51.090 ⇒ 00:17:56.920 Annie Yu: replace the bottom part with the newer Ltb key map table. We but we haven’t.
136 00:17:57.210 ⇒ 00:18:03.610 Robert Tseng: Okay? So yeah, they’re just like anchored to their previous understanding. That’s on models we haven’t updated. So
137 00:18:05.750 ⇒ 00:18:17.939 Robert Tseng: yeah, I mean, I guess a wish. If we can just get clear on the we. I understand that we’re we. We’re not including abandoned orders. That’s 1 big difference. If there are any other differences that we call out be like
138 00:18:19.210 ⇒ 00:18:25.560 Robert Tseng: what you were seeing was previously inflated because it was including abandoned orders. And whatever
139 00:18:26.230 ⇒ 00:18:31.300 Robert Tseng: we’ve taken that out of the way that we’re doing Ltv calculations moving forward. So you need to.
140 00:18:31.300 ⇒ 00:18:35.490 Josh : How would abandon orders, impact Ltv and Cac.
141 00:18:39.100 ⇒ 00:18:41.520 Robert Tseng: It would impact Cac.
142 00:18:41.520 ⇒ 00:18:43.790 Josh : Back, but not Ltv.
143 00:18:46.790 ⇒ 00:18:49.820 Robert Tseng: Well, Ltv would impact because.
144 00:18:49.820 ⇒ 00:18:51.590 Josh : It would impact the ratio. Maybe.
145 00:18:51.590 ⇒ 00:18:52.330 Robert Tseng: Yeah.
146 00:18:52.970 ⇒ 00:18:59.310 Josh : But not the actual Ltv. And I think her concern is about on the Ltv. Because that is a big difference.
147 00:19:00.260 ⇒ 00:19:08.959 Josh : like going from like 1,100 to 600. That just basically is saying how you’re only getting 2 renewals or 2 renewals which doesn’t make a lot of sense.
148 00:19:10.070 ⇒ 00:19:16.610 Josh : because traditionally, they’ve been seeing like people. I mean, you just do the regular math. It’s like, Hey, we have. How many renewals
149 00:19:17.050 ⇒ 00:19:25.029 Josh : we’re getting 6 renewals or 5 renewals. That product costs 2 96. I mean, you could do the math. It’s like 1,100 bucks.
150 00:19:29.370 ⇒ 00:19:33.740 Josh : So something just not passing the general sniff test is what I think she’s saying.
151 00:19:33.970 ⇒ 00:19:34.650 Robert Tseng: Yeah.
152 00:19:35.240 ⇒ 00:19:41.920 Annie Yu: Yeah. But she also mentioned that injectable Sema looks right, but not Sirmoralin. So I think.
153 00:19:41.920 ⇒ 00:19:42.840 Robert Tseng: More than it was.
154 00:19:42.840 ⇒ 00:19:43.829 Robert Tseng: She’s blessed. Yeah.
155 00:19:44.150 ⇒ 00:19:52.129 Annie Yu: Yeah, I think, she was saying, she’ll discuss with Cutter just because they might never had a solid number for some more.
156 00:19:52.340 ⇒ 00:19:59.239 Josh : Correct. Correct. I’m just saying like, just, generally speaking, though, it makes a lot more sense for us to just. Hey, how many renewals
157 00:19:59.480 ⇒ 00:20:03.509 Josh : are we seeing out of that general patient base? Are we seeing like
158 00:20:03.750 ⇒ 00:20:22.669 Josh : 2? Are we seeing 2 and a half? We’re seeing 4 for these monthly folks because that we can just then do a spot check to say, Okay, yeah. Then, then the numbers of the Ltv actually will look right or look wrong. So I think like, that’s how you can prove it right. So, generally. Broadly speaking.
159 00:20:23.350 ⇒ 00:20:28.997 Josh : when I blend these numbers together, I’m seeing that we had a thousand people. Of the 1,000 people. We had
160 00:20:29.650 ⇒ 00:20:47.210 Josh : 3,600 orders. That’s 3.6 orders per patient. So 3.6 orders per patient would roughly equal this much as an Ltv. Like that’s probably like a back, a napkin way to do it. But then there’s obviously some nuance we’re like, you know, we’ll have, like some patients that do like 5 orders and a bunch of patients do one order.
161 00:20:47.530 ⇒ 00:20:51.049 Josh : And then the value of those things is very different, too, right
162 00:20:52.420 ⇒ 00:21:01.900 Josh : because of like couponing and discounts and that kind of stuff. So that’s all I’m saying. I’m just saying like, Hey, when we’re going through it just like, give people a financial walk to help people understand.
163 00:21:02.330 ⇒ 00:21:05.170 Josh : You know how the how you guys are arriving at the number.
164 00:21:05.590 ⇒ 00:21:06.240 Robert Tseng: Okay.
165 00:21:07.260 ⇒ 00:21:18.309 Robert Tseng: yeah. And Annie, maybe we’ll stay. I mean, I’ll maybe I’ll grab time with you after this, like I I think I wanna I think it’s just a bit of storytelling that we need to do here. Yeah, kind of following that order of
166 00:21:19.160 ⇒ 00:21:46.510 Robert Tseng: yeah, top down patience, like recurring orders for across patients generally regardless of product. And then we can kind of anchor people to well, this is what it is for summer products. And then we could, that that’ll be a base. I mean, that’s that’s how people think about, anyway. So there’s a baseline there and then we can, when we can go into the then we can do this tomorrow in one. So I think we just have to have 3 levels to how we’re talking about this. So people can better understand it.
167 00:21:47.260 ⇒ 00:21:51.809 Annie Yu: Yeah, yeah, I I would just I would love support there. Cause I I, yeah.
168 00:21:52.100 ⇒ 00:21:54.840 Robert Tseng: Okay, got it?
169 00:21:54.840 ⇒ 00:21:57.899 Annie Yu: And until then we shouldn’t swap out.
170 00:21:58.240 ⇒ 00:22:02.960 Robert Tseng: Yeah, yeah, let’s not. No, no. Urgent to swap it out yet. Yeah.
171 00:22:04.640 ⇒ 00:22:05.480 Robert Tseng: Okay.
172 00:22:06.730 ⇒ 00:22:13.529 Robert Tseng: I don’t think Mattesh and and Cutter gave you feedback on? I guess they asked a couple of things.
173 00:22:15.330 ⇒ 00:22:17.660 Robert Tseng: About the about, the new
174 00:22:19.318 ⇒ 00:22:30.799 Robert Tseng: I would, where is it there are links
175 00:22:34.270 ⇒ 00:22:45.679 Robert Tseng: okay, yeah. So well, I mean, this is kind of related to the the heat map charts. Right? The 2 the 2 follow ups are integrating cross cell into Ltv, so
176 00:22:46.080 ⇒ 00:22:50.240 Robert Tseng: I mean, we just have to talk about what?
177 00:22:55.910 ⇒ 00:22:57.120 Robert Tseng: Sorry there’s a beep.
178 00:22:59.960 ⇒ 00:23:03.454 Robert Tseng: I’m not actually sure what he means by that, to be honest. So maybe I have to.
179 00:23:04.036 ⇒ 00:23:05.540 Annie Yu: Are you screen sharing.
180 00:23:05.900 ⇒ 00:23:06.740 Robert Tseng: I’m not.
181 00:23:06.740 ⇒ 00:23:07.570 Annie Yu: Oh, okay.
182 00:23:08.140 ⇒ 00:23:09.520 Robert Tseng: Oh, sorry I should be
183 00:23:15.580 ⇒ 00:23:23.700 Robert Tseng: right. So I kind of cross posted this, and then there’s a couple of pieces of feedback here that we need to kind of just figure out how we’re going to integrate and do
184 00:23:24.160 ⇒ 00:23:25.500 Robert Tseng: the revision.
185 00:23:25.760 ⇒ 00:23:32.900 Robert Tseng: So I mean, I I’ll I’ll clarify with him by what he means by this? I think, yeah.
186 00:23:34.090 ⇒ 00:23:36.760 Annie Yu: I think what we are. Working towards
187 00:23:37.300 ⇒ 00:23:41.460 Annie Yu: on that product. Switching is kind of similar to this one.
188 00:23:41.986 ⇒ 00:23:50.060 Annie Yu: Trying to figure out like what people bought before, and then what product they switch to. But there, there’s also like nuances.
189 00:23:50.713 ⇒ 00:24:02.729 Annie Yu: That I put in the ticket for the milati that we’ll need his help to figure out, because if someone’s just like having multiple plans, then we don’t want to flag them as like switching products.
190 00:24:03.540 ⇒ 00:24:09.240 Annie Yu: And I don’t know how how many cases there are, but edge cases like that.
191 00:24:12.000 ⇒ 00:24:12.949 Robert Tseng: Let’s see.
192 00:24:13.430 ⇒ 00:24:15.599 Josh : You talking about for the crossover report.
193 00:24:17.248 ⇒ 00:24:27.299 Annie Yu: This is more like products switching. But this one with this one we would be able to figure out people 1st bought what? And then, if they.
194 00:24:27.300 ⇒ 00:24:27.790 Josh : Yeah.
195 00:24:27.790 ⇒ 00:24:28.310 Annie Yu: Something else.
196 00:24:28.590 ⇒ 00:24:38.349 Josh : Got it to the cross. All report. Yeah, I mean, you’re gonna have to use dates. And you’re gonna have to use like dating for this. This is how I bought the one that I built before.
197 00:24:42.740 ⇒ 00:24:50.470 Robert Tseng: Yeah. But I guess if we just use dates, then that doesn’t. I mean, there could be people on multiple plans as well right as I. I guess that’s what she’s bringing. So
198 00:24:51.380 ⇒ 00:24:54.854 Robert Tseng: we have to like. Figure all the scenarios for, like what?
199 00:24:55.620 ⇒ 00:24:56.779 Robert Tseng: What that looks like.
200 00:24:59.950 ⇒ 00:25:04.049 Josh : Yeah, I think that the ask, though, is to help us help us understand like, Hey.
201 00:25:04.400 ⇒ 00:25:12.150 Josh : in order to better cross sell to people in the future. What are people naturally inclining themselves to to cross over into?
202 00:25:12.310 ⇒ 00:25:18.879 Josh : So it’s like, Hey, if someone comes in and they buy, let’s call it nad skin cream to start
203 00:25:19.190 ⇒ 00:25:36.720 Josh : what is the most common second purchase and 3rd purchase that they’ll make. And then the team can start building out all the email flows and the follow ups and the retargeting strategies around what we get the data on. So I think that’s a high level thing, right? And like, yeah, there can be. There can be a lot of times where people like come in, and
204 00:25:36.840 ⇒ 00:25:51.509 Josh : maybe they purchase something first, st then second, and 3, rd then 4th and 5, th like, I’m sure there’s a lot of scenarios for you guys. But I mean at the high level, like, we just care about the simple side of things of like, hey, whatever their 1st product is, what is the most likely second 3rd product that they are gonna buy.
205 00:25:54.950 ⇒ 00:25:59.731 Robert Tseng: Okay, yeah. I mean, I think this sample table that you put up
206 00:26:00.190 ⇒ 00:26:03.559 Robert Tseng: any kind of helps us get there. But yeah, I think we just
207 00:26:04.070 ⇒ 00:26:07.059 Robert Tseng: I mean, yeah, we’re just gonna be using dates to to figure that out. So.
208 00:26:07.060 ⇒ 00:26:15.080 Annie Yu: With the one I sketch out, though. I don’t have a clear idea on how we would, how we would present the
209 00:26:15.500 ⇒ 00:26:20.630 Annie Yu: like first, st second, 3, rd 4, th like the whole journey.
210 00:26:21.050 ⇒ 00:26:23.430 Annie Yu: This is more like where we can see.
211 00:26:23.430 ⇒ 00:26:27.480 Josh : Heat mapping is great. Honestly, heat mapping is good.
212 00:26:29.920 ⇒ 00:26:34.920 Demilade Agboola: I mean, can I? Can we like schedule time? So we can walk through this and just like so.
213 00:26:34.920 ⇒ 00:26:39.920 Annie Yu: Yeah, yeah, cause this might not be the most ideal granularity. If
214 00:26:42.580 ⇒ 00:26:44.590 Annie Yu: with the with the goal in mind.
215 00:26:46.260 ⇒ 00:26:49.409 Demilade Agboola: Okay, sure. Do you have time today?
216 00:26:52.166 ⇒ 00:26:58.563 Annie Yu: If you want to stay on after this call, I I do have time. I’m meeting Sarah
217 00:26:59.270 ⇒ 00:27:02.140 Annie Yu: After, like 30 min after.
218 00:27:02.810 ⇒ 00:27:04.020 Demilade Agboola: Okay, that’s fine.
219 00:27:04.020 ⇒ 00:27:21.919 Robert Tseng: Yeah. And when you meet Sarah, can you record the video? I’m sure that she will not be the only one on her team to ask for that. It’s just like a tableau. Intro walkthrough, right? So we could. Just yeah, if you’re using zoom, just make sure our recordings on. So I can. We can just share it, and I’ll tell.
220 00:27:22.321 ⇒ 00:27:30.350 Annie Yu: Is it more focused on the tableau navigation overall or or just that that dashboard, that she will be using.
221 00:27:30.570 ⇒ 00:27:51.870 Robert Tseng: Well, I think. Just assume that she’s never I I know she’s never logged in before. So start from the beginning, too, and it’s just like you’re logging the tableau here all the pharmacy related. Farm Ops related reports. Let’s look at this one particular dashboard, and then, just like talking through her with her like how to answer her question just by using the filters on the dashboard, because I feel like
222 00:27:52.090 ⇒ 00:27:55.549 Robert Tseng: many of the requests coming from their team.
223 00:27:55.690 ⇒ 00:28:01.320 Robert Tseng: or just because people don’t know how to like, answer their question by using the dashboards.
224 00:28:02.490 ⇒ 00:28:03.260 Annie Yu: Okay.
225 00:28:03.260 ⇒ 00:28:07.370 Robert Tseng: Yeah, so that would be great.
226 00:28:10.180 ⇒ 00:28:12.909 Robert Tseng: Yeah, I think.
227 00:28:13.150 ⇒ 00:28:20.869 Robert Tseng: Yeah, I know we’re coming up any other urgent things. I think those are the highest important things that are in flight.
228 00:28:22.590 ⇒ 00:28:30.210 Robert Tseng: right now, there’s more tickets coming in. But like I I just, I’m not gonna talk about them until we, we close these ones out.
229 00:28:31.170 ⇒ 00:28:33.610 Josh : Oh, good job guys, I gotta drop. Thanks.
230 00:28:33.610 ⇒ 00:28:35.090 Robert Tseng: Cool thanks. Josh.
231 00:28:41.600 ⇒ 00:28:44.960 Annie Yu: This is also for the product. Drill down, Robert.
232 00:28:45.220 ⇒ 00:28:45.780 Robert Tseng: Yep.
233 00:28:45.780 ⇒ 00:28:51.310 Annie Yu: We do have a cross sales section right now. That’s not
234 00:28:51.800 ⇒ 00:28:57.370 Annie Yu: directional. So it just means like, in the selected time frame we track.
235 00:28:57.690 ⇒ 00:29:03.740 Annie Yu: How many share customers that bought both product pair.
236 00:29:04.170 ⇒ 00:29:05.090 Robert Tseng: No?
237 00:29:08.810 ⇒ 00:29:12.719 Robert Tseng: Okay, if it’s on the loom video, I’ll watch it. Sorry I didn’t watch this yet.
238 00:29:14.530 ⇒ 00:29:17.179 Annie Yu: Yeah. Yeah. But this one, we we can’t
239 00:29:17.626 ⇒ 00:29:24.479 Annie Yu: track, like, if it’s a 1st order to the second or so, it just means like in that selected time, timeframe.
240 00:29:24.660 ⇒ 00:29:34.676 Robert Tseng: Okay, yeah, I know you and are gonna talk about it right afterwards. I’ll read this. I mean, I’ll watch this, and then you just catch me up and we’ll we’ll try to get to an answer there.
241 00:29:35.300 ⇒ 00:29:43.730 Robert Tseng: but yeah, I think I understand what you’re saying. I mean Josh’s point on heat maps. I think he was pretty much talking something like this, but that may not actually be ideal. So
242 00:29:44.940 ⇒ 00:29:58.189 Robert Tseng: because this doesn’t tell you order this just tells you, how many injectable semi customers are also going, you know, to purchase these other products doesn’t really tell you who’s like, what’s the second product, or the 3rd product, or whatever.
243 00:29:58.190 ⇒ 00:30:01.979 Annie Yu: Yeah, yeah. But this one can potentially help like, bundle or.
244 00:30:01.980 ⇒ 00:30:08.070 Robert Tseng: Yeah, that would help bundling. Yeah. Still, good. Still, a good view. But yeah, it doesn’t necessarily give us the order.
245 00:30:09.157 ⇒ 00:30:23.520 Robert Tseng: Okay, cool. Yeah. Thanks. Team. I know that some volume is picking up. There’s a couple of legacy things from Rob that are shifting over to us. Things I did not know even existed. So we’re
246 00:30:23.710 ⇒ 00:30:37.102 Robert Tseng: stretching a bit over the next week. 2 weeks. But then we’re gonna yeah, I mean, I think I’m only gonna give him a week. Then I’m gonna say, tell Josh like he needs to bump us, bump our budget. Since everybody’s spending more time.
247 00:30:37.850 ⇒ 00:30:39.839 Robert Tseng: okay, anyway. Thanks. Guys.
248 00:30:43.450 ⇒ 00:30:44.140 Awaish Kumar: You go.
249 00:30:44.500 ⇒ 00:30:46.249 Robert Tseng: Got it. I got a job. Yep. See ya.