Meeting Title: Brainforge x Amble Phase 2 Date: 2026-04-08 Meeting participants: Zoran Selinger, Zak Casey, Robert Tseng
WEBVTT
1 00:00:49.800 ⇒ 00:00:52.370 Zak Casey: Testing, testing. Oh, cool, you can.
2 00:00:52.660 ⇒ 00:00:53.670 Zak Casey: Hey, Zoran.
3 00:00:55.940 ⇒ 00:00:56.750 Zoran Selinger: Hi, Zach.
4 00:00:57.690 ⇒ 00:00:58.500 Zak Casey: How are you, mate?
5 00:00:58.720 ⇒ 00:01:00.260 Zoran Selinger: Yeah, yeah, good.
6 00:01:01.670 ⇒ 00:01:06.870 Zoran Selinger: Good, yeah, actually, having…
7 00:01:07.200 ⇒ 00:01:10.239 Zoran Selinger: A lot of fun with our project as well.
8 00:01:12.120 ⇒ 00:01:14.250 Zak Casey: Yeah, me too, man. It’s,
9 00:01:14.770 ⇒ 00:01:18.590 Zak Casey: Yeah, it’s a fun learning experience, like, all of the edge tracking stuff, like…
10 00:01:18.940 ⇒ 00:01:25.000 Zoran Selinger: Yeah, it’s really fun, and yeah, we have this challenge, as well now.
11 00:01:25.220 ⇒ 00:01:32.329 Zoran Selinger: I was… I was hoping you can literally tick a box to make the transaction ID to be in the URL.
12 00:01:32.760 ⇒ 00:01:35.280 Zoran Selinger: Inside the desk, somewhere.
13 00:01:35.680 ⇒ 00:01:41.269 Zoran Selinger: And that could… that would be enough, essentially.
14 00:01:41.270 ⇒ 00:01:46.939 Zak Casey: I’m 99% sure. I mean, let me just double check, like, I’m logged into Basque right now, I can see if there’s.
15 00:01:46.940 ⇒ 00:01:47.509 Zoran Selinger: and constantly.
16 00:01:47.510 ⇒ 00:01:49.840 Zak Casey: big than they set up that I haven’t seen before.
17 00:01:58.320 ⇒ 00:01:59.350 Zak Casey: No.
18 00:01:59.680 ⇒ 00:02:00.449 Robert Tseng: Hey, guys.
19 00:02:00.970 ⇒ 00:02:01.450 Zoran Selinger: Hi, hi.
20 00:02:01.450 ⇒ 00:02:03.820 Zak Casey: I think so. I, is it…
21 00:02:03.980 ⇒ 00:02:05.280 Zak Casey: Is that Robert? Yeah, hi, Robert.
22 00:02:05.280 ⇒ 00:02:05.650 Robert Tseng: Yeah.
23 00:02:08.460 ⇒ 00:02:10.239 Zak Casey: Yeah, no, I don’t see an option anywhere.
24 00:02:10.699 ⇒ 00:02:11.229 Zoran Selinger: Yeah, okay.
25 00:02:11.230 ⇒ 00:02:16.100 Zak Casey: I am a little bit concerned about it also from, like, a privacy perspective. Like, I’ve recently been doing our…
26 00:02:16.340 ⇒ 00:02:23.139 Zak Casey: Like, consent layer, and really just kind of everything privacy-related.
27 00:02:23.540 ⇒ 00:02:28.449 Zak Casey: And passing transaction ID, even in the URL, isn’t…
28 00:02:28.800 ⇒ 00:02:37.379 Zak Casey: really, really compliant. The great thing about cookies is that our consent layer can stop a cookie if someone opts out.
29 00:02:37.660 ⇒ 00:02:44.670 Zak Casey: But a URL parameter, as far as I know, our constant layer isn’t going to remove a parameter from
30 00:02:45.030 ⇒ 00:02:48.090 Zak Casey: From a URL, so I prefer to keep it all…
31 00:02:48.380 ⇒ 00:02:55.460 Zak Casey: information that could be classified as remotely sensitive in a cookie, as opposed to the URL where possible.
32 00:03:00.510 ⇒ 00:03:07.690 Zoran Selinger: Yeah, I mean, yeah, we’ll… let’s look into that together, and we’ll, we’ll see what’s…
33 00:03:09.700 ⇒ 00:03:14.530 Zoran Selinger: kind of what the best course there is. So, Robert, just a little bit of…
34 00:03:14.830 ⇒ 00:03:17.080 Zoran Selinger: An update on, on this.
35 00:03:17.190 ⇒ 00:03:19.200 Zoran Selinger: So they, they don’t have…
36 00:03:19.460 ⇒ 00:03:31.240 Zoran Selinger: transaction parameters in the URL, right? So we are, so I flagged this at the beginning, we had to set up a cookie,
37 00:03:31.390 ⇒ 00:03:46.019 Zoran Selinger: we did it through GTM, which is obviously not fully, fully, functional when, you know, consent is, is not given, things like that, right? So, Zach, set up
38 00:03:46.890 ⇒ 00:03:59.500 Zoran Selinger: setting up a cookie on… on the server side for their custom intakes, but Basque intakes are still… that’s still, something that we need to figure out, how to do it on… on Bask intakes.
39 00:03:59.730 ⇒ 00:04:04.950 Zoran Selinger: So we can essentially avoid this setting in GTM.
40 00:04:06.130 ⇒ 00:04:10.270 Zak Casey: Yeah, it’s mostly just an issue for things like incognito, ad blockers, etc.
41 00:04:10.270 ⇒ 00:04:10.760 Zoran Selinger: Yeah.
42 00:04:11.690 ⇒ 00:04:12.250 Robert Tseng: Yeah.
43 00:04:13.820 ⇒ 00:04:22.820 Zak Casey: It was when I was testing the… the Everflow tracking, because I had set up the Everflow tracking, and I did it based off of the Edge system that you set up, but when I was testing the…
44 00:04:23.010 ⇒ 00:04:24.900 Zak Casey: implementation for Overflow.
45 00:04:25.290 ⇒ 00:04:33.210 Zak Casey: I was, like, testing in incognito, and I noticed that the AM underscore TID cookie
46 00:04:33.780 ⇒ 00:04:38.250 Zak Casey: It just doesn’t get set if you make a purchase in incognito.
47 00:04:39.590 ⇒ 00:04:43.259 Zak Casey: Which, to be fair, might have been hard for you to tell, because I don’t think you’ve…
48 00:04:43.960 ⇒ 00:04:46.610 Zak Casey: Probably been able to do test purchases, right?
49 00:04:46.610 ⇒ 00:04:47.459 Zoran Selinger: Yeah, exactly.
50 00:04:47.460 ⇒ 00:04:49.339 Zak Casey: Many had a limited amount of them.
51 00:04:50.840 ⇒ 00:04:58.209 Zoran Selinger: Yeah, I see you are keeping track with setting… the consent being set up in GTM.
52 00:04:59.590 ⇒ 00:05:06.150 Zak Casey: Yeah, so what… so we’ve moved away from… We were using CookieBot.
53 00:05:06.910 ⇒ 00:05:15.550 Zak Casey: Cookiebot… or was it Cookie Yes, sorry, Cookie Yes, we’re using Cookie Yes, that was via QTM. I’ve now removed that completely, so for Ramble, there shouldn’t be any…
54 00:05:15.930 ⇒ 00:05:19.729 Zak Casey: consent configured at the GTM level, because we’re using Transcend.
55 00:05:21.520 ⇒ 00:05:28.930 Zak Casey: Transcend is… is… I don’t know if you’re familiar with… familiar with it, but it’s basically a consent layer. You put some code on your website.
56 00:05:29.120 ⇒ 00:05:32.620 Zak Casey: And then it essentially holds all requests
57 00:05:33.130 ⇒ 00:05:38.260 Zak Casey: In kind of a layer in between, you know, for example, the client.
58 00:05:38.420 ⇒ 00:05:47.849 Zak Casey: And the server, so, like, for example, sending data to Google Ads, for example, and it will only send that data once someone consents, for example, or whatever you set the consent to.
59 00:05:47.850 ⇒ 00:05:58.950 Zoran Selinger: Oh, so they… they… they store it and wait for a consent, so you… we do… we do not lose that… that first… potentially do not lose those first,
60 00:05:59.300 ⇒ 00:06:02.270 Zoran Selinger: Requests, events, while we’re.
61 00:06:02.270 ⇒ 00:06:03.390 Zak Casey: Exactly, yeah.
62 00:06:04.030 ⇒ 00:06:04.700 Zak Casey: ways to…
63 00:06:04.700 ⇒ 00:06:05.270 Zoran Selinger: It’s called…
64 00:06:05.560 ⇒ 00:06:13.399 Zak Casey: it applies to every request and every cookie, so even things outside of GTM that are recurring, like, you have control over that.
65 00:06:14.280 ⇒ 00:06:16.220 Zoran Selinger: That’s very cool, yeah.
66 00:06:18.880 ⇒ 00:06:20.330 Zoran Selinger: Okay, good.
67 00:06:20.330 ⇒ 00:06:27.179 Robert Tseng: Well, I mean, Zoran, since we’re already talking about, kind of what else is remaining, you know, I just got this call going because, you know, officially we’re…
68 00:06:27.370 ⇒ 00:06:42.189 Robert Tseng: wrapping up by next week, and so I know we’re coming at the end, we still have to get TikTok up, but, you know, Zoran’s gonna probably just share a little bit about what else needs to be done, and what he… what he sees kind of coming up ahead. And then, we did some kind of just, like, broad
69 00:06:43.030 ⇒ 00:06:53.750 Robert Tseng: data discovery, I guess, and yeah, there’s some insights that I shared in the deck. I’m happy to walk through some of those things, just to try to spot opportunities where we can continue to help Amble.
70 00:06:55.270 ⇒ 00:07:01.130 Robert Tseng: So yeah, I mean, I guess, Lauren, since you already… you already started, might as well just… you can… you can keep going.
71 00:07:01.130 ⇒ 00:07:06.480 Zoran Selinger: So, yeah, so we just, we just mentioned, we just mentioned that,
72 00:07:06.570 ⇒ 00:07:24.719 Zoran Selinger: one tweak that we’ll… we’ll need to make for… for Basque intakes, so that’s… that’s remaining. Essentially, that will… that will give us a little bit more, more fidelity with the data, than we have at the moment, which is high anyway, so…
73 00:07:24.750 ⇒ 00:07:33.309 Zoran Selinger: It’s a… it’s, it’s a slight… it’s gonna be a slight improvement. But really, we’re doing, anyway.
74 00:07:34.070 ⇒ 00:07:37.500 Zoran Selinger: And yeah, we have a still, still, we have TikTok.
75 00:07:37.690 ⇒ 00:07:41.010 Zoran Selinger: reverse ETL, part, to do.
76 00:07:41.350 ⇒ 00:07:49.840 Zoran Selinger: So that’s what’s been scoped, so far. And then everything else is, so going forward.
77 00:07:49.980 ⇒ 00:07:55.969 Zoran Selinger: Managing… managing this system is… is about, you know, a little bit of…
78 00:07:56.420 ⇒ 00:08:00.960 Zoran Selinger: Best practices on… on the data, adding…
79 00:08:01.450 ⇒ 00:08:13.570 Zoran Selinger: changing parameter, cookies, URL, like, grabbing new URL parameters, or cookies, they will change in whatever names, and then, obviously.
80 00:08:13.950 ⇒ 00:08:22.660 Zoran Selinger: Adding new platforms will… will mean, you know, work on… work on essentially every bit of the system.
81 00:08:22.810 ⇒ 00:08:33.149 Zoran Selinger: coming from the… from the worker to the database, and then anything downstream of that, so any reporting and all of that stuff. So,
82 00:08:34.610 ⇒ 00:08:40.510 Zoran Selinger: Robert will probably talk more about, about… the…
83 00:08:41.409 ⇒ 00:08:44.349 Zoran Selinger: Activating this data a little bit further.
84 00:08:44.350 ⇒ 00:09:02.460 Zoran Selinger: Because, like, reverse ETL and, like, initial modeling is really just the first part. This really, especially when we… we do collect, identifiers from all those other systems, that data is highly, highly mergeable, too.
85 00:09:02.640 ⇒ 00:09:04.860 Zoran Selinger: To, you know, post-hoc data, and…
86 00:09:04.970 ⇒ 00:09:11.280 Zoran Selinger: And then, you can imagine, you can do a lot. You can do a lot with… with that.
87 00:09:11.860 ⇒ 00:09:17.490 Zoran Selinger: So that’s, and that is… that is basically it.
88 00:09:18.160 ⇒ 00:09:36.039 Zoran Selinger: maintaining the workflows, the reverse CTLs for now, and then adding new ones to activate more things. You guys have more channels than you requested to be activated right now, so that’s,
89 00:09:36.390 ⇒ 00:09:43.800 Zoran Selinger: That’s in the scope. So that’s probably… Those are the next steps.
90 00:09:44.110 ⇒ 00:09:45.070 Zoran Selinger: For us.
91 00:09:46.430 ⇒ 00:10:01.889 Robert Tseng: Okay, cool. Yeah, so I mean, in summary, just… there’s… there’s some maintenance component, and obviously this is not a set-and-forget thing. We continue to add new channels, and yeah, as you make adjustments to different, like, web parameters, like, we’re gonna have to… we would have to tweak it, or…
92 00:10:01.890 ⇒ 00:10:08.350 Robert Tseng: So I think there’s probably some, like, base, like, maintenance that, like, obviously will be lower than…
93 00:10:08.430 ⇒ 00:10:18.609 Robert Tseng: less effort than what it was to kind of get started. And, like, I’m just interested in trying to stack a lot of different things so we can try to, you know, build the best package to present to you and your team.
94 00:10:18.940 ⇒ 00:10:25.160 Robert Tseng: That we can continue to stay on and keep kind of pushing, pushing forward on helping you unlock value from this data.
95 00:10:25.500 ⇒ 00:10:32.230 Robert Tseng: So, I don’t know if you got a chance to take a look at this, but I’ll start probably just kind of high level at this,
96 00:10:32.380 ⇒ 00:10:44.120 Robert Tseng: From this, outline. So, you know, we’ve worked with a few other GLP companies, so we have maybe some, you know, sense of an internal benchmark of, like, what we think
97 00:10:44.430 ⇒ 00:10:54.939 Robert Tseng: you know, Ambul’s doing well, where things could be improved. So I think this is, like, the one slide, if you need to share with anybody on where we think the opportunities are, we could find.
98 00:10:55.090 ⇒ 00:10:55.950 Robert Tseng: So…
99 00:10:56.290 ⇒ 00:11:00.850 Robert Tseng: Yeah, I think I’ll just kind of briefly run through them, I’ll let you read the text in your own time, but…
100 00:11:00.970 ⇒ 00:11:06.280 Robert Tseng: From, like, a product portfolio perspective, obviously, most of your… your…
101 00:11:06.700 ⇒ 00:11:22.909 Robert Tseng: less diversified than some of the clients we work with. And, you know, primary source of revenue is TERS. And, you know, the way that you expand products right now is you’re just kind of, like, adding on… you’re just incrementally creating additives. And it’s interesting, I’ll point to a couple
102 00:11:23.030 ⇒ 00:11:31.759 Robert Tseng: Charts, here, you know, showing kind of, like, the impact of, you know, just basically lodging new iterations of the same base drug.
103 00:11:32.170 ⇒ 00:11:35.600 Robert Tseng: And, you know, it’s… I’m not quantifying.
104 00:11:35.600 ⇒ 00:11:36.140 Zak Casey: Fine one.
105 00:11:36.140 ⇒ 00:11:36.960 Robert Tseng: cannibal…
106 00:11:37.870 ⇒ 00:11:40.860 Zak Casey: So… but those aren’t really separate.
107 00:11:41.170 ⇒ 00:11:47.070 Zak Casey: Products, or I guess the… so the bundles, so for example, Leftside Plus and AND, so Zevide Plus, we’re all in.
108 00:11:47.210 ⇒ 00:11:50.399 Zak Casey: Those would be considered separate products, but, like, the different additives.
109 00:11:50.480 ⇒ 00:12:07.920 Zak Casey: That is not even… like, we don’t advertise the additives at all, that’s purely almost operational, in terms of, like, we’re using different pharmacies at different times. Sure. When we switch pharmacy, we might add a new additive, for example, or, you know, more of the share. Let’s say, for example.
110 00:12:09.410 ⇒ 00:12:21.749 Zak Casey: Pharmacy A and Pharmacy B, right? Let’s say, for example, Pharmacy B costs us a better deal, so we start running more volume through pharmacy B, so more might go towards B12. It’s not like, I wouldn’t consider the additives actually different products, or that…
111 00:12:22.560 ⇒ 00:12:23.300 Zak Casey: Hmm.
112 00:12:24.430 ⇒ 00:12:28.689 Zak Casey: Or that they would really impact Revenue in that way, anyway.
113 00:12:29.340 ⇒ 00:12:33.170 Robert Tseng: I see. So, kind of what I’m hearing is the…
114 00:12:33.310 ⇒ 00:12:38.869 Robert Tseng: Having this basket of tourist products is really just more operate… because operational.
115 00:12:38.870 ⇒ 00:12:39.490 Zak Casey: Yes, operational.
116 00:12:39.490 ⇒ 00:12:40.190 Robert Tseng: Yeah.
117 00:12:40.510 ⇒ 00:12:51.220 Robert Tseng: yeah, not really, like, strategic that you’re just trying to launch, like, every version of TERS that you can, so… Oh, no, definitely not. Okay, heard that. I think it’s… we can still see that
118 00:12:51.400 ⇒ 00:12:59.590 Robert Tseng: I guess we went more granular than maybe you guys typically look at your business at, but maybe still interesting to just see that, you know, I think
119 00:13:00.360 ⇒ 00:13:06.510 Robert Tseng: versions of the tourist products is the most common product launch that you guys have, and…
120 00:13:06.610 ⇒ 00:13:14.459 Robert Tseng: Yeah, I think the takeaway here so far is just that, like, yeah, it doesn’t look like it’s really expanding, kind of, the…
121 00:13:14.520 ⇒ 00:13:33.519 Robert Tseng: it’s not… it’s not growing the pie for, like, tourist capital. This is probably the same customers over and over again, or, like, the same… same buyers, and watching different variations isn’t really, kind of, isn’t really growing… growing that share. So, I think we were kind of curious, like, okay, well, beyond, kind of, like, from a tourist perspective, like, what other products are you guys launching?
122 00:13:33.770 ⇒ 00:13:37.439 Robert Tseng: And, you know, we’ve, we’ve, we’ve, we’ve looked into…
123 00:13:37.890 ⇒ 00:13:44.830 Robert Tseng: I mean, we drilled into TERS for this particular chart, but there are some other slides here that kind of look more into,
124 00:13:45.230 ⇒ 00:13:49.940 Robert Tseng: The overall product mix of… Yeah, just seeing that.
125 00:13:50.090 ⇒ 00:13:54.210 Robert Tseng: You know, you’re lodging kind of varying levels of…
126 00:13:54.680 ⇒ 00:13:57.220 Robert Tseng: of new SKUs, I guess,
127 00:13:57.460 ⇒ 00:14:10.219 Robert Tseng: So, there are recent product groups that you’re experimenting with, we can see, and, you know, this year looks like, was the start of Q1, you kind of launched a bunch of these products. Yeah, I think there are just other trends that we’re seeing from other competitors around
128 00:14:10.350 ⇒ 00:14:27.549 Robert Tseng: microdosing or, you know, non-GLP-1 kind of, like, products as well that are being thrown into the mix to try to diversify that product portfolio. So, is that… is that something that you guys are really leaning into now? Because I know that this… that’s not really been the story for the past year.
129 00:14:28.460 ⇒ 00:14:33.750 Zak Casey: Yeah, I don’t know how much you guys know, and I don’t know how much I can tell you, but we have, we have 3 brands.
130 00:14:34.200 ⇒ 00:14:36.659 Zak Casey: Yeah. We just launched microdosing on…
131 00:14:37.200 ⇒ 00:14:42.850 Zak Casey: Oh, you did? Oh, you did? On another round. Okay. Yeah, I don’t know why, I’m gonna ask now quickly.
132 00:15:00.840 ⇒ 00:15:01.870 Zak Casey: Okay, well, it sounds like.
133 00:15:01.870 ⇒ 00:15:05.900 Robert Tseng: We only look at, you know, when we have a third of the story, so…
134 00:15:08.060 ⇒ 00:15:10.940 Zak Casey: Yes, yeah, in the context of this, yeah.
135 00:15:11.300 ⇒ 00:15:16.300 Robert Tseng: Yeah, so MinuteMD is, like, the holdco, and then there’s, like, 3 sub-brands underneath? Is that what it is?
136 00:15:17.300 ⇒ 00:15:29.890 Zak Casey: Kind of. I mean, technically, MinuteMD is the fourth telehealth, like, MinuteMD is technically a… a real telehealth with real patients. It’s just, like, the legacy, legacy one. Like, it’s… it’s very old, it’s on…
137 00:15:31.340 ⇒ 00:15:40.970 Zak Casey: like, open loop instead of Basque. It had… we hadn’t done any marketing for it in probably over 3 years, so that’s… it’s not really the Holding Co,
138 00:15:41.490 ⇒ 00:15:46.440 Zak Casey: But yes, it is… I guess you could look at it as the parent company, kind of.
139 00:15:46.830 ⇒ 00:15:47.460 Robert Tseng: Sure.
140 00:15:47.860 ⇒ 00:15:53.280 Zak Casey: It’s not really that structured, we’re kind of a little bit more freestyle over here, I guess.
141 00:15:53.280 ⇒ 00:16:04.890 Robert Tseng: Okay. Yeah, no, that’s… that’s great, you know? Well, then I’ll kind of skip the strategy commentary. Seems like we’re missing gaps there. I mean, I’d be interested in seeing, like, how do we work with some of those other brands, too, you know? Like,
142 00:16:05.190 ⇒ 00:16:24.069 Robert Tseng: seems like the same work that we’ve been doing on this brand could probably support the other two brands that are active, so maybe that’s, you know, that’s something to note. But what I think could be helpful to share is still about around retention, repeat purchases, and then something about, kind of, like, what we’re seeing on margins. So…
143 00:16:24.140 ⇒ 00:16:25.620 Robert Tseng: I think…
144 00:16:25.960 ⇒ 00:16:31.280 Robert Tseng: Okay, well, revenue is pretty, pretty stagnant on, on the Amble brand, I think you probably know that.
145 00:16:31.600 ⇒ 00:16:41.909 Robert Tseng: So on the retention side, so what we’re seeing here is really that, you know, if we’re indexing, I mean, I don’t know if 4.3 is the right number, but I think 4, like, for every
146 00:16:42.070 ⇒ 00:16:53.519 Robert Tseng: for every 4 earned from returning customers is probably what we would expect. So, looks like the, you know, that mix is a little bit low, for what you guys are seeing, and…
147 00:16:53.620 ⇒ 00:17:03.519 Robert Tseng: So yeah, I think that this prompts me to want to double-click into what you guys are doing on the… from, like, a retention perspective. You know, we have some understanding of
148 00:17:03.570 ⇒ 00:17:15.599 Robert Tseng: you know, LTV, number of purchases, like, I think it’s about, like, 3 to 4, like, orders per typical customer. And then, yeah, just trying to figure out how do we, like.
149 00:17:15.599 ⇒ 00:17:28.629 Robert Tseng: increase this number, so more, more, more revenue is coming from specifically, like, returning customers. I don’t think it’s just, like, the ratio is lower than we would expect, but also that I think returning customer
150 00:17:28.700 ⇒ 00:17:36.290 Robert Tseng: revenue is just kind of flat. So, I wonder if there’s, you know, some… something missing about once someone’s not… finds that that…
151 00:17:36.570 ⇒ 00:17:55.990 Robert Tseng: that product is not working, you guys don’t really have a way to route them to a different program, or, you know, just further downstream, like, 3 or 4 months down the line, like, how are you continuing to, you know, cater the experience to the patients, and are there, like, cross-sell opportunities where you’re moving to different treatments? Do you have any context around that?
152 00:18:01.330 ⇒ 00:18:06.740 Zak Casey: I mean, that’s an interesting one, because, like, for example, if you were to compare us to an Eden.
153 00:18:07.180 ⇒ 00:18:12.909 Zak Casey: I can tell you that, you know, we have a very, very similar retention strategy and kind of, like, implementation.
154 00:18:14.500 ⇒ 00:18:17.580 Zak Casey: Unless they’ve done something drastic in the last, like, month or two, but…
155 00:18:17.970 ⇒ 00:18:23.369 Zak Casey: Outside of the last month or two, we would have a very, very… in a similar…
156 00:18:24.120 ⇒ 00:18:30.320 Zak Casey: situation. I wonder sometimes, like, what the… one of the things I’ve learned with Amber in particular, is…
157 00:18:30.650 ⇒ 00:18:37.199 Zak Casey: Like, Anvo is unique, at least relative to other past clients, in that it gains almost all of its customers through influencer marketing.
158 00:18:37.330 ⇒ 00:18:39.430 Zak Casey: Whereas most of our clients.
159 00:18:39.580 ⇒ 00:18:51.410 Zak Casey: usually GLP, sorry, Google Ads, you know, orientated, or some other paid channel. Because Amber is so influencer-orientated, I’ve noticed that, in general, that it just has very different…
160 00:18:51.580 ⇒ 00:18:57.109 Zak Casey: standards like our CAC, for example, is insanely low compared to other GLP-1 companies.
161 00:18:57.850 ⇒ 00:18:58.420 Robert Tseng: Yeah.
162 00:18:58.420 ⇒ 00:19:03.429 Zak Casey: But yeah, I wonder if that plays a part in retention, because I don’t think that the retention strategy
163 00:19:04.070 ⇒ 00:19:10.640 Zak Casey: Amble is overly different to other, like, for example, BAS clients, so if it is lower.
164 00:19:11.250 ⇒ 00:19:16.400 Zak Casey: That would be my first thought, but honestly, I don’t know, it could be… It could be,
165 00:19:16.620 ⇒ 00:19:29.659 Zak Casey: a handful of different things. I usually find as well that companies with better pricing have significantly better retention, so I’m surprised that our retention is actually lower than average, because I would say that Amazon’s pricing is probably still better than average.
166 00:19:31.110 ⇒ 00:19:42.120 Robert Tseng: Yeah, well, I don’t know if you’re getting your benchmarks from BASC, probably doing better, one of the… obviously one of the leading customers in the BASC portfolio, but just from the… from the companies we’ve worked with, so…
167 00:19:42.230 ⇒ 00:19:51.929 Robert Tseng: But yeah, I mean, that makes sense in terms of the customers that you’re bringing through the door, they’re coming from the influencer channel. You know, that mix is probably just different than what
168 00:19:52.170 ⇒ 00:19:55.609 Robert Tseng: everyone else is going after from Google Ads, and, you know.
169 00:19:55.610 ⇒ 00:19:55.990 Zak Casey: You’re looking.
170 00:19:55.990 ⇒ 00:19:57.150 Robert Tseng: your affiliate.
171 00:19:58.210 ⇒ 00:20:01.560 Zak Casey: Customers there are much more in tune with, kind of.
172 00:20:01.690 ⇒ 00:20:09.079 Zak Casey: like, competitors, etc, because they’re constantly seeing content from different influences in the space, promoting different brands. I think they’re more likely to switch.
173 00:20:09.220 ⇒ 00:20:17.260 Zak Casey: Whereas someone who, you know, is going on Google, for example, they might not even know any other brand exists except yours, because they just googled it, found yours.
174 00:20:17.390 ⇒ 00:20:27.550 Zak Casey: been happy enough with the service, so they haven’t looked elsewhere yet. Whereas if they’re already on TikTok, they’re already constantly being advertised at other brands’ offers, essentially.
175 00:20:27.550 ⇒ 00:20:28.120 Robert Tseng: Yeah.
176 00:20:28.620 ⇒ 00:20:36.449 Robert Tseng: who are you competing with for ad space on Influencer? If you guys are dominating on influencer marketing, like, do you know where you’re seeing patients switch to?
177 00:20:37.310 ⇒ 00:20:42.780 Zak Casey: I mean, that wouldn’t be a question for me, but, like, I know ShedRx is big on… on,
178 00:20:43.600 ⇒ 00:20:44.980 Zak Casey: Yeah, on TikTok.
179 00:20:45.300 ⇒ 00:20:49.250 Zak Casey: Okay. It’s been a while since I’ve looked at it, it’s not really my area.
180 00:20:49.870 ⇒ 00:20:51.230 Robert Tseng: Okay, got it.
181 00:20:52.010 ⇒ 00:21:00.659 Robert Tseng: Yeah, maybe I’ll just cut a couple… yeah, so, I mean, I think we just… we also, at very high level, just saw some of the spend kind of decisions.
182 00:21:00.890 ⇒ 00:21:17.239 Robert Tseng: that you guys made, you know, obviously, shifting Google’s affiliate. I guess influencer’s not in here, because we don’t have TikTok, so I think this is a little bit misinformed. So, yeah, I think all it really shows here is that, okay, you’re spending significantly more on affiliate as a percentage of ad spend, and then.
183 00:21:17.240 ⇒ 00:21:17.670 Zak Casey: Give us.
184 00:21:17.670 ⇒ 00:21:18.320 Robert Tseng: you add influencers.
185 00:21:18.320 ⇒ 00:21:19.470 Zak Casey: Southeast.
186 00:21:19.640 ⇒ 00:21:22.710 Zak Casey: That affiliate spend is actually influencer spend, I believe.
187 00:21:23.360 ⇒ 00:21:25.020 Robert Tseng: Oh, it’s mostly affiliate, okay.
188 00:21:25.020 ⇒ 00:21:28.269 Zak Casey: I don’t believe we do any affiliates for Anvo, so I don’t…
189 00:21:28.530 ⇒ 00:21:32.539 Zak Casey: influencer marketing, so that… I believe that is the influencer spend. I don’t know if the
190 00:21:32.870 ⇒ 00:21:35.060 Zak Casey: is, like, accurate historically, though.
191 00:21:35.220 ⇒ 00:21:39.310 Zak Casey: It was actually Rob that… that did it, not me. Yeah.
192 00:21:41.400 ⇒ 00:21:48.320 Robert Tseng: Okay, maybe it was mislabeled in the marts, maybe we just misinterpreted it, and it’s still missing spend for TikTok, so…
193 00:21:48.390 ⇒ 00:22:07.420 Robert Tseng: Yeah, I guess we could… I mean, that’s probably something to dial in a bit more. You know, we… last time we talked, we talked about, hey, do you have a… do you have a sense, like, do you have, like, reporting on… on MER? And, just, like, a good, you know, channel-by-channel, kind of look across the board of, like, where your dollars are going?
194 00:22:07.480 ⇒ 00:22:16.390 Robert Tseng: I mean, we obviously don’t have access to it, so we try to at least use the data that was there, and this is, at least on the paid side, what we found. But yeah, I mean, I think that…
195 00:22:16.490 ⇒ 00:22:17.920 Robert Tseng: Seems like it would be…
196 00:22:18.600 ⇒ 00:22:34.779 Robert Tseng: something that you guys probably need to build, if… because we… with limited context, just going in, using the models as they are, like, this is what we pulled out, and if it’s… if it’s wrong, then I imagine that whoever’s looking at the reports off of these tables is probably wrong, too.
197 00:22:36.260 ⇒ 00:22:46.590 Zak Casey: Yeah, well, most of… almost stored on marketing reports are actually via, like, Northbeam and Wicked, so most of it’s not relying on the BigQuery data, but yeah, I mean, that’s something I’m working on at the moment, is trying to clean up the…
198 00:22:47.180 ⇒ 00:22:52.270 Zak Casey: the BigQuery and the DBT, and really just the entire GCP for all three brands.
199 00:22:52.740 ⇒ 00:22:53.700 Zak Casey: Yeah.
200 00:22:55.090 ⇒ 00:22:57.690 Zak Casey: But yeah, I agree.
201 00:22:57.890 ⇒ 00:22:59.959 Zak Casey: I think it needs to be cleaned up a lot.
202 00:23:00.300 ⇒ 00:23:00.890 Zak Casey: improved.
203 00:23:00.890 ⇒ 00:23:01.520 Robert Tseng: Okay.
204 00:23:02.250 ⇒ 00:23:12.919 Robert Tseng: Maybe the last part I’ll touch for now is just, margin. So, obviously, margins have kind of, like, dipped since the end of 2024. It’s been hovering around sub-50%,
205 00:23:13.110 ⇒ 00:23:24.169 Robert Tseng: yeah, obviously think this could be better. I mean, Eden’s is, like, 55, other, other brands, some brands, like, maybe around, around this range, but, you know, by looking into…
206 00:23:24.170 ⇒ 00:23:34.199 Robert Tseng: specifically your product mix. Yeah, I mean, this makes sense, TERS being the highest margin product. I mean, as you guys are testing other ones, probably you’ll, you’ll,
207 00:23:34.300 ⇒ 00:23:36.289 Robert Tseng: Analyst mix, but.
208 00:23:36.990 ⇒ 00:23:38.900 Zak Casey: These are, these are estimated cogs.
209 00:23:39.390 ⇒ 00:23:52.259 Robert Tseng: Estimated, yeah, just because this is all we were able to get from BigQuery. We made some assumptions around what we typically see in terms of the fee structure from BASC, in terms of doctor fees, shipping, etc.
210 00:23:52.590 ⇒ 00:23:55.059 Robert Tseng: So yeah, I mean, I think, like.
211 00:23:57.300 ⇒ 00:24:12.000 Robert Tseng: I don’t know if this is the exact… these are all… these are all, like, valid assumptions for… for you guys, but assuming they are, then, yeah, I think, like, one… one way that we’ve been able to kind of help impact margins is we basically rebuilt, like.
212 00:24:12.150 ⇒ 00:24:13.580 Robert Tseng: a system.
213 00:24:13.750 ⇒ 00:24:31.109 Robert Tseng: we’ve helped brands, like, rebuild, like, a version of a BASC that they can run… at least route some of their orders to, and that has had a meaningful impact on margin. Like, you’re able to get back at least 5 percentage points, just from that. And, you know, I don’t know if you guys have exclusive
214 00:24:31.280 ⇒ 00:24:47.399 Robert Tseng: it starts to make sense if you have, like, a particular pharmacy you like working with, you want to go direct with them, you want to move them off the BAS platform, and then you can… I mean, this is a bigger project. It probably takes, like, at least… it took us about 3 months to build, so…
215 00:24:47.590 ⇒ 00:25:01.060 Robert Tseng: I would say, like, kind of 3-6 month, like, from start to finish to really see… see that lifted margin. But if that’s, you know, something that you guys want to aggressively go after, I do think that’s the best way to impact… impact your margins.
216 00:25:01.640 ⇒ 00:25:04.320 Robert Tseng: Yeah.
217 00:25:05.920 ⇒ 00:25:11.040 Zak Casey: Yeah, for us, at the moment, we’re… we’re very happy with Basque, actually.
218 00:25:11.520 ⇒ 00:25:15.879 Zak Casey: And I’d say we, you know, have a pretty committed relationship with them.
219 00:25:15.990 ⇒ 00:25:16.920 Zak Casey: Sure.
220 00:25:17.200 ⇒ 00:25:24.969 Zak Casey: But yeah, I mean, it’s interesting, but yeah, I don’t think that would be something we’d be focused on at the moment. We actually recently tried another
221 00:25:25.320 ⇒ 00:25:31.879 Zak Casey: we tried Talegra, and it was just, a horrible experience. And after that, we’re very grateful for Vask now.
222 00:25:32.970 ⇒ 00:25:33.580 Robert Tseng: Okay.
223 00:25:34.260 ⇒ 00:25:40.770 Zak Casey: And so, yeah, our appetite to experiment outside of Basque has gone down a lot since trying Telegra.
224 00:25:41.380 ⇒ 00:25:41.960 Robert Tseng: Yeah.
225 00:25:44.770 ⇒ 00:25:50.169 Robert Tseng: Okay, well, yeah, I mean, the other stuff is not really all that urgent. I think I can just…
226 00:25:51.040 ⇒ 00:26:00.209 Robert Tseng: Pause there, maybe interesting to look through. Yeah, curious, like, yeah, where do you… where do you feel like you could see us, continuing to…
227 00:26:00.660 ⇒ 00:26:14.610 Robert Tseng: work with… work with you guys? Is it… is it in doing the same… same work across more brands? Do any of these areas feel like something that would be worth, kind of, connecting us to somebody else on the team to… to keep following up on? Seems like…
228 00:26:14.940 ⇒ 00:26:25.109 Robert Tseng: from margin expansion, if it’s not, like, changing systems, that’s fine. Like, maybe there are other things. Maybe even it’s just getting to that good operational view of, like, having a true sense of, like.
229 00:26:25.920 ⇒ 00:26:39.280 Robert Tseng: gross net margins, like, you know, having all that data really, like, locked into your warehouse, and I mean, at some point, having that level of… that, like, financial discipline.
230 00:26:39.590 ⇒ 00:26:45.290 Robert Tseng: Or is it on the lifecycle side, really wanting to see, like, how do you…
231 00:26:45.390 ⇒ 00:26:48.189 Robert Tseng: Take the data that, you know, now that we have
232 00:26:49.370 ⇒ 00:26:55.230 Robert Tseng: we’re dumping all this data into BigQuery. We could actually build out that golden customer data set.
233 00:26:55.350 ⇒ 00:27:07.019 Robert Tseng: Obviously you’re using it to retarget better… better customers, but then also, like, downstream, we’re able to take that to, whoever runs your lifecycle team, and to build
234 00:27:07.310 ⇒ 00:27:22.490 Robert Tseng: better audiences for them, so that they can go after specific, like, design a more personalized journey and, try to go after retention in that way. I think those seem like the biggest opportunities, but I guess, what do you think?
235 00:27:23.930 ⇒ 00:27:26.570 Zak Casey: Yeah, so, in regards to, like, margins, so we’re actually…
236 00:27:26.760 ⇒ 00:27:29.360 Zak Casey: working on COGS at the moment, so we should have COGS…
237 00:27:29.840 ⇒ 00:27:33.409 Zak Casey: Like, integrated into the data model within…
238 00:27:33.710 ⇒ 00:27:36.710 Zak Casey: the next few weeks. Okay.
239 00:27:37.530 ⇒ 00:27:40.310 Zak Casey: Or… I guess I’m just reading this quick.
240 00:27:41.790 ⇒ 00:27:44.450 Zak Casey: I think, I think in particular, retention stuff.
241 00:27:44.740 ⇒ 00:27:48.830 Zak Casey: Retention-related things, would probably be of interest.
242 00:27:49.340 ⇒ 00:27:53.950 Zak Casey: Yeah. And then I think in general, like, actually looking at the…
243 00:27:54.140 ⇒ 00:27:56.390 Zak Casey: Graphs that you guys have generated?
244 00:27:57.580 ⇒ 00:28:11.650 Zak Casey: Today, I think one of the issues we have here is kind of like the opposite of Eden. At Eden, you have constant requests and constant questions being asked, and you constantly, you know, are constantly creating charts to answer those questions. Here is… it’s kind of the opposite, in terms of…
245 00:28:12.070 ⇒ 00:28:13.830 Zak Casey: There’s no questions being asked.
246 00:28:14.110 ⇒ 00:28:18.080 Zak Casey: So you don’t explore… kind of…
247 00:28:18.940 ⇒ 00:28:23.789 Zak Casey: the data, I think, as, like, extensively as you could.
248 00:28:24.330 ⇒ 00:28:28.930 Zak Casey: Because those, you know, that stimulus, i.e. the question, is never there in the first place, right, to…
249 00:28:29.070 ⇒ 00:28:36.209 Zak Casey: to kind of generate the graph. So I’d be, I think, particularly interested once, like, I’m working on cleaning up the data model at the moment.
250 00:28:36.870 ⇒ 00:28:40.059 Zak Casey: Because I haven’t really been focused on the data side of the business for…
251 00:28:40.300 ⇒ 00:28:45.250 Zak Casey: I mean, for the last year, it’s only now that we’ve started hiring more people that I’m…
252 00:28:45.410 ⇒ 00:28:48.140 Zak Casey: Focusing back more on the data side of the business.
253 00:28:48.400 ⇒ 00:28:48.730 Robert Tseng: Yeah.
254 00:28:48.730 ⇒ 00:28:55.849 Zak Casey: Yeah, once I clean up the data model a little bit and integrate COGS, I think in particular, like, having you guys come in and…
255 00:28:56.820 ⇒ 00:29:03.609 Zak Casey: kind of do what you’ve done here, really, but maybe with more context, where you, like, look at areas.
256 00:29:03.900 ⇒ 00:29:08.419 Zak Casey: Because there’s inevitably gonna be… Like, blind spots.
257 00:29:09.370 ⇒ 00:29:10.100 Robert Tseng: Yeah.
258 00:29:10.100 ⇒ 00:29:14.009 Zak Casey: That we can’t see. Like, for example, some of this retention-related stuff.
259 00:29:14.850 ⇒ 00:29:20.690 Zak Casey: And yeah, I think, I think data visualization, actually, look, I think…
260 00:29:21.230 ⇒ 00:29:23.770 Zak Casey: Where you’ve answered a lot more questions.
261 00:29:24.110 ⇒ 00:29:31.879 Zak Casey: you know, your knowledge of answering those questions, like, as a collective, I think… there’s probably… a lot of…
262 00:29:32.350 ⇒ 00:29:45.740 Zak Casey: like, questions that haven’t been asked, but if we did provide the answers to them, that’d be helpful, if that makes sense. I mean, there’s a lot of graphs that we probably don’t know would be helpful, but if they were there, they would actually be very helpful. Some of the ones in this,
263 00:29:45.870 ⇒ 00:29:47.849 Zak Casey: In this special, for example.
264 00:29:49.270 ⇒ 00:30:08.840 Robert Tseng: Sure. Yeah, I mean, I think this was a pretty light exercise, like, we just, you know, I just gave some of the general opening kind of questions, had, like, analysts on my team just go in and start to rip these, but I think if I were to kind of describe this as, like, a… like, this is really a data insights function, you know, my last in-house role, like, you know, 4 years ago is pretty much
265 00:30:09.220 ⇒ 00:30:23.959 Robert Tseng: Yeah, I just, like, had a team of analysts under me. Each one was kind of, like, tasked with each part of the business, and I would basically help them, come up with… come up with the questions, and they would… they would put out either decks, memos, you know, whatever the… whatever the exception was.
266 00:30:23.960 ⇒ 00:30:30.199 Robert Tseng: To, to help, you know, like, I’m really just given, like, targets to hit, so it’s like.
267 00:30:30.650 ⇒ 00:30:48.539 Robert Tseng: make forecasts, you know, our… make our product forecasts, within… within 10% accuracy, where it was, like, within 25%, and I’ll figure out how to do that. Or if it’s like, hey, gross margin dip… dip below 60%, we need to get it back above… above 60%, like, figure out how to do that. So, like.
268 00:30:48.540 ⇒ 00:30:52.979 Robert Tseng: I think I’m used to taking those type of open-ended questions, and then figuring out, like, what
269 00:30:52.980 ⇒ 00:31:01.579 Robert Tseng: what, you know, breaking that down, figuring out what we need to do to do that. So that is, you know, where we prefer to work, in terms of, like.
270 00:31:01.820 ⇒ 00:31:21.719 Robert Tseng: yeah, I think that’s where a lot… most of the value is, like, I want to be able… I want you guys to be able to see our work hit… hit the P&L. So yeah, I think I… that… I’m aligned with that. Like, I think that would be… that’s more aligned with kind of the… our… our strong suit beyond the engineering work. So, I guess as far as, like, how to kind of put this proposal together.
271 00:31:22.070 ⇒ 00:31:34.289 Robert Tseng: Yeah, I know you guys operate more informally, maybe you’re not DEX or Memos people, like, what do you think I would need to put together in order to make this compelling pitch for you or for the decision maker?
272 00:31:35.730 ⇒ 00:31:39.900 Zak Casey: I think really, I mean, I’m the decision maker.
273 00:31:39.900 ⇒ 00:31:40.850 Robert Tseng: Okay, great.
274 00:31:40.850 ⇒ 00:31:44.729 Zak Casey: I think… really, I need to…
275 00:31:44.860 ⇒ 00:31:47.820 Zak Casey: Like, a couple weeks to, essentially clean up.
276 00:31:48.110 ⇒ 00:31:50.429 Zak Casey: the BigQuery and the dbt, like, get all of our…
277 00:31:51.230 ⇒ 00:31:55.230 Zak Casey: Our model’s kind of organized, like a,
278 00:31:55.590 ⇒ 00:31:59.899 Zak Casey: Like, at our business logic level as well, not just, like, an analytical level.
279 00:32:00.340 ⇒ 00:32:04.280 Zak Casey: And then once you’ve done that, ideally, what would happen is you guys would kind of…
280 00:32:04.740 ⇒ 00:32:07.860 Zak Casey: take a look at the data that you’ve got there. Like I said, it would have cogs in there.
281 00:32:08.220 ⇒ 00:32:14.750 Zak Casey: We’d have the ad spend data in there, organized a lot better, and all of the ad spend data, for example, rather than, you know, some of it missing and whatnot.
282 00:32:14.910 ⇒ 00:32:15.720 Zak Casey: Yeah.
283 00:32:15.830 ⇒ 00:32:22.020 Zak Casey: And then from there, like, you guys, like, do, like, an audit or whatever, basically take a look at where you think
284 00:32:22.930 ⇒ 00:32:28.899 Zak Casey: You know, essentially like this, but in… but in a… In more detail, I guess.
285 00:32:28.900 ⇒ 00:32:29.240 Robert Tseng: Yeah.
286 00:32:29.240 ⇒ 00:32:35.299 Zak Casey: Because, yeah, like, at the moment, I’m not… Sure, specifically, like…
287 00:32:36.290 ⇒ 00:32:39.769 Zak Casey: Where… where we’re leaving value on the table, if that makes sense.
288 00:32:40.440 ⇒ 00:32:42.980 Robert Tseng: Yeah. Okay. No, that makes sense.
289 00:32:42.980 ⇒ 00:32:43.830 Zak Casey: ones.
290 00:32:44.800 ⇒ 00:32:48.970 Zak Casey: Yeah. Like, just, like, at the moment, we don’t even have COGS integrated, right? So, like, that’s an obvious one, but…
291 00:32:49.600 ⇒ 00:32:50.200 Robert Tseng: Right.
292 00:32:51.370 ⇒ 00:33:08.360 Robert Tseng: Okay, and I know you guys have, like, Rob in-house already, and, like, you have some data engineering support there, so I guess if you would prefer us to just purely be kind of consumers of the data model, and just kind of go deeper on the analysis side, happy to do that. I think, obviously, you know, from working with us, we kind of span the full stack, so…
293 00:33:08.360 ⇒ 00:33:20.009 Robert Tseng: if you want us to be in there, kind of helping you, like, clean up EBT, bringing in the scaffolding that we’re used to running as well, like, happy to kind of… kind of set that up, as well, so I think that way.
294 00:33:20.190 ⇒ 00:33:24.120 Robert Tseng: I think the limiting factor for, you know.
295 00:33:24.310 ⇒ 00:33:28.050 Robert Tseng: Data model maintenance, is that it often falls on the key person that set it up.
296 00:33:28.210 ⇒ 00:33:39.449 Robert Tseng: But even the way that we’ve set it up for other clients now is that, like, we don’t want to be the only ones contributing, like, we make it so that other stakeholders can actually, like.
297 00:33:39.540 ⇒ 00:33:50.080 Robert Tseng: it’s just, like, a tighter feedback loop. Like, everybody ends up kind of contributing to the data models, obviously in a governed way, but, like, it’s, like, setting it up so that, there are more contributors, kind of.
298 00:33:50.180 ⇒ 00:34:01.219 Robert Tseng: helps… helps, you know, helps relieve… relieve that… relieve that pressure. But, if you prefer to kind of keep it under… under tight ropes for now, like, fully understand.
299 00:34:01.720 ⇒ 00:34:04.169 Robert Tseng: Yeah, I guess, like, what are your thoughts on that?
300 00:34:07.110 ⇒ 00:34:13.420 Zak Casey: Yeah, I guess, I guess, again, I’d be interested to see, like, once I’ve built out this, like.
301 00:34:14.250 ⇒ 00:34:18.030 Zak Casey: You know, framework of a data model, and kind of, like, the…
302 00:34:18.429 ⇒ 00:34:21.710 Zak Casey: Pillars and the boundaries of it and whatnot, like, just to…
303 00:34:22.130 ⇒ 00:34:23.770 Zak Casey: Like, the base of it, really.
304 00:34:24.270 ⇒ 00:34:26.590 Zak Casey: I’d be curious to see…
305 00:34:27.120 ⇒ 00:34:35.950 Zak Casey: you know, like, what you guys think then, basically. So yeah, I guess really, like, what I’m saying is, like, we need to get the house in order first internally over the next month.
306 00:34:36.070 ⇒ 00:34:39.789 Zak Casey: Then it’d be good for you guys to just take a look at really everything and see…
307 00:34:40.630 ⇒ 00:34:45.870 Zak Casey: You know, where you think there’s value, even if it’s, you know, the data model specifically, and how we’re…
308 00:34:46.210 ⇒ 00:34:51.820 Zak Casey: designing the data model. At the moment, Rob is actually more of, like, an ad hoc… Hmm.
309 00:34:52.440 ⇒ 00:34:56.909 Zak Casey: ad hoc, kind of, data engineer in-house, but, like, Rob isn’t…
310 00:34:57.030 ⇒ 00:35:01.420 Zak Casey: like, Rob definitely isn’t doing the data model, a lot of his work isn’t actually…
311 00:35:02.120 ⇒ 00:35:07.129 Zak Casey: even properly integrated into the data model, I’m going to integrate it in even, like, today, for example.
312 00:35:07.770 ⇒ 00:35:08.560 Zak Casey: Yeah.
313 00:35:08.560 ⇒ 00:35:13.190 Robert Tseng: I remember working with him. He’s more kind of just like a free agent going…
314 00:35:13.680 ⇒ 00:35:14.549 Zak Casey: Yeah, yeah, exactly.
315 00:35:14.550 ⇒ 00:35:19.969 Robert Tseng: I open the first connector, and then it doesn’t really get integrated into anything, but yeah.
316 00:35:19.970 ⇒ 00:35:23.370 Zak Casey: Yeah, I’m currently on Rob cleanup duty right now, so.
317 00:35:23.370 ⇒ 00:35:23.940 Robert Tseng: Yeah.
318 00:35:24.480 ⇒ 00:35:29.890 Zak Casey: But yeah, Rob’s more ad hoc. I’m hoping to bring in, like, some form of…
319 00:35:30.620 ⇒ 00:35:33.189 Zak Casey: Data engineer at some point to kind of…
320 00:35:34.010 ⇒ 00:35:36.700 Zak Casey: But I want to clean everything up first, myself.
321 00:35:37.160 ⇒ 00:35:44.050 Zak Casey: Sure. My role isn’t really data engineer, like, my role’s a lot more encompassing than that, so… Yeah.
322 00:35:46.210 ⇒ 00:35:49.130 Zak Casey: So yeah, I don’t know. Does that… does that make sense?
323 00:35:49.680 ⇒ 00:36:04.750 Robert Tseng: Yeah, makes sense. And, you know, I think we do that pretty well, and so I, you know, if you want us to be that, I could do that. Okay, so as far as, like, next steps, what I’ll do is, obviously we’re gonna finish out what we have. Probably by next week, I want to put something in front of you.
324 00:36:04.920 ⇒ 00:36:08.890 Robert Tseng: Yeah, like, around just kind of maintenance, continuing.
325 00:36:09.240 ⇒ 00:36:26.020 Robert Tseng: like, I… it sounds like scope is kind of pretty open, you guys don’t really necessarily need, like, fixed… as much kind of, like, hand-holding on setting the milestones and stuff, so I could pitch you just kind of, like, a continuation off of a retainer, you know, just on what I think would be the minimum for the
326 00:36:26.140 ⇒ 00:36:43.229 Robert Tseng: For the, for the maintenance, and then also just kind of teeing up some ideas for, like, where… where, you know, understanding that it probably won’t ramp… ramp up until you finish a data cleanup and you bring us in. But I do want to kind of, like, introduce some more people for my team, probably next time we meet.
327 00:36:43.420 ⇒ 00:37:00.400 Robert Tseng: So yeah, there’s… I would… I would staff an analyst here, and, you know, if you wanted us to actually do the data model maintenance, like, I would… I would staff a data engineer here. So this… right now, it’s just me and Zoran, or really just Zoran, like, I barely did anything, except for putting… putting together some of the stuff.
328 00:37:00.400 ⇒ 00:37:03.940 Robert Tseng: But yeah, we would increase from him to, like, probably 4 people.
329 00:37:03.940 ⇒ 00:37:06.419 Robert Tseng: is kind of how I see the opportunity here.
330 00:37:08.170 ⇒ 00:37:14.549 Zak Casey: Yeah, yeah, I’d be interested to see, like, kind of costs of that kind of stuff, right? Like, that’s what it’s all based on, right? It’s, like, costs.
331 00:37:15.060 ⇒ 00:37:16.060 Zak Casey: Sure.
332 00:37:17.930 ⇒ 00:37:23.709 Zak Casey: Because, yeah, like you say, like you guys, in theory, could be the data engineer higher, in a way.
333 00:37:24.000 ⇒ 00:37:26.630 Zak Casey: But yeah, it’s all a matter of…
334 00:37:26.830 ⇒ 00:37:31.399 Zak Casey: Both, you know, weighing up costs and… Benefits and whatnot, right?
335 00:37:31.580 ⇒ 00:37:40.420 Robert Tseng: Okay, sure, yeah. So I’ll put together a longer doc with kind of the pricing and everything in there. Yeah, and then we can kind of review it, review it next time.
336 00:37:41.200 ⇒ 00:37:42.409 Zak Casey: Alright, cool, sounds good.
337 00:37:43.400 ⇒ 00:37:44.600 Robert Tseng: Alright, exact.
338 00:37:45.180 ⇒ 00:37:45.670 Zoran Selinger: Thank you.
339 00:37:45.670 ⇒ 00:37:46.490 Zak Casey: Second.