Meeting Title: Brainforge x Eden Project Onboarding Date: 2025-12-02 Meeting participants: Sezim Zhenishbekova, Henry Zhao
WEBVTT
1 00:14:48.700 ⇒ 00:14:50.690 Sezim Zhenishbekova: Hi, can you hear me?
2 00:14:50.840 ⇒ 00:14:51.570 Henry Zhao: Yes, honey.
3 00:14:51.970 ⇒ 00:14:53.180 Sezim Zhenishbekova: Yes, how are you?
4 00:14:53.750 ⇒ 00:14:55.910 Sezim Zhenishbekova: Oh, awesome. Okay.
5 00:14:58.340 ⇒ 00:15:01.230 Henry Zhao: How’s, how’s Brainforge going for you so far?
6 00:15:01.590 ⇒ 00:15:03.530 Sezim Zhenishbekova: Oh, it’s a lot.
7 00:15:03.530 ⇒ 00:15:04.390 Henry Zhao: Yeah.
8 00:15:04.390 ⇒ 00:15:15.050 Sezim Zhenishbekova: It’s taking me some time to understand where to start at the beginning, and then… yeah, otherwise it’s a lot of documentation, for sure, but super interesting.
9 00:15:15.390 ⇒ 00:15:18.520 Henry Zhao: Yeah, definitely. Can you tell me a little bit about yourself first?
10 00:15:18.520 ⇒ 00:15:19.000 Sezim Zhenishbekova: Yeah, yeah.
11 00:15:19.000 ⇒ 00:15:19.630 Henry Zhao: Pardon?
12 00:15:19.630 ⇒ 00:15:38.200 Sezim Zhenishbekova: Yes, I’m originally from Kyrgyzstan. I moved to the US 3 years ago to get my master’s degree at Fordham, Master’s in finance. Fordham is located in New York, so I’m based here, and that’s how I met Robert, through a common network that we had.
13 00:15:38.200 ⇒ 00:15:38.910 Henry Zhao: Yeah.
14 00:15:39.240 ⇒ 00:15:57.859 Sezim Zhenishbekova: In the past, I used to be product manager, but then decided to get more technical skills and did my master’s in finance, so I strive in a mix of strategy, finance, and management, and I think that’s why I decided to try myself at Brainforge AI.
15 00:15:57.880 ⇒ 00:15:59.809 Sezim Zhenishbekova: Seemed like a perfect fit.
16 00:16:00.700 ⇒ 00:16:01.679 Henry Zhao: Okay, very cool.
17 00:16:02.130 ⇒ 00:16:03.019 Henry Zhao: Oh my god.
18 00:16:03.020 ⇒ 00:16:03.480 Sezim Zhenishbekova: How long?
19 00:16:03.480 ⇒ 00:16:03.880 Henry Zhao: Yeah.
20 00:16:03.880 ⇒ 00:16:05.680 Sezim Zhenishbekova: And then Brainforge.
21 00:16:06.020 ⇒ 00:16:08.420 Henry Zhao: 4 months now?
22 00:16:08.640 ⇒ 00:16:11.719 Sezim Zhenishbekova: Okay, cool! Not long ago.
23 00:16:12.440 ⇒ 00:16:13.140 Henry Zhao: Yeah.
24 00:16:14.750 ⇒ 00:16:17.840 Henry Zhao: So, what languages do you speak, being from Kurdistan?
25 00:16:17.840 ⇒ 00:16:23.330 Sezim Zhenishbekova: I speak Kyrgyz, Russian, and English, yeah. How about you?
26 00:16:24.870 ⇒ 00:16:28.329 Henry Zhao: obviously English, but I’m learning Russian now, so that’s why I asked.
27 00:16:28.640 ⇒ 00:16:29.680 Henry Zhao: That’s cool.
28 00:16:29.830 ⇒ 00:16:31.429 Henry Zhao: I had my Russian lesson later today.
29 00:16:31.430 ⇒ 00:16:33.680 Sezim Zhenishbekova: Russian. Like, why Russian?
30 00:16:34.270 ⇒ 00:16:40.340 Henry Zhao: Cause I really like it, I really like the culture, and I speak 9 other languages, so, like, the next one I’m trying to learn.
31 00:16:40.340 ⇒ 00:16:42.320 Sezim Zhenishbekova: Dick, that’s so cool.
32 00:16:53.120 ⇒ 00:16:56.080 Sezim Zhenishbekova: What accent to Ochini Hiroshi.
33 00:16:56.350 ⇒ 00:16:57.070 Sezim Zhenishbekova: Yeah.
34 00:16:57.070 ⇒ 00:16:58.900 Henry Zhao: Spaciva, no…
35 00:17:02.000 ⇒ 00:17:02.790 Henry Zhao: Harashom.
36 00:17:09.020 ⇒ 00:17:09.700 Henry Zhao: And.
37 00:17:09.849 ⇒ 00:17:14.249 Sezim Zhenishbekova: That’s so cool. Nine languages, that’s insane! That’s…
38 00:17:14.660 ⇒ 00:17:19.409 Sezim Zhenishbekova: I tried to learn… I’m learning Chinese now, but nothing’s…
39 00:17:19.410 ⇒ 00:17:19.890 Henry Zhao: Oh, yeah.
40 00:17:19.890 ⇒ 00:17:21.799 Sezim Zhenishbekova: Just Duolingo strikes.
41 00:17:22.150 ⇒ 00:17:22.950 Henry Zhao: Oh, yeah.
42 00:17:22.950 ⇒ 00:17:26.259 Sezim Zhenishbekova: Yeah, it’s not bad. I try, I’m trying Hello Chinese now.
43 00:17:26.260 ⇒ 00:17:26.720 Henry Zhao: Amazing.
44 00:17:26.720 ⇒ 00:17:30.860 Sezim Zhenishbekova: much better than any other app I’ve used, but it’s…
45 00:17:30.860 ⇒ 00:17:31.350 Henry Zhao: Okay.
46 00:17:31.350 ⇒ 00:17:38.520 Sezim Zhenishbekova: Like, I mean, grammar-wise, it’s easy, but memorization and pronunciation is taking quite a bit of time.
47 00:17:38.850 ⇒ 00:17:44.199 Henry Zhao: Yeah, Duolingo’s actually not bad for Chinese, but it’s very bad for other languages. Like, Russian, it’s not great.
48 00:17:44.200 ⇒ 00:17:48.089 Sezim Zhenishbekova: Yeah, it’s a lot more complicated, I guess.
49 00:17:48.290 ⇒ 00:17:50.370 Sezim Zhenishbekova: But it creates habits, which is amazing.
50 00:17:51.100 ⇒ 00:17:54.800 Sezim Zhenishbekova: Yeah, definitely. This is my… this is what I’m learning right now.
51 00:17:55.400 ⇒ 00:17:56.820 Henry Zhao: My lessons, like…
52 00:17:57.910 ⇒ 00:18:00.510 Henry Zhao: All these… all these crazy conjugations, like…
53 00:18:01.780 ⇒ 00:18:03.460 Henry Zhao: Putechest wearat.
54 00:18:03.990 ⇒ 00:18:04.740 Sezim Zhenishbekova: Yeah.
55 00:18:05.040 ⇒ 00:18:08.959 Henry Zhao: Kinda like your last name, right? Like, your last name is really long, too.
56 00:18:08.960 ⇒ 00:18:25.610 Sezim Zhenishbekova: Yeah, yeah, it is… but Jenish means victory in Kyrgyz language, but ova is post-Soviet Union, and that was suffix, no. Pristavka, there was a suffix that was added, or… Yeah. Something like that, yeah.
57 00:18:26.150 ⇒ 00:18:33.790 Sezim Zhenishbekova: Yeah. Yeah, Russian is hard. I went to Russian school growing up, but still struggle with grammar from time to time.
58 00:18:34.840 ⇒ 00:18:35.710 Henry Zhao: Yum.
59 00:18:35.890 ⇒ 00:18:40.990 Sezim Zhenishbekova: Yeah, just like any other speakers. But yeah, perfect.
60 00:18:42.080 ⇒ 00:18:43.130 Henry Zhao: Also, what…
61 00:18:43.130 ⇒ 00:18:46.860 Sezim Zhenishbekova: This is cool, I have to ask.
62 00:18:46.860 ⇒ 00:18:51.699 Henry Zhao: No, no, this is just my back… just a random background. We should probably change it, actually.
63 00:18:51.950 ⇒ 00:18:55.169 Henry Zhao: No, just a random background. Right now I’m actually in Brazil, so…
64 00:18:55.450 ⇒ 00:18:57.550 Sezim Zhenishbekova: That’s cool. Yeah.
65 00:18:58.780 ⇒ 00:18:59.800 Sezim Zhenishbekova: Yeah.
66 00:18:59.800 ⇒ 00:19:05.440 Henry Zhao: Cool, so what do you know about Eden so far? What have you done, learned, or done research in terms of?
67 00:19:05.700 ⇒ 00:19:11.090 Sezim Zhenishbekova: Yeah, so I checked out the roadmaps that I’ve found so far, like, the.
68 00:19:11.090 ⇒ 00:19:11.560 Henry Zhao: Okay.
69 00:19:11.560 ⇒ 00:19:22.249 Sezim Zhenishbekova: cards, ERDs, architecture diagrams. Like, I know in general, what it does, and how it helps, but other than that, in terms of how many
70 00:19:22.580 ⇒ 00:19:41.159 Sezim Zhenishbekova: like, I try to get better understanding of data and where you store it in general, so it’s still, like, taking some time, but thanks to your Figma, like, data platform planning, I can get it. Like, now I know what’s North Buren, Zendesk.
71 00:19:41.450 ⇒ 00:19:59.979 Sezim Zhenishbekova: Sigma and Daxter, all those things do, but definitely I would love to get more insights from you, just to see what’s important, what’s not, because I see so many information, and I don’t know what’s still relevant, and what is not, and what I need to focus on specifically to get…
72 00:19:59.980 ⇒ 00:20:02.630 Henry Zhao: Have you been to their website yet to kind of look at all of the…
73 00:20:03.000 ⇒ 00:20:05.359 Henry Zhao: The things they sell and what… how they sell it?
74 00:20:05.360 ⇒ 00:20:08.140 Sezim Zhenishbekova: Yeah, but not, like, in detail.
75 00:20:08.540 ⇒ 00:20:13.040 Henry Zhao: So I’m gonna add you to this, forecasting plan document.
76 00:20:18.650 ⇒ 00:20:25.520 Henry Zhao: So this is kind of the forecasting task that we want to do. So what we plan to do, in general, these are what we want to look at.
77 00:20:25.750 ⇒ 00:20:32.419 Sezim Zhenishbekova: So 6 months forward, looking forecasting dashboards to predict the following, right? So, new returning customers, churn…
78 00:20:32.580 ⇒ 00:20:34.199 Henry Zhao: All this good stuff.
79 00:20:34.200 ⇒ 00:20:34.550 Sezim Zhenishbekova: smoothie?
80 00:20:34.550 ⇒ 00:20:46.929 Henry Zhao: would be CAC, ad spend, any other metrics. We can list those out later. That’s for us to decide. And then you can read through these questions that I put, and also let me know if you have any other questions.
81 00:20:47.050 ⇒ 00:20:53.309 Sezim Zhenishbekova: And then I left some notes here of, like, the main plan, but first, let’s go through the drugs. So these are all the drugs that they sell. Okay.
82 00:20:53.560 ⇒ 00:21:00.189 Henry Zhao: And that would help you make sense of what Robert is doing here, where he breaks it down by, like, these major…
83 00:21:00.390 ⇒ 00:21:04.159 Henry Zhao: product buckets, right? And he just looks at sales of each one.
84 00:21:04.380 ⇒ 00:21:08.990 Henry Zhao: By month, and it applies a ratio, right, to forecast the future.
85 00:21:08.990 ⇒ 00:21:09.590 Sezim Zhenishbekova: Yeah.
86 00:21:09.590 ⇒ 00:21:20.100 Henry Zhao: And that’s what Brad does as well, so Brad is the pharmacy ops person. Yeah. So he takes, like… right now it’s empty, but he takes the orders by pharmacy and by product, and then he does a VLOOKUP.
87 00:21:20.640 ⇒ 00:21:21.079 Sezim Zhenishbekova: to kind of.
88 00:21:21.080 ⇒ 00:21:22.689 Henry Zhao: Categorize them, right?
89 00:21:22.690 ⇒ 00:21:23.040 Sezim Zhenishbekova: Yeah.
90 00:21:23.040 ⇒ 00:21:30.699 Henry Zhao: We can do this on our own with a script, so we don’t actually do this by hand, but we have a script that labels this.
91 00:21:31.680 ⇒ 00:21:35.580 Henry Zhao: And then he does a monthly forecast by basically…
92 00:21:36.010 ⇒ 00:21:40.300 Henry Zhao: Taking the monthly volumes, and then applying a ratio, right?
93 00:21:40.620 ⇒ 00:21:47.029 Henry Zhao: So what we can do for now is we can, like, look at his forecast for November and see how close we were to that November forecast.
94 00:21:47.980 ⇒ 00:21:51.989 Henry Zhao: Right? But before we get into that, I just want to show you the website. So, whenever you…
95 00:21:52.160 ⇒ 00:21:55.620 Henry Zhao: Want to get a new medicine? You have to go to Get Started.
96 00:21:55.870 ⇒ 00:21:56.670 Sezim Zhenishbekova: Yeah.
97 00:21:56.830 ⇒ 00:21:59.049 Henry Zhao: And fill out this form, okay?
98 00:21:59.720 ⇒ 00:22:05.049 Henry Zhao: and this form will be tracked with, like, UTMs and stuff like that, and so we have a dashboard in…
99 00:22:05.690 ⇒ 00:22:07.340 Henry Zhao: Tableau, that basically.
100 00:22:07.340 ⇒ 00:22:11.360 Sezim Zhenishbekova: Yeah, I was wondering if we could… I could have access to Tableau, and what.
101 00:22:11.360 ⇒ 00:22:12.240 Henry Zhao: Yeah.
102 00:22:12.240 ⇒ 00:22:12.800 Sezim Zhenishbekova: Cheers.
103 00:22:12.800 ⇒ 00:22:15.360 Henry Zhao: You can just use Robert’s logins. Do you have Robert’s login?
104 00:22:15.600 ⇒ 00:22:20.079 Sezim Zhenishbekova: I don’t know if I have it for Tableau. Okay, let me check it afterwards.
105 00:22:20.080 ⇒ 00:22:21.209 Henry Zhao: I’m sharing with you right now.
106 00:22:21.980 ⇒ 00:22:22.630 Sezim Zhenishbekova: Yeah.
107 00:22:23.730 ⇒ 00:22:32.590 Henry Zhao: We all use Robert’s login, because we only have, two… I can give you the general Eden one. That might be better, because then you don’t log Robert out whenever you…
108 00:22:32.590 ⇒ 00:22:33.450 Sezim Zhenishbekova: Yeah.
109 00:22:33.450 ⇒ 00:22:33.955 Henry Zhao: win.
110 00:22:34.900 ⇒ 00:22:36.319 Sezim Zhenishbekova: Yeah, I know one, then.
111 00:22:39.570 ⇒ 00:22:40.700 Henry Zhao: I use this one, too.
112 00:22:42.160 ⇒ 00:22:44.269 Henry Zhao: And if it logs out, it just goes to the other one.
113 00:22:49.900 ⇒ 00:22:50.790 Henry Zhao: a link…
114 00:22:56.610 ⇒ 00:22:58.569 Henry Zhao: Okay, so that’s the Tableau login.
115 00:22:58.900 ⇒ 00:23:02.100 Henry Zhao: And then the Tableau, like,
116 00:23:03.510 ⇒ 00:23:07.719 Henry Zhao: like, website is Eden Health, okay?
117 00:23:08.040 ⇒ 00:23:08.770 Sezim Zhenishbekova: No room.
118 00:23:08.770 ⇒ 00:23:12.179 Henry Zhao: Like, they’re gonna ask you for, like, which server name, right? So it’s Eden Health.
119 00:23:12.180 ⇒ 00:23:12.640 Sezim Zhenishbekova: True.
120 00:23:12.640 ⇒ 00:23:15.040 Henry Zhao: But I think if you just click the link, it’ll take you there directly.
121 00:23:17.610 ⇒ 00:23:23.619 Henry Zhao: Okay, so all this stuff gets tracked, and basically… We want to look at…
122 00:23:24.240 ⇒ 00:23:27.570 Henry Zhao: This dash, to get a better understanding, so this is the important dash.
123 00:23:28.140 ⇒ 00:23:30.889 Henry Zhao: So, add spend to NCAC.
124 00:23:31.790 ⇒ 00:23:32.260 Sezim Zhenishbekova: Right?
125 00:23:32.260 ⇒ 00:23:35.330 Henry Zhao: Because the more I spend, the more customers I’m going to get.
126 00:23:35.730 ⇒ 00:23:52.449 Henry Zhao: or the more the NCAC is, the less customers I’m gonna get, right? So we use this to look at by product group, how much they’re spending on ads, and how much the NCAC is, right? So on average, like, we have to spend 274 to get a terzepatide patient.
127 00:23:52.540 ⇒ 00:23:56.089 Henry Zhao: And then you can analyze, like, this is their average order value, so, like, how much…
128 00:23:56.440 ⇒ 00:24:04.269 Sezim Zhenishbekova: Yeah. What is the error margin? Like, do you have… is it, like, how specific is this, and based on what kind of analysis you calculate in CAX?
129 00:24:05.190 ⇒ 00:24:10.140 Henry Zhao: There’s no error margin, this is just directly, like, what the customer revenue was divided by the ad spend.
130 00:24:10.140 ⇒ 00:24:12.089 Sezim Zhenishbekova: Okay, okay,
131 00:24:13.990 ⇒ 00:24:19.920 Sezim Zhenishbekova: It’s not, like, deeper dive analysis of, how much return is, like.
132 00:24:20.080 ⇒ 00:24:23.409 Sezim Zhenishbekova: What’s the cost to acquire every customer?
133 00:24:23.920 ⇒ 00:24:25.760 Henry Zhao: Not yet, we haven’t done that analysis yet.
134 00:24:27.420 ⇒ 00:24:37.090 Henry Zhao: That’s kind of what the marketing team does, right? So, Zoran’s job at Brainforge is to kind of analyze the attributions, so, like, out of the people that we send ads to, how far do they get here?
135 00:24:37.620 ⇒ 00:24:39.940 Henry Zhao: This is all tracked within segment.
136 00:24:40.940 ⇒ 00:24:53.399 Henry Zhao: which we’ll talk about later. But basically, that’s how you get a new drug, then it goes to the pharmacy. The pharmacy then approves it, or denies it, obviously, and if it gets approved, then, obviously, the customer gets the medicine.
137 00:24:53.580 ⇒ 00:25:03.560 Henry Zhao: I’m analyzing now the pharmacy part, right? So, Zaran is analyzing the marketing part to get the people to submit a prescription request, then I’m analyzing the pharmacy part, so…
138 00:25:03.670 ⇒ 00:25:13.909 Henry Zhao: How fast is it getting filled? Are we getting good quality of pharmacy submissions? Are the pharmacies overworked? Do we need to spread those out more evenly? Things like that.
139 00:25:14.900 ⇒ 00:25:15.700 Henry Zhao: Okay.
140 00:25:15.900 ⇒ 00:25:16.650 Sezim Zhenishbekova: Okay.
141 00:25:16.820 ⇒ 00:25:34.990 Henry Zhao: And then afterwards, the financing part is to figure out how can the leadership be well prepared for the upcoming volumes and plan financially, right? Do we want to have more ad spend? Do we want to spend less but get better customers? Like, that’s… hopefully those are the questions that they want to answer with this forecasting project.
142 00:25:35.350 ⇒ 00:25:36.909 Sezim Zhenishbekova: Make sense? Yeah.
143 00:25:36.910 ⇒ 00:25:39.419 Henry Zhao: And so my initial thoughts were…
144 00:25:39.980 ⇒ 00:25:48.149 Henry Zhao: my main question is, like, do we need to split ad channel… ad spend by channel and drug type, or is that too detailed? So that’s, I think, something we should think about in the meantime.
145 00:25:48.150 ⇒ 00:25:48.530 Sezim Zhenishbekova: Victor.
146 00:25:48.530 ⇒ 00:25:56.269 Henry Zhao: We want to break this up by product type, and then also, like, Facebook ad, Instagram ads, TikTok ads, Reddit.
147 00:25:56.540 ⇒ 00:26:02.510 Henry Zhao: affiliate ads, right? Do we want to break that, all that down, and have potentially, like, 600 rows?
148 00:26:02.760 ⇒ 00:26:11.219 Henry Zhao: Or is it good enough to just look at it by product, or just good enough to look at it by ad channel? So that’s the initial analysis that we want to do.
149 00:26:12.920 ⇒ 00:26:18.450 Henry Zhao: And then I’m looking at the pharmacy part, so, like, what is the turnaround time, all that stuff. So we will have to work together on this.
150 00:26:18.860 ⇒ 00:26:25.750 Henry Zhao: But initially, I think that’s the first thing, is to get used to this data and think about some of these questions on this spreadsheet.
151 00:26:26.210 ⇒ 00:26:26.900 Sezim Zhenishbekova: Okay.
152 00:26:27.180 ⇒ 00:26:28.090 Henry Zhao: Does that make sense?
153 00:26:28.220 ⇒ 00:26:29.249 Sezim Zhenishbekova: Yeah, it does.
154 00:26:29.250 ⇒ 00:26:38.329 Henry Zhao: Robert has very, like, simple. It’s just, like, he has the products, he has a ratio, and he just multiplies it. Like, it’s nothing complex, like, it’s literally just…
155 00:26:38.920 ⇒ 00:26:39.300 Sezim Zhenishbekova: Hmm.
156 00:26:39.300 ⇒ 00:26:41.119 Henry Zhao: Maybe she’s literally multiplying by this growth rate.
157 00:26:42.910 ⇒ 00:26:44.930 Sezim Zhenishbekova: Pretty straightforward, yeah.
158 00:26:47.470 ⇒ 00:26:51.359 Henry Zhao: And then if you want to look at any attribution stuff, there’s also an attribution dashboard.
159 00:26:52.530 ⇒ 00:26:53.700 Henry Zhao: That kind of shows.
160 00:26:53.700 ⇒ 00:26:57.739 Sezim Zhenishbekova: doc that you prepared, there was… yeah, I just couldn’t access it.
161 00:26:58.540 ⇒ 00:27:00.089 Henry Zhao: Yeah, I’m sending you this link also.
162 00:27:06.400 ⇒ 00:27:10.860 Sezim Zhenishbekova: So this is, if you want to break it down by, you can get rid of the, like, detail.
163 00:27:10.860 ⇒ 00:27:13.050 Henry Zhao: Like, if you don’t want… don’t care about source…
164 00:27:18.200 ⇒ 00:27:19.139 Henry Zhao: He’s calling me.
165 00:27:24.550 ⇒ 00:27:28.979 Henry Zhao: So yeah, so if you want to look at, like, do I want to just break it up by Facebook, etc.
166 00:27:29.140 ⇒ 00:27:31.610 Henry Zhao: You can do it this way, but right now, everything is really messy.
167 00:27:32.600 ⇒ 00:27:37.829 Henry Zhao: Just look at the big ones, like email, 56,000, influencer, whatever, and just kind of break down that ratio.
168 00:27:38.130 ⇒ 00:27:42.360 Henry Zhao: I’m just doing nonsense that way. However, however you want to do it, I don’t know.
169 00:27:43.100 ⇒ 00:27:45.679 Henry Zhao: There’s also this marketing dashboard that might be helpful.
170 00:27:46.800 ⇒ 00:27:49.650 Henry Zhao: It’s a little bit more summarized on the marketing piece.
171 00:27:53.170 ⇒ 00:27:56.310 Henry Zhao: No, this might not be as useful. This is, like, first marketing product.
172 00:28:00.240 ⇒ 00:28:01.549 Henry Zhao: I don’t know if this is as helpful.
173 00:28:02.010 ⇒ 00:28:04.120 Henry Zhao: But it’s something we can look into, moving forward.
174 00:28:04.570 ⇒ 00:28:05.070 Sezim Zhenishbekova: Is that…
175 00:28:05.070 ⇒ 00:28:08.240 Henry Zhao: I haven’t looked at it yet. Right now, I’m just brainstorming and, like, thinking about this stuff.
176 00:28:08.990 ⇒ 00:28:09.820 Sezim Zhenishbekova: Okay.
177 00:28:11.460 ⇒ 00:28:13.399 Henry Zhao: Okay, any questions in the meantime?
178 00:28:13.790 ⇒ 00:28:21.830 Sezim Zhenishbekova: Yeah, I would like to go through how you gather all this data, and know, like, Structure of it?
179 00:28:22.210 ⇒ 00:28:26.679 Sezim Zhenishbekova: Like, what are the dependencies that I need to know about?
180 00:28:28.170 ⇒ 00:28:33.060 Henry Zhao: Hmm, so what, data tools are you familiar with? So, let’s go to the…
181 00:28:33.770 ⇒ 00:28:41.040 Sezim Zhenishbekova: So, basically, in the document you have shared about, Sigment, BigQuery, Customer I.O, and BASC,
182 00:28:41.270 ⇒ 00:28:49.169 Sezim Zhenishbekova: North Beam, so I was just wondering, like, how the systems work between… how they interact with one another, and…
183 00:28:49.400 ⇒ 00:28:58.359 Sezim Zhenishbekova: Just get the general idea of how it’s done, and maybe opening the Eden Figma roadmap will be very helpful for me, specifically.
184 00:28:58.360 ⇒ 00:29:01.500 Henry Zhao: Okay. Can you send me a link to the Eden roadmap you’re talking about?
185 00:29:01.830 ⇒ 00:29:04.819 Henry Zhao: Oh yeah, send me the figment, please.
186 00:29:07.330 ⇒ 00:29:09.340 Sezim Zhenishbekova: I think that would help me visually.
187 00:29:22.250 ⇒ 00:29:23.530 Henry Zhao: There’s a lot of stuff here.
188 00:29:24.220 ⇒ 00:29:26.599 Sezim Zhenishbekova: Yeah, that’s why I was a bit overwhelmed.
189 00:29:27.100 ⇒ 00:29:29.180 Henry Zhao: Yeah, you don’t need to worry about all this stuff, this is…
190 00:29:29.370 ⇒ 00:29:30.730 Henry Zhao: I think a little bit too detailed.
191 00:29:33.230 ⇒ 00:29:40.849 Sezim Zhenishbekova: And it’s a lot like that with the documentations we have to, like, you have it print forage, so, like, there’s a lot, and I’m…
192 00:29:41.830 ⇒ 00:29:49.809 Sezim Zhenishbekova: Yeah, so this is, just, could you please walk through the important things that’s listed here that I need to know, that’s relevant?
193 00:29:49.810 ⇒ 00:29:52.130 Henry Zhao: I’m gonna show you my HIPAA diagram first, it’s a little bit more simple.
194 00:29:52.280 ⇒ 00:29:56.980 Henry Zhao: You’ll have to be… you don’t have to be as confused. So this is the big picture, right?
195 00:29:56.980 ⇒ 00:29:57.590 Sezim Zhenishbekova: Yeah.
196 00:29:58.100 ⇒ 00:30:08.629 Henry Zhao: So, we have a lot of things that come in, right? So Cloudflare is the edge layer stuff, so this captures the website I was just showing you, like, what website they’re coming from, what UTMs are coming from, etc.
197 00:30:09.080 ⇒ 00:30:13.239 Henry Zhao: Google Tag Manager, which is, like, the actual information from the website.
198 00:30:13.240 ⇒ 00:30:13.980 Sezim Zhenishbekova: Yeah.
199 00:30:14.150 ⇒ 00:30:22.870 Henry Zhao: Anytime you click something, anytime you go back, the Google Tag Manager tracks that. We have Segment, which tracks all of these different things, so if you go to Sources and Segment.
200 00:30:23.120 ⇒ 00:30:30.329 Henry Zhao: Which has Bask, which is their telehealth platform. I kind of like how e-commerce has Shopify.
201 00:30:30.680 ⇒ 00:30:36.540 Henry Zhao: Or Amazon has, like, the one-click, like, this is just… Their health platform, right?
202 00:30:37.000 ⇒ 00:30:40.820 Henry Zhao: We have Customer I.O, which is in that spreadsheet, in that Notion.
203 00:30:41.040 ⇒ 00:30:48.100 Henry Zhao: We have Facebook ads, Shippo is our tracking information, and then Webflow is more like web information, okay?
204 00:30:48.100 ⇒ 00:30:48.470 Sezim Zhenishbekova: Hmm.
205 00:30:48.470 ⇒ 00:30:50.010 Henry Zhao: That’s from GTM, okay?
206 00:30:50.860 ⇒ 00:31:00.370 Henry Zhao: And then we have Catalyst, which is one of our affiliates, okay? So, these are all the sources of data. So, these three things send anonymous data to the data warehouse, right?
207 00:31:00.900 ⇒ 00:31:05.519 Henry Zhao: You can see here destinations, like, one of the destinations is Google BigQuery.
208 00:31:07.030 ⇒ 00:31:10.139 Henry Zhao: So BigQuery is where all our data lives, right? So this is BigQuery…
209 00:31:12.630 ⇒ 00:31:18.589 Henry Zhao: We send all this data, don’t worry about it, like, that’s where the detailed chart comes in, but don’t worry about it.
210 00:31:18.590 ⇒ 00:31:19.020 Sezim Zhenishbekova: Any day.
211 00:31:19.020 ⇒ 00:31:23.939 Henry Zhao: that you need is sent in, like, say, Shippo’s shipping data, like I just said, Webflow’s web activity.
212 00:31:24.070 ⇒ 00:31:28.810 Henry Zhao: All the BASC stuff is any, like, prescription activity, right? Because that’s their telehealth platform.
213 00:31:29.260 ⇒ 00:31:36.770 Henry Zhao: So, like, anytime someone abandons a session, when they create a dispute, if they have a new treatment, if they complete an order, if they update an order…
214 00:31:36.940 ⇒ 00:31:38.839 Henry Zhao: These you don’t have to worry about.
215 00:31:39.470 ⇒ 00:31:49.949 Henry Zhao: If they update a treatment, and then Customer I.O, which is the email platform. So Customer I.O. is like MailChimp, or Klaviyo, or HubSpot, like any of those email platforms, okay?
216 00:31:49.950 ⇒ 00:31:50.300 Sezim Zhenishbekova: This is the one.
217 00:31:50.300 ⇒ 00:31:51.930 Henry Zhao: what that Eden uses.
218 00:31:53.760 ⇒ 00:31:59.769 Sezim Zhenishbekova: So that goes to BigQuery so that we have data, right? And that’s what you’re seeing in this stuff. Like, all this is BigQuery, right?
219 00:31:59.770 ⇒ 00:32:03.560 Henry Zhao: kind of showing you what all the tables are and how it’s made using dbt.
220 00:32:03.900 ⇒ 00:32:08.600 Henry Zhao: And GitHub is just how the programmers kind of, check each other’s work and stuff like that, yeah.
221 00:32:09.430 ⇒ 00:32:27.100 Henry Zhao: And then these go out, right? So after we process it, it goes out to Customer I.O. so that they can run email campaigns, so we feed it with, like, customer data, profile data. Since it’s anonymized, right, we can say, like, now you know that these people are starting up treatment, these are the people that need to get a refill, etc, okay?
222 00:32:27.520 ⇒ 00:32:39.079 Henry Zhao: Mixpanel is our event tracking platform, Tableau is the dashboards for the people to use at Eden, and then Northbeam is for the marketers to, run their campaigns and optimize their campaigns.
223 00:32:40.490 ⇒ 00:32:49.230 Henry Zhao: But the ELT, which is the executive leadership team, you’ll hear that a lot, basically uses Tableau to make all their decisions and look at data and do analysis.
224 00:32:49.460 ⇒ 00:32:50.370 Sezim Zhenishbekova: Yeah, okay.
225 00:32:50.370 ⇒ 00:33:02.660 Henry Zhao: But you can do spreadsheets as well. So, like, Robert here did a spreadsheet. So you can do a spreadsheet, or you can do a tableau. Spreadsheets are a little bit shady, because they can, like, break, and they can… you can give someone editor access, and they just break everything, right? So, I don’t know.
226 00:33:05.400 ⇒ 00:33:07.639 Henry Zhao: Okay, so hopefully that explains it a little bit better.
227 00:33:07.640 ⇒ 00:33:12.270 Sezim Zhenishbekova: So this is just, like, detail… getting into more detail from what I just showed you.
228 00:33:12.270 ⇒ 00:33:14.709 Henry Zhao: Right, so these are all the capital reports that you can look into.
229 00:33:14.710 ⇒ 00:33:16.290 Sezim Zhenishbekova: Okay,
230 00:33:17.220 ⇒ 00:33:20.439 Henry Zhao: This is, again, the same thing, just kind of, like, more horizontal, right?
231 00:33:21.650 ⇒ 00:33:24.120 Henry Zhao: This is Cloudflare, which you don’t have to really worry about.
232 00:33:26.140 ⇒ 00:33:28.450 Henry Zhao: And the others are just, like, team-related stuff, I guess.
233 00:33:29.370 ⇒ 00:33:30.090 Henry Zhao: Yeah.
234 00:33:30.670 ⇒ 00:33:32.590 Henry Zhao: Yeah, we’re talking about.
235 00:33:34.400 ⇒ 00:33:39.930 Henry Zhao: But maybe you can read the value chain analysis to kind of understand the high-level stuff of what we’re trying to do.
236 00:33:39.930 ⇒ 00:33:43.329 Sezim Zhenishbekova: And this is the roadmap. Okay.
237 00:33:43.640 ⇒ 00:33:45.839 Henry Zhao: I don’t think… worried about that.
238 00:33:48.330 ⇒ 00:33:50.369 Henry Zhao: That shit’s useful for me to look at, too, actually.
239 00:33:51.350 ⇒ 00:33:52.360 Sezim Zhenishbekova: Mmm.
240 00:33:56.030 ⇒ 00:33:58.290 Henry Zhao: Customers 36360 would be good.
241 00:34:06.050 ⇒ 00:34:10.519 Sezim Zhenishbekova: And what is the timeline like for the Eden project, and…
242 00:34:12.920 ⇒ 00:34:18.139 Henry Zhao: It’s an ongoing project, so don’t worry about timeline. Right now, we’re just trying to get a lot of analysis done.
243 00:34:18.330 ⇒ 00:34:19.930 Sezim Zhenishbekova: Okay, okay, got it.
244 00:34:20.530 ⇒ 00:34:22.730 Henry Zhao: So that they continue to renew our contract.
245 00:34:23.139 ⇒ 00:34:25.209 Henry Zhao: Yeah, so that’s the main goal, yeah.
246 00:34:25.210 ⇒ 00:34:32.609 Sezim Zhenishbekova: Well, okay. So we… you basically prepared this forecasting prototype.
247 00:34:32.880 ⇒ 00:34:37.419 Sezim Zhenishbekova: That you want it to be functional and running continuously, right?
248 00:34:38.280 ⇒ 00:34:40.660 Sezim Zhenishbekova: We just, like, insomnia cookies.
249 00:34:40.860 ⇒ 00:34:43.130 Sezim Zhenishbekova: Where… Yeah.
250 00:34:43.130 ⇒ 00:34:48.249 Henry Zhao: It can be a Tableau dash where you can, like the attribution dash that I showed you, where you can…
251 00:34:48.250 ⇒ 00:34:48.889 Sezim Zhenishbekova: Sure.
252 00:34:49.409 ⇒ 00:34:56.539 Henry Zhao: down and pick… there might be a parameter that says I want to break down my product, or I want to break down my pharmacy, and they can see what the projected…
253 00:34:56.820 ⇒ 00:34:59.599 Sezim Zhenishbekova: And platforms… okay.
254 00:34:59.600 ⇒ 00:35:07.270 Henry Zhao: So let’s say that right now, the Eden Pharmacy, which is the big one, because that’s their pharmacy, is, like, getting… filling 100,000 orders a month.
255 00:35:07.520 ⇒ 00:35:16.810 Henry Zhao: And then your forecast says now it’s gonna be $300,000 a month, right? Then they need to up their staff, or they need to reroute it to different pharmacies. So it’s like those kind of things that it enables.
256 00:35:16.810 ⇒ 00:35:25.579 Sezim Zhenishbekova: And then, could you provide some type of, like, final product that you usually deliver to the clients when they ask it?
257 00:35:25.580 ⇒ 00:35:28.320 Henry Zhao: I haven’t delivered anything yet. That’s what we’re working on delivering now.
258 00:35:28.320 ⇒ 00:35:29.670 Sezim Zhenishbekova: Okay, okay.
259 00:35:29.670 ⇒ 00:35:30.400 Henry Zhao: Right now, I would just…
260 00:35:30.400 ⇒ 00:35:37.019 Sezim Zhenishbekova: You just need to prepare the document on our sites, and they package it in the way they want, right? Okay. Yeah.
261 00:35:37.260 ⇒ 00:35:40.880 Henry Zhao: In a way that you think would be useful to them, based on the context I gave you and the roadmap.
262 00:35:41.130 ⇒ 00:35:46.250 Henry Zhao: But that’s also what I’m gonna talk to Robert about in a few hours. So I have a one-on-one with him today, I’m gonna talk to him about
263 00:35:46.500 ⇒ 00:36:04.739 Henry Zhao: what my thoughts are, and he might give feedback that I’ll come back to you and say, this is the feedback. But for now, you can just look at, kind of, my notes, the dashboards, and just got an understanding of the goals, and kind of have some ideas of what you think would be useful for a leadership team that’s trying to maybe improve their pharmacy operations, improve their sales.
264 00:36:04.740 ⇒ 00:36:20.539 Henry Zhao: and spend as little money as possible to get new customers, right? So, that could be bringing back current customers, that could be spending less on channels of marketing that are not doing so well, which would be Zoran’s space, but you can, like, work with him on that, right, if that’s your idea.
265 00:36:21.170 ⇒ 00:36:28.909 Henry Zhao: Or do we want to hire more people? Do we want to spend more on affiliates? Like, whatever it may be, right? So just think about, kind of, what are the analysis that we want to do.
266 00:36:29.710 ⇒ 00:36:31.170 Henry Zhao: That’s what I’m thinking about right now as well.
267 00:36:31.170 ⇒ 00:36:40.339 Sezim Zhenishbekova: Yeah, okay, I will focus on that after going through your approximate plan, and I think I need to spend a bit more time on Tableau to understand.
268 00:36:40.340 ⇒ 00:36:41.030 Henry Zhao: Yeah.
269 00:36:41.030 ⇒ 00:37:00.299 Sezim Zhenishbekova: like, because I had some certificate… I did some certification on Tableau, but I haven’t used it actively, so I need to refresh that to understand what else I can do to work on that. So, for the Tableau, do you want, like, to use the existing sheets, or do you, like, see completely new
270 00:37:00.340 ⇒ 00:37:03.439 Sezim Zhenishbekova: Dashboard that has its own dependencies.
271 00:37:03.700 ⇒ 00:37:06.769 Henry Zhao: It might be a new dashboard, but it’ll probably use pieces that we already have.
272 00:37:06.770 ⇒ 00:37:08.140 Sezim Zhenishbekova: Okay,
273 00:37:08.310 ⇒ 00:37:09.710 Henry Zhao: And do you know SQL?
274 00:37:09.970 ⇒ 00:37:19.270 Sezim Zhenishbekova: Yeah, a little bit, but not amazing, but I took the Udacity course, so… Yeah.
275 00:37:19.570 ⇒ 00:37:22.630 Henry Zhao: Yeah, so maybe you can download Tableau, log into Roberts.
276 00:37:23.100 ⇒ 00:37:31.019 Henry Zhao: And then try and play around with some of the underlying data if you want to. But yeah, I would just look at it by product and by pharmacy for now, because that’s the simplest way to look at it.
277 00:37:31.020 ⇒ 00:37:43.780 Sezim Zhenishbekova: Okay. And then the other thing, so since I’m also getting onboarded right now, what are your expectations from my side to the project?
278 00:37:43.970 ⇒ 00:37:46.199 Sezim Zhenishbekova: Like, that was my question.
279 00:37:46.200 ⇒ 00:37:51.680 Henry Zhao: I would just get an understanding of the data and, like, give any thoughts that you might have on the forecasting prototype.
280 00:37:51.680 ⇒ 00:37:52.720 Sezim Zhenishbekova: For now, okay.
281 00:37:52.720 ⇒ 00:37:54.740 Henry Zhao: Yeah, you can add it to the notes section here.
282 00:37:55.670 ⇒ 00:37:59.290 Henry Zhao: If you have questions, if you have additional ideas, anything here, you can add.
283 00:37:59.620 ⇒ 00:38:05.100 Sezim Zhenishbekova: Okay, perfect. Sounds good. Thank you, Henry.
284 00:38:05.310 ⇒ 00:38:05.860 Sezim Zhenishbekova: Yes.
285 00:38:05.860 ⇒ 00:38:06.869 Henry Zhao: Look forward to working together.
286 00:38:06.870 ⇒ 00:38:08.060 Sezim Zhenishbekova: Likewise.
287 00:38:08.590 ⇒ 00:38:11.469 Sezim Zhenishbekova: Okay, and let me know how the call with Robert goes.
288 00:38:11.470 ⇒ 00:38:13.480 Henry Zhao: Oh yeah, I’ll let you know what his feedback is.
289 00:38:13.480 ⇒ 00:38:14.400 Sezim Zhenishbekova: Okay, amazing.
290 00:38:14.460 ⇒ 00:38:19.910 Henry Zhao: Well, yeah. As many questions if you have any questions along the way, okay? Okay, sounds good. Thank you. Bye.