Meeting Title: Finance Forecasting Date: 2025-12-08 Meeting participants: Sezim Zhenishbekova, Henry Zhao, Jonah
WEBVTT
1 00:01:45.440 ⇒ 00:01:46.550 Henry Zhao: Thanks, Asm.
2 00:01:46.790 ⇒ 00:01:48.569 Sezim Zhenishbekova: Hi, Henry, how are you?
3 00:01:48.820 ⇒ 00:01:53.849 Henry Zhao: I feel like us and, Robert are the ones that follow the Zoom rules of, like, camera on and stuff like that.
4 00:01:53.850 ⇒ 00:01:55.530 Sezim Zhenishbekova: Yeah.
5 00:01:55.900 ⇒ 00:02:04.720 Sezim Zhenishbekova: I think I don’t really completely like working remotely, but I think at least it is a good chance to see and come to meet with teammates, I guess.
6 00:02:05.360 ⇒ 00:02:09.049 Henry Zhao: But I saw Ember, I had one-on-one with her.
7 00:02:09.050 ⇒ 00:02:11.990 Sezim Zhenishbekova: So, I had a chance to see her.
8 00:02:12.410 ⇒ 00:02:13.180 Henry Zhao: Oh, yeah.
9 00:02:14.430 ⇒ 00:02:19.360 Sezim Zhenishbekova: So basically, for today’s call’s agenda, you’re gonna… how is the flow gonna go?
10 00:02:19.600 ⇒ 00:02:36.289 Henry Zhao: Yeah, just go over the spreadsheet that we looked at last time, take notes on each of the… so I already changed the last column from your notes to his notes, like, notes from our meeting today. And then we’ll see if there’s anything we need to add, and then we’ll divvy up the work responsibilities, and create tickets for everything.
11 00:02:36.290 ⇒ 00:02:45.249 Sezim Zhenishbekova: Yes. So I basically finished with Insomnia last week, so this week I’m gonna be completely… be onboarding on it, and then I can take more tasks on.
12 00:02:45.250 ⇒ 00:02:48.820 Henry Zhao: Perfect. Yeah, I’m looking forward to this. This will be a big project, and I think we’ll be good.
13 00:02:48.820 ⇒ 00:02:52.830 Sezim Zhenishbekova: It’s more interesting, too, there’s so much data to work with.
14 00:02:53.100 ⇒ 00:02:53.680 Henry Zhao: John.
15 00:02:53.970 ⇒ 00:02:58.439 Henry Zhao: One thing I’m doing right now is I’m literally just looking at by pharmacy and by product.
16 00:02:58.650 ⇒ 00:02:59.460 Sezim Zhenishbekova: Like…
17 00:02:59.460 ⇒ 00:03:02.750 Henry Zhao: kind of the… The money involved.
18 00:03:02.950 ⇒ 00:03:07.230 Henry Zhao: The cogs per order, and, like, how many orders, and when they’re shipping out.
19 00:03:07.830 ⇒ 00:03:10.129 Henry Zhao: data here that we can start looking at.
20 00:03:10.640 ⇒ 00:03:11.480 Sezim Zhenishbekova: Cool.
21 00:03:12.120 ⇒ 00:03:13.410 Henry Zhao: Especially on cogs.
22 00:03:13.910 ⇒ 00:03:18.020 Henry Zhao: CODS is gonna be a big focus on how do we, like, make CODS better.
23 00:03:25.880 ⇒ 00:03:27.890 Henry Zhao: Okay, we’ll wait on Joe now, let’s see…
24 00:03:29.200 ⇒ 00:03:30.390 Henry Zhao: Still training.
25 00:03:37.420 ⇒ 00:03:44.039 Sezim Zhenishbekova: And how often such meetings are conducted with their team? Usually once in two weeks, or once in a week?
26 00:03:44.040 ⇒ 00:03:46.969 Henry Zhao: Yeah, once every two weeks, depends on what meeting.
27 00:03:48.700 ⇒ 00:03:51.279 Sezim Zhenishbekova: And depending on their requests as well, right?
28 00:03:53.300 ⇒ 00:03:54.449 Henry Zhao: Sorry, what was the question?
29 00:03:54.620 ⇒ 00:04:01.640 Sezim Zhenishbekova: Oh, and then they usually have their own client requests, and we have also our own recommendations that we usually provide to them.
30 00:04:01.890 ⇒ 00:04:02.980 Henry Zhao: Yeah.
31 00:04:04.800 ⇒ 00:04:08.140 Henry Zhao: Yeah, I think Robert just wants to make sure we’re not losing steam, like, at the end of the year.
32 00:04:08.780 ⇒ 00:04:12.070 Sezim Zhenishbekova: Yeah, it’s hard to not do that.
33 00:04:12.070 ⇒ 00:04:12.790 Henry Zhao: Yeah.
34 00:04:12.790 ⇒ 00:04:13.220 Sezim Zhenishbekova: Yeah.
35 00:04:13.220 ⇒ 00:04:18.140 Henry Zhao: At least my focus this week is to, like, provide stuff that can inform them on their, like, strategy planning for 2027.
36 00:04:19.070 ⇒ 00:04:21.359 Henry Zhao: That’s kind of what I’m trying to finish up this week.
37 00:04:21.360 ⇒ 00:04:24.399 Sezim Zhenishbekova: Yeah, I will be down. I think that’s a great idea.
38 00:04:25.610 ⇒ 00:04:26.300 Sezim Zhenishbekova: Yeah.
39 00:04:27.100 ⇒ 00:04:38.510 Henry Zhao: Because they’ll probably want to know, like, where are opportunities to, like, improve their operations? Like, I was going to see, like, are there too much, like, being bunched into one pharmacy? So, like, this Boothwin one, right, it’s like…
40 00:04:38.600 ⇒ 00:04:42.569 Sezim Zhenishbekova: The order was high, but that was good because the cogs went down per order.
41 00:04:43.960 ⇒ 00:04:46.390 Henry Zhao: But I also don’t know if the COGS is that accurate.
42 00:04:47.610 ⇒ 00:04:50.160 Henry Zhao: We gotta ask them for accurate COGS data.
43 00:04:50.590 ⇒ 00:04:54.280 Sezim Zhenishbekova: And then all this data was provided by them, right?
44 00:04:54.900 ⇒ 00:04:56.939 Henry Zhao: No, this I got from our data warehouse.
45 00:04:56.940 ⇒ 00:04:57.830 Sezim Zhenishbekova: Oh, okay.
46 00:04:57.830 ⇒ 00:05:01.760 Henry Zhao: This Curexa, I feel a lot better, because, like, the cogs is very, very consistent.
47 00:05:02.310 ⇒ 00:05:03.250 Sezim Zhenishbekova: Damn.
48 00:05:03.250 ⇒ 00:05:04.020 Henry Zhao: This one is very cool.
49 00:05:04.360 ⇒ 00:05:06.490 Henry Zhao: This one’s all $216.
50 00:05:06.840 ⇒ 00:05:09.719 Henry Zhao: I don’t know what happened here, why it went down, but…
51 00:05:11.200 ⇒ 00:05:11.900 Sezim Zhenishbekova: Hmm.
52 00:05:12.190 ⇒ 00:05:13.190 Sezim Zhenishbekova: Interesting.
53 00:05:13.510 ⇒ 00:05:16.440 Henry Zhao: No, Pharmacy Hub was just, like, for 2 months to kind of, like, fill a gap.
54 00:05:18.800 ⇒ 00:05:21.190 Sezim Zhenishbekova: What are the dates there?
55 00:05:21.700 ⇒ 00:05:22.720 Henry Zhao: The order dates.
56 00:05:24.280 ⇒ 00:05:30.990 Sezim Zhenishbekova: I mean, the, like, the months of orders, why it went down? Yeah. What happened in that month?
57 00:05:31.920 ⇒ 00:05:32.880 Henry Zhao: I don’t know.
58 00:05:33.200 ⇒ 00:05:35.569 Henry Zhao: Oh, no. Not grouped product name.
59 00:05:38.660 ⇒ 00:05:40.850 Henry Zhao: I’m gonna actually make that a DE task.
60 00:05:41.860 ⇒ 00:05:43.569 Henry Zhao: We’re looking for DE work, right?
61 00:05:43.750 ⇒ 00:05:46.870 Henry Zhao: It’s order summary… their standardized product name?
62 00:05:50.630 ⇒ 00:05:52.260 Henry Zhao: Maybe it’s marketing product name.
63 00:05:56.690 ⇒ 00:06:00.940 Sezim Zhenishbekova: I wonder if I have access to Google Cloud, local.
64 00:06:01.710 ⇒ 00:06:02.900 Henry Zhao: I can give you access.
65 00:06:03.510 ⇒ 00:06:05.399 Henry Zhao: I think this is better.
66 00:06:07.950 ⇒ 00:06:08.710 Sezim Zhenishbekova: Oh, please.
67 00:06:15.590 ⇒ 00:06:18.380 Henry Zhao: Yeah, much better Okay.
68 00:06:32.910 ⇒ 00:06:36.039 Sezim Zhenishbekova: Where are you planning to celebrate Christmas this year?
69 00:06:36.760 ⇒ 00:06:38.390 Sezim Zhenishbekova: Or New Year, I don’t know what…
70 00:06:38.390 ⇒ 00:06:40.509 Henry Zhao: New York. New York City.
71 00:06:40.510 ⇒ 00:06:45.229 Sezim Zhenishbekova: You’re gonna be in New York! Oh, we should… we should catch up, or…
72 00:06:45.230 ⇒ 00:06:45.990 Henry Zhao: Yeah, we somehow…
73 00:06:45.990 ⇒ 00:06:48.330 Sezim Zhenishbekova: Meet for a coffee, or work together.
74 00:06:48.330 ⇒ 00:06:53.620 Henry Zhao: Yeah, go to WeWork, that would be great. Usually, Tom expenses it, too. Like, we can expense the WeWork.
75 00:06:53.800 ⇒ 00:06:54.810 Sezim Zhenishbekova: Yay.
76 00:06:54.990 ⇒ 00:07:00.030 Sezim Zhenishbekova: I’ve been in a couple of them, in Grand Central and one in Harlem.
77 00:07:00.300 ⇒ 00:07:02.510 Sezim Zhenishbekova: I liked them, they were pretty good.
78 00:07:02.510 ⇒ 00:07:03.430 Henry Zhao: Yeah, me too.
79 00:07:04.180 ⇒ 00:07:06.890 Sezim Zhenishbekova: In which area are you gonna stay in for how long?
80 00:07:08.220 ⇒ 00:07:10.139 Henry Zhao: I’m being Upper East Sign for 10 days.
81 00:07:10.140 ⇒ 00:07:14.539 Sezim Zhenishbekova: Oh, okay, that’s good. I’m on the Upper West Side.
82 00:07:15.090 ⇒ 00:07:15.530 Henry Zhao: Oh, nice.
83 00:07:16.260 ⇒ 00:07:17.039 Henry Zhao: of the park.
84 00:07:17.300 ⇒ 00:07:21.980 Sezim Zhenishbekova: Yeah, yeah, yeah. And then, which dates are you planning to come over?
85 00:07:22.630 ⇒ 00:07:28.279 Henry Zhao: The 18th is when I arrive, and I think, Robert leaves the 18th, right?
86 00:07:28.280 ⇒ 00:07:33.820 Sezim Zhenishbekova: Yeah, yeah, he said it. Okay, I’m leaving on the 24th, so we have 6 days, 5 days.
87 00:07:33.820 ⇒ 00:07:35.169 Henry Zhao: Where are you going on the 24th?
88 00:07:35.400 ⇒ 00:07:45.809 Sezim Zhenishbekova: I’m traveling to Michigan. Oh, right. Yeah, when I was an exchange student, I was an exchange student in my high school years, so I still kept in touch, just gonna see them.
89 00:07:45.960 ⇒ 00:07:48.660 Sezim Zhenishbekova: And gonna celebrate New Year there, too.
90 00:07:51.510 ⇒ 00:07:53.979 Henry Zhao: Sounds good. Okay, I just pinged Jonah to see if he’s coming.
91 00:07:54.570 ⇒ 00:07:55.150 Sezim Zhenishbekova: Okay.
92 00:07:56.180 ⇒ 00:07:57.339 Henry Zhao: Yeah, I like Michigan.
93 00:07:58.110 ⇒ 00:07:58.960 Sezim Zhenishbekova: Hmm.
94 00:07:58.960 ⇒ 00:08:02.290 Henry Zhao: My school in football is gonna be playing Michigan.
95 00:08:02.470 ⇒ 00:08:04.190 Henry Zhao: University of Michigan.
96 00:08:04.680 ⇒ 00:08:08.349 Sezim Zhenishbekova: Mmm Oh, what, what team?
97 00:08:08.500 ⇒ 00:08:09.620 Sezim Zhenishbekova: What’s port?
98 00:08:09.620 ⇒ 00:08:11.529 Henry Zhao: Texas in football.
99 00:08:12.050 ⇒ 00:08:14.630 Sezim Zhenishbekova: Wait, there… right now, it’s like…
100 00:08:14.870 ⇒ 00:08:17.349 Sezim Zhenishbekova: a lot… the brackets, right? The brackets?
101 00:08:17.350 ⇒ 00:08:17.820 Henry Zhao: Yeah.
102 00:08:17.820 ⇒ 00:08:18.590 Sezim Zhenishbekova: I mean…
103 00:08:19.000 ⇒ 00:08:19.550 Henry Zhao: Cool.
104 00:08:19.900 ⇒ 00:08:27.430 Sezim Zhenishbekova: I’m… my other… like, so I’m on half-time on, Brain Forge, and I have another half-time job where I do
105 00:08:27.750 ⇒ 00:08:29.220 Sezim Zhenishbekova: Sports Analytics.
106 00:08:29.440 ⇒ 00:08:35.270 Sezim Zhenishbekova: And… We are launching a competition, sports prediction competition.
107 00:08:35.669 ⇒ 00:08:42.699 Sezim Zhenishbekova: I’ll send you a cargo link if you’re interested, and we’re actually… Trying to, like…
108 00:08:42.809 ⇒ 00:08:47.769 Sezim Zhenishbekova: Literate predictors more, instead of use padding, try to analyze data.
109 00:08:48.359 ⇒ 00:08:50.959 Sezim Zhenishbekova: And we’re… we launched it on Kaggle.
110 00:08:51.789 ⇒ 00:08:52.439 Sezim Zhenishbekova: Yeah.
111 00:08:52.440 ⇒ 00:08:53.749 Henry Zhao: On what? Sorry, on what?
112 00:08:53.750 ⇒ 00:08:55.990 Sezim Zhenishbekova: Coggle? Have you used Coggle before?
113 00:08:55.990 ⇒ 00:08:56.670 Henry Zhao: No, I haven’t.
114 00:08:56.670 ⇒ 00:09:09.410 Sezim Zhenishbekova: Kaggle, Kegel, I don’t know how to pronounce it, but it’s a data science, like, competition platform where people can set up all these queries, like, run an analysis, and then, people just attend.
115 00:09:09.590 ⇒ 00:09:12.440 Sezim Zhenishbekova: Predict, and get scored automatically.
116 00:09:13.010 ⇒ 00:09:19.489 Sezim Zhenishbekova: They have, like, enterprises. You can find a lot of cool projects there that people work on.
117 00:09:19.960 ⇒ 00:09:20.670 Henry Zhao: Yeah, cool.
118 00:09:20.890 ⇒ 00:09:21.890 Sezim Zhenishbekova: Yeah.
119 00:09:24.190 ⇒ 00:09:25.250 Sezim Zhenishbekova: Thanks, Tom.
120 00:09:25.250 ⇒ 00:09:26.389 Henry Zhao: I sent you the invites.
121 00:09:26.560 ⇒ 00:09:30.389 Sezim Zhenishbekova: Thank you. Bye, yes.
122 00:09:30.390 ⇒ 00:09:31.910 Henry Zhao: Jonah said… oh, he didn’t say anything yet.
123 00:09:32.190 ⇒ 00:09:36.519 Sezim Zhenishbekova: So I think our best thing is probably to, you know, reply to her.
124 00:09:37.080 ⇒ 00:09:40.809 Henry Zhao: I think this, alert is just me giving you access, this Eden Data Alert.
125 00:09:41.820 ⇒ 00:09:42.720 Sezim Zhenishbekova: Mists.
126 00:09:42.760 ⇒ 00:09:43.510 Henry Zhao: Maybe not.
127 00:09:43.510 ⇒ 00:09:45.399 Sezim Zhenishbekova: CO2 cents.
128 00:09:47.210 ⇒ 00:09:48.430 Henry Zhao: Are you in a WeWork right now?
129 00:09:48.430 ⇒ 00:09:49.240 Sezim Zhenishbekova: No.
130 00:09:49.420 ⇒ 00:09:52.210 Sezim Zhenishbekova: No, I’m at other jobs often.
131 00:09:52.630 ⇒ 00:09:54.600 Henry Zhao: Oh, okay, gotcha. Yeah.
132 00:09:54.680 ⇒ 00:10:02.960 Sezim Zhenishbekova: I will send you this, maybe you would be interested to see Michigan versus… Texas?
133 00:10:03.540 ⇒ 00:10:06.130 Sezim Zhenishbekova: I’ll send you via Slack.
134 00:10:06.400 ⇒ 00:10:15.579 Sezim Zhenishbekova: You can run the head-to-head analysis there. So, basically, my boss, he… he’s been, like, a long-time contractor.
135 00:10:16.020 ⇒ 00:10:24.460 Sezim Zhenishbekova: And then, but since, like, for 10 years, he’s been developing on his own model, using, like, over 250 models.
136 00:10:24.460 ⇒ 00:10:33.040 Henry Zhao: to predict the sports outcomes, so we use, like, a lem and everything. The Chris rate is pretty good. Just give it a try, maybe you will.
137 00:10:33.860 ⇒ 00:10:38.110 Sezim Zhenishbekova: I just want to hear your opinion, if it’s close enough or not.
138 00:10:45.040 ⇒ 00:10:46.080 Henry Zhao: I like it.
139 00:10:48.190 ⇒ 00:10:51.030 Henry Zhao: Oh, okay, so there’s the home team and there’s the away team.
140 00:10:51.170 ⇒ 00:10:53.860 Sezim Zhenishbekova: Yeah, because it plays a big role, yeah.
141 00:10:56.280 ⇒ 00:10:59.049 Henry Zhao: Okay, let’s do, like, the best versus the worst. Let’s see that.
142 00:10:59.810 ⇒ 00:11:00.900 Henry Zhao: Biggest Delta.
143 00:11:04.590 ⇒ 00:11:05.270 Henry Zhao: Hmm?
144 00:11:05.440 ⇒ 00:11:08.710 Henry Zhao: Margin 10 to 12, when probably 80-some percent.
145 00:11:13.450 ⇒ 00:11:14.190 Henry Zhao: Let’s see.
146 00:11:15.430 ⇒ 00:11:20.750 Henry Zhao: Let’s take a real example. So today is Eagles at Chargers, I think.
147 00:11:23.240 ⇒ 00:11:26.719 Henry Zhao: We’ll see how close we are to 40… 59%, 41%.
148 00:11:27.060 ⇒ 00:11:28.600 Henry Zhao: This doesn’t even add up to 100%.
149 00:11:29.180 ⇒ 00:11:29.990 Sezim Zhenishbekova: VR.
150 00:11:30.470 ⇒ 00:11:31.080 Henry Zhao: Ghost.
151 00:11:31.360 ⇒ 00:11:33.089 Henry Zhao: So I guess there is a tie probability.
152 00:11:39.340 ⇒ 00:11:43.049 Henry Zhao: 54% Eagles win, 59. Okay, pretty close.
153 00:11:43.730 ⇒ 00:11:47.350 Sezim Zhenishbekova: Yeah, and it’s also depending on the matchup side.
154 00:11:47.700 ⇒ 00:11:50.759 Sezim Zhenishbekova: what’s home and what’s away, I think, that will be grown.
155 00:11:50.760 ⇒ 00:11:51.819 Henry Zhao: It’s the same team.
156 00:11:52.210 ⇒ 00:11:54.400 Sezim Zhenishbekova: Yeah, we don’t…
157 00:12:00.200 ⇒ 00:12:00.720 Sezim Zhenishbekova: No.
158 00:12:00.720 ⇒ 00:12:02.269 Henry Zhao: Much higher for at home.
159 00:12:04.080 ⇒ 00:12:15.890 Sezim Zhenishbekova: Home always plays a role. And then, if you want it, there’s a Vegas line analysis as well, if you go to Sport category and choose NFL, and there’s a Vegas line, it’s more in detail.
160 00:12:16.060 ⇒ 00:12:18.569 Sezim Zhenishbekova: Did, did John respond, or no?
161 00:12:23.240 ⇒ 00:12:24.120 Sezim Zhenishbekova: Thanks.
162 00:12:24.710 ⇒ 00:12:29.429 Sezim Zhenishbekova: So maybe we can use this call to talk,
163 00:12:30.980 ⇒ 00:12:35.270 Sezim Zhenishbekova: Maybe you can assign me tasks, maybe you can start assigning tasks, if that.
164 00:12:35.760 ⇒ 00:12:40.639 Sezim Zhenishbekova: measurable, or… Or should I just, like, go over and…
165 00:12:41.020 ⇒ 00:12:41.800 Henry Zhao: Start dry.
166 00:12:43.100 ⇒ 00:12:45.660 Henry Zhao: Let’s see how we would break up these tasks, even.
167 00:12:48.400 ⇒ 00:12:50.049 Henry Zhao: New returning customers.
168 00:12:51.630 ⇒ 00:12:53.850 Henry Zhao: Because I already started pulling some of the data.
169 00:12:55.760 ⇒ 00:12:56.330 Sezim Zhenishbekova: Hmm.
170 00:13:02.920 ⇒ 00:13:03.650 Sezim Zhenishbekova: Oh, bless you.
171 00:13:03.650 ⇒ 00:13:04.710 Henry Zhao: Thank you.
172 00:13:07.730 ⇒ 00:13:11.409 Henry Zhao: You’ll probably need to do some, like, product labeling, first of all.
173 00:13:12.720 ⇒ 00:13:16.659 Henry Zhao: Like, it needs to be mapped properly, so, like, we have different data sources, right?
174 00:13:16.990 ⇒ 00:13:18.629 Henry Zhao: And product names don’t always match.
175 00:13:18.750 ⇒ 00:13:28.490 Henry Zhao: So, like, here we’ll have an HRT, but they’re actually in another table broken down by Kit 1, Kit 2, Kit 3. So we need to figure out how we, kind of.
176 00:13:28.860 ⇒ 00:13:30.389 Henry Zhao: Break HRT by kits.
177 00:13:30.390 ⇒ 00:13:38.959 Sezim Zhenishbekova: And I wonder what’s the difference? Like, is it just mislabeling, or did they mean something else?
178 00:13:38.960 ⇒ 00:13:40.080 Henry Zhao: Figure that out, you know.
179 00:13:40.580 ⇒ 00:13:41.320 Sezim Zhenishbekova: Yeah.
180 00:13:44.520 ⇒ 00:13:47.639 Henry Zhao: Everything else is pretty standard. Ozempic, Ozempic, Ozempic…
181 00:13:48.040 ⇒ 00:13:58.999 Henry Zhao: semaglutide is, like, combined here, I guess? Oh, I think this is injectable semaglutide. So if it just says semaglutide in marketing product name, it’s injectable semaglutide, okay? So it’s injectable semi.
182 00:13:59.000 ⇒ 00:14:02.060 Sezim Zhenishbekova: ODT, gummings, okay,
183 00:14:02.720 ⇒ 00:14:05.989 Henry Zhao: I have no idea what Metatrim is. Oh, I guess MetaTrim is this, MetaTrim.
184 00:14:08.330 ⇒ 00:14:14.290 Henry Zhao: Terzepatide is fine, Bardenafil, tendanofil, Wegovy, Zepbound, Zofran.
185 00:14:14.390 ⇒ 00:14:16.630 Henry Zhao: And then this minoxidil, we had to figure out.
186 00:14:17.070 ⇒ 00:14:18.630 Henry Zhao: We have that somewhere else.
187 00:14:18.790 ⇒ 00:14:20.260 Henry Zhao: Yes, this one, probably.
188 00:14:22.610 ⇒ 00:14:24.569 Henry Zhao: Oh, no, here we have minoxidil tablets.
189 00:14:24.720 ⇒ 00:14:27.979 Henry Zhao: So you probably just group al-minoxidil into one. Group these two.
190 00:14:29.800 ⇒ 00:14:33.990 Henry Zhao: But basically, we want to have, like, a standard… Standard list of products.
191 00:14:35.420 ⇒ 00:14:41.059 Henry Zhao: Right, so we have Everyday Plus… okay, so some of these are the exact same, so we can just copy and paste over.
192 00:14:42.500 ⇒ 00:14:45.280 Henry Zhao: Jeez, these are the same also.
193 00:14:46.810 ⇒ 00:14:49.139 Henry Zhao: HRT will need to figure out…
194 00:14:49.970 ⇒ 00:14:50.740 Sezim Zhenishbekova: Say…
195 00:14:50.740 ⇒ 00:14:54.439 Henry Zhao: Like, do we want to maybe just group all the three kids and do one HRT? So when you start.
196 00:14:54.440 ⇒ 00:14:55.019 Sezim Zhenishbekova: Probably just not.
197 00:14:55.080 ⇒ 00:14:56.660 Henry Zhao: The client about that.
198 00:14:57.120 ⇒ 00:14:59.699 Henry Zhao: Then we probably want to keep injectable sema.
199 00:15:01.650 ⇒ 00:15:06.670 Henry Zhao: lira glutide, MIC plus B12.
200 00:15:07.620 ⇒ 00:15:08.390 Sezim Zhenishbekova: these ones.
201 00:15:09.280 ⇒ 00:15:11.610 Henry Zhao: Here, let me… put a placeholder.
202 00:15:19.480 ⇒ 00:15:21.640 Henry Zhao: I’ll make it red say, like, that’s not the final, okay?
203 00:15:21.740 ⇒ 00:15:25.179 Henry Zhao: And then Medicaid 1 through 5 seems okay…
204 00:15:25.910 ⇒ 00:15:28.810 Henry Zhao: MetaTrim, put on to MetaTrim, I think.
205 00:15:29.430 ⇒ 00:15:32.529 Henry Zhao: Methylene blue and minoxidil tablets, looks good to me.
206 00:15:33.640 ⇒ 00:15:39.210 Henry Zhao: Manjaro NAD… see here, again, like, probably want to just put NAD all in one.
207 00:15:40.740 ⇒ 00:15:42.239 Jonah: Hey, Henry, sorry about that.
208 00:15:42.990 ⇒ 00:15:43.909 Henry Zhao: No worries.
209 00:15:44.350 ⇒ 00:15:45.700 Henry Zhao: Beef and jingle.
210 00:15:45.700 ⇒ 00:15:48.469 Jonah: And CPA conversations.
211 00:15:50.370 ⇒ 00:15:50.720 Henry Zhao: Sure.
212 00:15:50.720 ⇒ 00:15:53.070 Jonah: I invited you on, you might have had some fun.
213 00:15:53.370 ⇒ 00:15:55.910 Sezim Zhenishbekova: Yeah, we’re having some fun right now.
214 00:15:55.990 ⇒ 00:16:10.660 Henry Zhao: So, yeah, thanks for your time, Jonah. We just wanted to kind of go over… so, I wanted to introduce you to Seism. I don’t know, Sezm, if you want to do a quick intro, but Seism is kind of a finance expert, right? So, she’s going to be helping us with some of the finance work that we want to do.
215 00:16:10.740 ⇒ 00:16:18.870 Henry Zhao: Specifically, I got past this task that was, I think, done a while ago, where we scoped out a possible forecasting dashboard.
216 00:16:19.060 ⇒ 00:16:33.409 Henry Zhao: right, where you guys can kind of, like, adjust ad spend, adjust CAC, and some other types of things, and kind of figure out what 2026 is gonna look like, in terms of COGS, order volume, you know, and then try to figure out, kind of.
217 00:16:33.530 ⇒ 00:16:39.059 Henry Zhao: what we want to do in terms of financial planning, as well as pharmacy distribution, right? So make sure we have
218 00:16:39.230 ⇒ 00:16:57.020 Henry Zhao: the capacity to kind of fill those orders within SLA. So, I’ve kind of organized that task into this spreadsheet of what we plan to do, and I just wanted to walk through it with you, just to make sure that that kind of makes sense to you, and to see if you have any additional things you want to add, or things you want to see, in the final product. Does that work?
219 00:16:57.430 ⇒ 00:17:03.749 Jonah: That’s perfect, and nice to meet you, Seism. And just a little bit of background on the genesis behind this project.
220 00:17:03.800 ⇒ 00:17:19.110 Jonah: I… I sort of operate in a certain way with finance of how I think about it and monitor things, and the idea is I translate a lot of that to Adam and Josh and Danny, and I think the goal would be that anybody
221 00:17:19.300 ⇒ 00:17:32.979 Jonah: could log in and be able to just generally see where we are in some sort of easy, reportable ways. And so, as we go through this, if you have ideas of, hey, this would also be a neat thing to show, or this would be helpful.
222 00:17:33.890 ⇒ 00:17:39.949 Jonah: Let’s feel for we can… we can change some of these… these ideas, or some of the original instructions, but…
223 00:17:40.070 ⇒ 00:17:41.150 Jonah: This is perfect.
224 00:17:43.080 ⇒ 00:17:53.029 Sezim Zhenishbekova: Perfect, sounds good. Yeah. During my onboarding process, I will also, like, pay attention to that, and let you know about those two things, too.
225 00:17:53.450 ⇒ 00:17:55.270 Jonah: Okay, great.
226 00:17:55.810 ⇒ 00:17:57.280 Jonah: You wanna do it, Henry?
227 00:17:57.440 ⇒ 00:18:16.550 Henry Zhao: Yeah, so the things that we’ve broken things down are, initially, are just by pharmacy and by product. So, we really want to just… so right now, what we were doing, we were just classifying products into groups, so we can look at it by product and by pharmacy, because I think that’s pretty much the amount of detail we want to get into. If we break it down even more, it probably will be too…
228 00:18:16.720 ⇒ 00:18:24.289 Henry Zhao: inaccurate. So once we have that, we want to look at new returning customers, so that’ll probably require us to have an understanding
229 00:18:24.530 ⇒ 00:18:41.550 Henry Zhao: of, kind of, the life cycle for a customer for each product. So, in other words, if somebody takes injectable sema, like, how long should they be taking that, and then when they’re done with that treatment, should they be continuing that treatment, or do they usually churn, right? So we want to look at new returning customers as well as churn by product.
230 00:18:41.910 ⇒ 00:18:55.850 Henry Zhao: Then we want to look at revenues, COGS, and expenses. Right now, we’ve asked BASC to give us vial size data, and we’re gonna need that to calculate COGS. But for now, I’m trying to get, like, as much accurate data as possible in terms of how can we calculate COGS.
231 00:18:56.060 ⇒ 00:19:01.409 Henry Zhao: We already have a, like, a data source that has COGS, but I’m not sure how accurate it is.
232 00:19:01.560 ⇒ 00:19:11.939 Henry Zhao: Just for example, like, if I take a look at one pharmacy, like, for example, Precision looks good to me, because it’s 43.33 COGS per order, that looks pretty good.
233 00:19:11.990 ⇒ 00:19:24.650 Henry Zhao: But then there’s things like precision telemedicine, where it’s like the COGS is all over the place, and I’m just not sure if that’s… that’s accurate. So I want to understand where can we get accurate COGS data, and one thing we’re working on is getting that from Rebecca and Basque.
234 00:19:25.160 ⇒ 00:19:28.500 Henry Zhao: After that, we want to look at NOI, OEM Yeah, go ahead.
235 00:19:28.500 ⇒ 00:19:36.199 Jonah: If, so Rebecca will do Eden Pharmacy. Make sure you’re looping in Brad. Yeah.
236 00:19:36.330 ⇒ 00:19:37.790 Jonah: Okay, he can do…
237 00:19:37.790 ⇒ 00:19:38.779 Henry Zhao: competitors, yeah.
238 00:19:39.180 ⇒ 00:19:57.489 Henry Zhao: Yeah. And then we also want to kind of project our ROAS as well, basically by looking at the historical ROAS, and just kind of seeing what kind of curve that follows, so that we can kind of implement an equation that says, like, if we spend this much, or if we grow this much, this is what we think the ROAS will go down to. I mean, go up to.
239 00:19:57.810 ⇒ 00:20:17.000 Henry Zhao: And then finally, pharmacy SLA. So that’s something I’m working on right now with Brad, to kind of understand, as we grow, at what point do we maybe start getting challenged in terms of pharmacy SLAs? So again, going back to this sheet, so just to show what this is, this is number of orders that got past 10 days, this is the number of orders that went past 5 days.
240 00:20:17.170 ⇒ 00:20:32.839 Henry Zhao: And you can see the correlation of as the number of orders grows, then more and more orders get out of SLA. So we also want to have this type of projection, where we can say, we’re either gonna need to improve our capacity at the pharmacies, or do a better job of, like, assigning it to a pharmacy that can handle the volume.
241 00:20:33.090 ⇒ 00:20:36.230 Henry Zhao: So for our adjustable levers, we’re gonna have CAC, right, as well.
242 00:20:36.230 ⇒ 00:20:49.010 Jonah: One quick second on the 6-month forward looking. The… the 1, 2, and 4, are all tied together, and let me share my opinion about it, I’m curious what you think.
243 00:20:49.010 ⇒ 00:21:02.990 Jonah: So, the ROAS is important, from one metric, but it’s hard to make a prediction from ROAS. ROAS basically follows whatever we’re doing. So, if we think about a typical return,
244 00:21:03.090 ⇒ 00:21:20.279 Jonah: or retention cycle, and let’s do it by drug, obviously, because each one’s going to behave differently. But let’s just take SEMA, for instance. After the first month, we drop to, like, 70% retention, and then it goes to, like, 55, and then 48, and then so on, and then it kind of flatlines around 10%.
245 00:21:20.890 ⇒ 00:21:39.359 Jonah: Somewhere around month 5 or 6, it’s in the 20s, and then it gets 10%, somewhere around, like, month 10, 11, or 12. The number of new customers we acquire relative to the returning customers is all going to be dictating the ROAS more than anything else.
246 00:21:39.420 ⇒ 00:21:49.589 Jonah: And so if we think about that, the way I’ve been thinking is basically taking what was our number of new acquired customers.
247 00:21:49.900 ⇒ 00:21:58.089 Jonah: for each cohort, so I would go back, for instance, 12 months and say, okay, we acquired 2,000 that month, 12 months ago.
248 00:21:58.390 ⇒ 00:22:00.710 Henry Zhao: We should have 10% of those left.
249 00:22:00.720 ⇒ 00:22:08.620 Jonah: Okay, I go back 6 months, and we acquired 2,500 people, and we should have 30% of those left.
250 00:22:08.740 ⇒ 00:22:27.729 Jonah: And one month ago, we acquired 5,000, so we should have 70% of those left. And so we should… I think we could build out that retention curve. The way we would determine how… what the retention is for month 5, for instance, would be stacking all of our cohorts through time, and saying, by month 5, on average.
251 00:22:28.110 ⇒ 00:22:34.470 Jonah: For all of our cohorts, month 5 is usually a… 22%, or whatever it is.
252 00:22:34.630 ⇒ 00:22:43.100 Jonah: And then the ROAS is basically just going to be a function of how many new customers were acquiring relative to what that retention curve looks like.
253 00:22:44.410 ⇒ 00:22:45.460 Jonah: Oh. Yeah.
254 00:22:45.790 ⇒ 00:22:57.299 Jonah: So, from my perspective, when I think about projections, I’m going, how many… how many… what’s our ad spend, so that I can come up with a function of how many new customers we’re going to have?
255 00:22:57.860 ⇒ 00:23:07.329 Jonah: And how many customers did I acquire in the past for each cohort, based on, you know, each of the past, whatever, 12, 24, 18 months?
256 00:23:07.500 ⇒ 00:23:12.179 Jonah: And what’s my retention for those? And that’s gonna tell me my ROAS.
257 00:23:12.780 ⇒ 00:23:13.360 Henry Zhao: Yep.
258 00:23:13.690 ⇒ 00:23:19.130 Henry Zhao: And so, 7, we have the retention triangle here, so basically you can just do the… we can just do the calculation this way.
259 00:23:19.850 ⇒ 00:23:20.450 Jonah: Yeah.
260 00:23:20.450 ⇒ 00:23:22.479 Henry Zhao: Right, horizontally or vertically, yeah.
261 00:23:22.820 ⇒ 00:23:36.320 Jonah: But… and the reason I’m bringing it up is we historically have looked at it and said, oh, our ROAS is this, so we’re doing well. It’s like, well, no, we can just change our ROAS by spending more on ad dollars or less on ad dollars, and so if we spent $0 on ads.
262 00:23:36.420 ⇒ 00:23:44.220 Jonah: and acquired 4 people, our ROAS is gonna be through the roof. But did we actually achieve something? So,
263 00:23:48.060 ⇒ 00:24:05.570 Jonah: I guess I’m trying to think about… as we get into building something, I don’t want ROAS to blind us to the fact that ROAS isn’t a function of how well we’re doing ad spend, it’s just how much ad spend we’re doing relative to everything else. So, for me, it comes down to things like CPA,
264 00:24:05.570 ⇒ 00:24:11.330 Jonah: what does it cost to acquire those customers, and how much are we spending on those customers that matters, ROAS,
265 00:24:11.500 ⇒ 00:24:12.930 Jonah: can be misleading.
266 00:24:13.520 ⇒ 00:24:14.579 Jonah: Does that make sense?
267 00:24:14.870 ⇒ 00:24:16.480 Henry Zhao: Yeah, we’ll take that into account.
268 00:24:16.520 ⇒ 00:24:17.420 Jonah: Okay. Yeah.
269 00:24:18.580 ⇒ 00:24:24.210 Henry Zhao: And that’s why also we’ll have LTV, we’ll have ROAS and blended ROAS, because I think those three together kind of tell the whole picture.
270 00:24:27.930 ⇒ 00:24:42.879 Henry Zhao: All right, okay, so then the levers that we want to adjust for are CAC, ad spend, and then we can think of if there’s any other metrics that you want to, be able to adjust on. So we might want to think about, like, if there’s going to be maybe new drugs coming out.
271 00:24:43.240 ⇒ 00:24:51.939 Henry Zhao: And then Seism also mentioned that maybe we can look into competitor data if we have that, or market trends, right? Like, if we’re in a recession, maybe we apply, like,
272 00:24:52.060 ⇒ 00:24:57.759 Henry Zhao: ratio to, like, diminish certain projections or things like that, right? Did you have anything to add on that?
273 00:24:58.490 ⇒ 00:24:59.350 Sezim Zhenishbekova: Yeah.
274 00:24:59.350 ⇒ 00:25:07.439 Jonah: All good things. One… so, as we think about that yield curve, I’m gonna call it a yield curve, it’s really a retention curve,
275 00:25:08.020 ⇒ 00:25:25.360 Jonah: it’s sort of like, every month, we should be able to predict what the results should be based on how many cohort… how many were in the original cohort, and then what month they’re on, and what that retention cycle is. And we can generally do better than that, or worse than that. So, we might be able to predict, hey, there should be 22,000 returning customers, based on
276 00:25:25.470 ⇒ 00:25:30.289 Jonah: Prior history of retention and cohorts. And we ended up getting $25,000.
277 00:25:30.440 ⇒ 00:25:49.539 Jonah: Okay, so somewhere on that curve, we know there’s been some lift, and we can determine, did that come from one cohort or all of them? Kind of don’t care, because that number’s going to change through time. You’re gonna get some weirdness of, like, double orders in one month, and whatever else. So as we’re kind of thinking about, a relative retention number.
278 00:25:50.020 ⇒ 00:25:59.950 Jonah: I think it’s important to be able to say something like, hey, relative to what we would have anticipated retention would be, we actually outperformed by 5%, or we underperformed by 5%.
279 00:26:00.490 ⇒ 00:26:10.539 Jonah: And to give you an example, the past 2 months, we outperformed by 20%, and we went back and looked, and it was, like, there was a bunch of double orders, because it’s a 25- or 28-day
280 00:26:10.690 ⇒ 00:26:12.040 Jonah: repurchase cycle?
281 00:26:12.250 ⇒ 00:26:15.270 Jonah: So, maybe a… maybe a comparison of retention.
282 00:26:16.520 ⇒ 00:26:22.700 Sezim Zhenishbekova: The percentage change over the years and try to understand what made that change, right?
283 00:26:24.110 ⇒ 00:26:28.120 Jonah: Yes, the question being the why.
284 00:26:28.630 ⇒ 00:26:29.060 Sezim Zhenishbekova: I don’t know that.
285 00:26:29.300 ⇒ 00:26:41.440 Jonah: have it. I think just as sort of a red flag, just to know it did increase, or it did decrease, and what we’d hope to see is through time, we could go back and look and say, hey, we’ve consistently been 5% better
286 00:26:41.440 ⇒ 00:26:50.479 Jonah: Okay, maybe it’s time to start assuming our retention has actually improved, versus if some months it’s up 10, some months it’s down 10, we can just say, you know what, there’s just some variance in here, and…
287 00:26:51.400 ⇒ 00:26:53.630 Jonah: You know, we just anticipate some variance.
288 00:26:53.810 ⇒ 00:27:01.539 Henry Zhao: Yeah, and we’re hoping that the work that Judd does on win-back campaigns and, retention campaigns will help drive this up, right? Because that’s…
289 00:27:02.650 ⇒ 00:27:03.390 Jonah: You got it.
290 00:27:03.390 ⇒ 00:27:18.239 Henry Zhao: So yeah, we’ll be able to adjust that metric. And then the other way we can do it, instead of having, like, a market trend modifier, is we can just give 3 projections. We can give, like, an optimistic one, an average one, and then a conservative one, so that you can just kind of see the range of where we forecast this to be.
291 00:27:18.590 ⇒ 00:27:30.929 Jonah: Yep, and then on the adjustable levers, obviously, we’re going to have a bunch related to expenses. What’s payroll? What are we spending on this, that, and the other. I don’t know if we want to get that complicated in a shared dashboard, so I’m kind of…
292 00:27:31.700 ⇒ 00:27:33.939 Jonah: Cool with keeping the 3 that you have here.
293 00:27:34.200 ⇒ 00:27:40.270 Henry Zhao: Yeah, we can keep these 3, and then after the first version, like, we can look at it and provide feedback to add new things, right? So…
294 00:27:40.710 ⇒ 00:27:43.329 Henry Zhao: This is not the be-all, end-all.
295 00:27:43.810 ⇒ 00:27:47.429 Henry Zhao: So that’s pretty much it. I think the other just things that we wanted to…
296 00:27:47.590 ⇒ 00:28:07.119 Henry Zhao: just consider is, like, how do we consider upsells, new products, and, like, we already talked about this, the, like, the intended course by drug, right? So, we’ll probably need somebody at the pharmacy to kind of go through the list of drugs with us, just to understand, kind of, what the typical… just to… I’ll just confirm, like, what does the typical treatment plan look like?
297 00:28:07.120 ⇒ 00:28:09.470 Henry Zhao: In terms of when they’re done with that treatment.
298 00:28:09.640 ⇒ 00:28:20.200 Henry Zhao: Do we sell them something else? Do they potentially continue? Do we figure that out from their health info, right? Like, if their BMI is, I don’t know, 30 to 35, maybe they only do one round of…
299 00:28:21.170 ⇒ 00:28:28.569 Henry Zhao: GLP-1 versus if they’re, like, 50 to 55, maybe we see them doing it more long-term, right? So it’s just kind of getting this product knowledge.
300 00:28:29.070 ⇒ 00:28:34.669 Henry Zhao: And then, how do we factor in increased CPMs over time, inflation, health trends, and economic variations?
301 00:28:34.920 ⇒ 00:28:40.039 Henry Zhao: And then how do we move the lever with win-back and cross-sell campaigns that Judd is working on?
302 00:28:40.170 ⇒ 00:28:43.309 Henry Zhao: And how do we kind of figure out economy… like, decreasing.
303 00:28:44.480 ⇒ 00:28:54.579 Henry Zhao: returns, right? Like, I don’t think, like, if you keep spending more and more, it’s gonna be a linear growth. I think eventually we might hit some sort of, like, tapering off, and we need to figure out where that would be.
304 00:28:54.670 ⇒ 00:28:55.610 Jonah: Yup.
305 00:28:56.590 ⇒ 00:28:57.220 Henry Zhao: So…
306 00:28:57.220 ⇒ 00:29:04.509 Jonah: And that’ll be by drug and in aggregate. Our brand should run into some walls, but so should each drug.
307 00:29:06.700 ⇒ 00:29:20.800 Henry Zhao: Yeah, and I wonder if we need to do some, like, market research on, like, by drug, to say, like, what is the total addressable market, and maybe just as it reaches 50% of the TAM, like, that’s when the… it starts tapering off, something like that, right? So there… there are things we can do, but yeah.
308 00:29:21.160 ⇒ 00:29:33.409 Jonah: Yeah, I think all those are great. I think, one of the inputs to consider is, historically, November and December, we believe, and I haven’t seen great data yet, but we believe that
309 00:29:33.410 ⇒ 00:29:47.740 Jonah: CPAs are going to increase during those periods, and then they decrease dramatically during January, February for New Year’s resolutions and all that, so we could have a seasonal adjustment. The rest of the year is pretty well flat, from my understanding and from what I’ve seen, but
310 00:29:47.760 ⇒ 00:29:49.110 Jonah: Seasonal adjustments?
311 00:29:49.210 ⇒ 00:29:58.119 Jonah: And then here’s something else that’s potentially interesting that I want to include in here, and I don’t know where you put it, so I’m just going to throw it here, is…
312 00:29:58.120 ⇒ 00:30:12.000 Jonah: In Google specifically, we set two things. We set a CPA target, and we set a budget. So we tell Google, okay, you can spend up to 10,000, go do it. And then it tries to hit those things on average.
313 00:30:12.380 ⇒ 00:30:22.960 Jonah: If the CPA we set is too low, we’re not going to be able to sped our ad, because it’s basically saying, hey, you’re spending 300, so you’re just not going to spend your dollars. And so we can tell how well
314 00:30:23.210 ⇒ 00:30:28.169 Jonah: Our CPA is matched to market based on what we’re spending, relative to what we target.
315 00:30:28.300 ⇒ 00:30:40.890 Jonah: I think that data of those two things, and the third being, what did we actually spend, would be super, super incredible. Because if we had a year’s worth of data, two years’ worth of that data, you can then be able to say.
316 00:30:41.090 ⇒ 00:30:47.229 Jonah: hey, our CPA, like, the market CPA adjusted through time, and we’re keeping up with that.
317 00:30:50.170 ⇒ 00:30:50.760 Henry Zhao: Okay?
318 00:30:55.540 ⇒ 00:30:56.220 Henry Zhao: What else?
319 00:31:01.730 ⇒ 00:31:12.820 Jonah: I think that’s great. I’m gonna half-step back on some of this, the inputs to consider. I mean, you’ve got health trends, and economic variations, and inflation.
320 00:31:12.980 ⇒ 00:31:21.160 Jonah: first of all, all that’s insane and awesome, and I love it. Insane in a good way. My caution is to say.
321 00:31:21.330 ⇒ 00:31:40.809 Jonah: we… currently, we do nothing. Like, we are starting so, so basic. So part of my thought here is, if we put too much, will everybody be able to follow it? Will everybody, be able to understand it and use it, or is it just gonna be so much data? And so my thought is, does it make sense on the first iteration to just keep it simple? Like.
322 00:31:40.810 ⇒ 00:31:45.870 Henry Zhao: Yeah. Basic, basic things. Keep all those things in mind, because I’d love to see it through time.
323 00:31:45.870 ⇒ 00:31:55.239 Jonah: But I just don’t think we’re anywhere near sophisticated enough to… like, you might be to get the data, but I don’t think we’re sophisticated enough to know what to do with it.
324 00:31:56.550 ⇒ 00:32:05.809 Henry Zhao: Yeah, I think let’s do a simple first pass for… before end of year, so that you guys can take it into account for 2026 planning, but then in January, let’s go ahead and see what are…
325 00:32:05.810 ⇒ 00:32:18.279 Henry Zhao: out of these things, what can we add? And to start adding them piece by piece. And all of it will be optional also. Like, we’ll probably make it a filter where you can decide whether or not you want to include these additional things we’re adding, and then you can see how that kind of changes.
326 00:32:18.280 ⇒ 00:32:26.320 Henry Zhao: the forecast, and see if it makes sense, right? Like, you might click something, and we do a calculator, and it’s like, we’re gonna sell $20 trillion, like, obviously that’s not right.
327 00:32:26.320 ⇒ 00:32:33.730 Henry Zhao: But, like, putting these things in will help us get an idea of whether or not it’s something that we can accurately put into our forecast.
328 00:32:34.110 ⇒ 00:32:37.580 Jonah: And, if you don’t mind, can I screen share something really quick, and then come right.
329 00:32:37.580 ⇒ 00:32:38.290 Henry Zhao: Yeah, absolutely.
330 00:32:38.290 ⇒ 00:32:39.340 Jonah: Might be a good time.
331 00:32:39.520 ⇒ 00:32:40.500 Jonah: Alright.
332 00:32:44.580 ⇒ 00:32:47.799 Jonah: There we go, the screen, projects and fun, share.
333 00:32:48.640 ⇒ 00:33:04.139 Jonah: Okay, so, this is sort of how we’re doing it now, is basically we’ve got a general pro forma of, month by month, what we’re anticipating, just normal P&L. And we’re… we basically dictate what is our ad spend, so this is our main input.
334 00:33:04.420 ⇒ 00:33:11.690 Jonah: And we have our CACs that we are anticipating, which is the other input, which are two of the things that you’ve got, which is great.
335 00:33:12.150 ⇒ 00:33:27.280 Jonah: And then, basically, I take, and this is a little bit messy, but I basically have our typical retention cycle for our 3 main… 4 main drugs, being Sema, TERS, CIRM, and then I’m calling the other one Other, and it’s just everything else.
336 00:33:27.390 ⇒ 00:33:37.569 Jonah: And the reason everything else is in other is because we are spending a de minimis amount of money in those. The only three that have legitimate budgets right now are TERS, CERM, and SEMA.
337 00:33:37.680 ⇒ 00:33:41.979 Jonah: So then, basically, this is the retention curve through time for each of the drugs.
338 00:33:42.660 ⇒ 00:33:53.750 Jonah: And then I’m applying… what was our cohorts? So these are our cohorts that we acquired. And so, for this month, I’m gonna apply this cohort to this
339 00:33:53.980 ⇒ 00:34:02.420 Jonah: retention number in this cohort to this retention number. And then every month as we go forward, we’re basically rolling these people all the way back through.
340 00:34:02.420 ⇒ 00:34:02.820 Henry Zhao: Yep.
341 00:34:02.820 ⇒ 00:34:07.000 Jonah: And that gets some estimate of, like, this is how many returning customers we should have.
342 00:34:07.600 ⇒ 00:34:24.740 Jonah: And based on the ad spend and the CAC, this is the new customers we should have, and so this should be the total of customers we should get month by month. And so, if we’re going to do a full year budget, what we… it would be easy to be able to say is, here’s our cohorts, here’s our retention cycle, here’s what we should anticipate month by month.
343 00:34:24.850 ⇒ 00:34:30.509 Jonah: Then let’s assume we have some ad spend curve with some basic growth.
344 00:34:31.080 ⇒ 00:34:45.140 Jonah: and that ad spend growth, and let’s assume our CPAs remain somewhat fixed. Maybe we adjust for November, December, January, and February for those seasonal adjustments. But then it would be pretty easy to basically just say, assuming these things are… stay static.
345 00:34:45.610 ⇒ 00:35:01.180 Jonah: We can pretty much predict throughout the year what our… what our customer count’s going to be. And then, based on the customer count, we’re going to know what our revenue is going to be, because, you know, we’ll have an input on pricing, which should stay pretty static throughout the year.
346 00:35:01.830 ⇒ 00:35:10.890 Jonah: COGS, same thing. It should be pretty static, but we should have maybe some basic growth, things like Brad’s gonna be able to find some efficiencies through time and get us from 40% to 39.
347 00:35:11.910 ⇒ 00:35:23.999 Henry Zhao: Who can… in the meantime, who can give me the calculation on how COGS is calculated? Something like, this vial size is this much money, this is overhead costs, like, where can I get the actual calculation for COGS?
348 00:35:24.610 ⇒ 00:35:30.409 Jonah: What I… so what I do is, we pay something like…
349 00:35:30.670 ⇒ 00:35:43.580 Jonah: 80 bucks, 85 bucks for Sema, and so what I did is took Brad’s numbers that he’s acquired from all 5 of our pharmacies, and I just assumed, hey, 10% of our volume is going to Eden Pharmacy, where we’re paying $40,
350 00:35:44.000 ⇒ 00:35:53.730 Jonah: whatever I said, 90% is going to everybody else, and we, on average, pay $85. So I applied that number to the semi-sales that we have.
351 00:35:53.850 ⇒ 00:36:10.510 Jonah: TERS is something like 110, same math. And then CIRM is, oh gosh, 75 or 100, Blinken. And then for other, I basically just take an average of all the other. The thing with the others is it’s such a low volume that it kind of doesn’t matter.
352 00:36:10.640 ⇒ 00:36:13.079 Jonah: And that… that comes out to something like…
353 00:36:14.600 ⇒ 00:36:18.919 Jonah: You know, there’s a little bit of variance here, but 39 to 40… Which is about right.
354 00:36:19.100 ⇒ 00:36:26.299 Henry Zhao: So we’ll group it the same way, too, then. Instead of breaking out, like, HRT, medkits, all that stuff, we’ll just also do CIRM, SAMA, TERS, and other.
355 00:36:26.440 ⇒ 00:36:28.290 Henry Zhao: Maybe split out AD+.
356 00:36:28.570 ⇒ 00:36:45.390 Jonah: And then in the future, if we say, hey, let’s start putting a bunch of money into marketing HRT, great, we’ll break it out at that point. But even then, we’ll have so many returning… so many fewer returning customers, that breaking it out almost doesn’t make sense until we have 4, 5, 6 months of
357 00:36:45.510 ⇒ 00:36:47.219 Jonah: Real ad spend data.
358 00:36:55.210 ⇒ 00:36:56.559 Henry Zhao: Okay, makes sense.
359 00:36:57.340 ⇒ 00:37:00.599 Jonah: And I’m gonna stop sharing here, and then you can go back.
360 00:37:00.730 ⇒ 00:37:07.500 Jonah: So anyway, that… what I’m thinking about, what does it look like? It’s basically this, but just a cleaned up, easy version.
361 00:37:08.170 ⇒ 00:37:15.410 Henry Zhao: Okay, yeah, in Tableau, we’ll put it in Tableau for now. Okay. If you could actually share this with us, you can get rid of, like.
362 00:37:16.050 ⇒ 00:37:20.740 Henry Zhao: the… any sensitive data, but that would be a good, I think, for us to have something to go off of.
363 00:37:21.440 ⇒ 00:37:22.470 Jonah: Yeah, cool.
364 00:37:23.080 ⇒ 00:37:25.300 Henry Zhao: Okay. That’s it on my end as well.
365 00:37:25.300 ⇒ 00:37:26.709 Jonah: There’s no other…
366 00:37:26.710 ⇒ 00:37:27.390 Henry Zhao: Thoughts?
367 00:37:27.890 ⇒ 00:37:29.119 Jonah: Let’s definitely get started.
368 00:37:29.120 ⇒ 00:37:31.050 Henry Zhao: This initial version.
369 00:37:32.100 ⇒ 00:37:38.069 Henry Zhao: like, basically just applying curves and things like that, kind of like what Jonah has done, and then we can start making a plan says them on what are the things we want to add.
370 00:37:38.070 ⇒ 00:37:38.460 Sezim Zhenishbekova: Damn.
371 00:37:40.290 ⇒ 00:37:56.410 Jonah: Awesome. Awesome. Well, thank you all so much, really appreciate it, and this is probably one of those where the 80-20 rule is, like, you can have something pretty simple and easy that covers most of what we need, but man, you could spend 14 years really making this thing awesome, and we’ll get there when we get there.
372 00:37:56.590 ⇒ 00:37:57.200 Henry Zhao: Yeah.
373 00:37:58.430 ⇒ 00:38:00.120 Jonah: Alright, thanks guys, appreciate it.
374 00:38:00.120 ⇒ 00:38:01.389 Sezim Zhenishbekova: Thank you, take care. Thank you.
375 00:38:01.780 ⇒ 00:38:02.850 Sezim Zhenishbekova: There, bye.