Meeting Title: Brainforge x Eden Data Story Guidance Date: 2026-01-09 Meeting participants: Henry Zhao, Greg Stoutenburg
WEBVTT
1 00:00:10.410 ⇒ 00:00:11.850 Henry Zhao: Hey, Greg, how’s it going?
2 00:00:11.850 ⇒ 00:00:13.340 Greg Stoutenburg: Hey, good, how are you?
3 00:00:13.820 ⇒ 00:00:14.980 Henry Zhao: Good, thanks.
4 00:00:15.410 ⇒ 00:00:16.430 Greg Stoutenburg: Good Friday.
5 00:00:16.810 ⇒ 00:00:17.570 Henry Zhao: Good, yeah.
6 00:00:17.970 ⇒ 00:00:18.560 Greg Stoutenburg: Yeah.
7 00:00:19.450 ⇒ 00:00:27.050 Henry Zhao: I won’t keep you too long, just kind of wanted to run your, run your mind through some of these things, on just some quick guidance on, like, how I can…
8 00:00:27.200 ⇒ 00:00:36.249 Henry Zhao: basically tell the story with data. We got some help from Robert, too, and I think kind of our focus right now is just to talk about where the problems are.
9 00:00:36.250 ⇒ 00:00:37.210 Greg Stoutenburg: Yeah.
10 00:00:37.690 ⇒ 00:00:49.080 Henry Zhao: to know what we can test. I think he doesn’t really think I need to tell anything in terms of what do we expect the actual lift to be, because, like I said, I won’t know the lift until we actually do the experiment right, so…
11 00:00:49.080 ⇒ 00:00:57.640 Greg Stoutenburg: If I could just ask, so what’s the, what’s the gist of the Eden engagement? I don’t, I don’t know what Eden does, or what we’re doing for them.
12 00:00:58.330 ⇒ 00:01:03.169 Henry Zhao: Cool. So, since I just gave you access to Mixpanel, I think this is a good segue into it, okay?
13 00:01:03.170 ⇒ 00:01:03.739 Greg Stoutenburg: Okay, cool.
14 00:01:03.740 ⇒ 00:01:17.749 Henry Zhao: So, Eden, I don’t even know if you know this, they do, like, a lot of different drugs, so they do, like, GLP-1, muscle support, energy longevity, anything like hormone therapy, hair loss, those types of things.
15 00:01:18.000 ⇒ 00:01:20.600 Greg Stoutenburg: Garden of Eden, I get it, alright. Yeah.
16 00:01:20.600 ⇒ 00:01:29.110 Henry Zhao: Yep. Well, and what happens is, they do advertising, and then in order to get on a treatment, you have to fill out these intake forms.
17 00:01:29.360 ⇒ 00:01:30.340 Greg Stoutenburg: Yep.
18 00:01:30.340 ⇒ 00:01:35.700 Henry Zhao: So these forms ask a bunch of different questions, usually it’s, like, 23 different questions. You go in and you answer them.
19 00:01:36.250 ⇒ 00:01:39.210 Henry Zhao: Sometimes you answer the wrong question, so you have to go back.
20 00:01:39.480 ⇒ 00:01:56.109 Henry Zhao: And then you either get approved or you get denied, right? When you get approved, you get your order sent to the pharmacy, you pay for it already, and then the pharmacy then fills your order and sends you the order, and then you do your treatment, but then obviously we want them to renew, right? So I’m focused on 3 different things. One is…
21 00:01:56.110 ⇒ 00:02:00.980 Henry Zhao: Up top of funnel, so getting actual people to… the Tri-Eden website.
22 00:02:01.320 ⇒ 00:02:17.720 Henry Zhao: conversions, or getting people through these forms, so, like, analyzing these questions. Do people get stuck on something? Do they, like, drop off a lot after a certain question? And then, do they follow through with the treatment? Do they churn? Do they maybe not get approved by the pharmacy? Those are, I think, the three big pieces of Eden.
23 00:02:18.330 ⇒ 00:02:19.020 Greg Stoutenburg: Okay.
24 00:02:19.020 ⇒ 00:02:30.919 Henry Zhao: So, Mixpanel has a lot of that intake form stuff, so… I pulled this one data of, like, conversion by intake form, right? Because it would make sense, right? Like, each form has a specific, like, completion percentage.
25 00:02:30.920 ⇒ 00:02:44.740 Henry Zhao: So I pulled, like, intake started, which is, like, them starting the form. I broke it down by the URL piece that comes after the backslash, so each of these is a specific form, and the conversion is order completed, which is the event
26 00:02:44.740 ⇒ 00:03:03.679 Henry Zhao: for whenever they finish the order and pay for it, and so it gets sent to the pharmacy. I don’t know if they’ll actually go through with it, if they will actually get approved, but at least this shows, like, the user intent. So I was gonna say, like, look at these forms over here, like, these four products have, like, only a 10% completion rate, whereas this C0CHA has 22%.
27 00:03:03.680 ⇒ 00:03:06.969 Greg Stoutenburg: So I was just gonna go to, like, app… like, literally go to…
28 00:03:07.190 ⇒ 00:03:11.300 Henry Zhao: whatever the URL is, 4DSWO, and see what product that is.
29 00:03:11.540 ⇒ 00:03:11.930 Greg Stoutenburg: Good idea.
30 00:03:13.200 ⇒ 00:03:21.950 Henry Zhao: And so, it looks like it’s GLP-1, GLP whatever, right? So that one is not doing well, whereas C0THA is doing well.
31 00:03:22.880 ⇒ 00:03:26.310 Henry Zhao: So, C0THA, looks like it’s hair loss.
32 00:03:26.420 ⇒ 00:03:39.049 Henry Zhao: So hair loss is doing well, GLP is not doing well. If we were to improve GLP up to 22%, like hair loss, it would equate to, like, this much additional revenue. But I don’t know if we can get it up to 22%, you know what I mean? That’s where I’m kind of…
33 00:03:39.410 ⇒ 00:03:39.940 Greg Stoutenburg: Yep.
34 00:03:39.940 ⇒ 00:03:42.290 Henry Zhao: I’m like, what do I… how do I tell the story that way?
35 00:03:42.290 ⇒ 00:03:48.550 Greg Stoutenburg: Yeah, okay, and you just kind of like my input on, what to say to Eden.
36 00:03:48.600 ⇒ 00:04:03.709 Greg Stoutenburg: from right here, right? Yeah. Cool. I think probably… so not knowing any con… so thanks for that context, not knowing any context beyond that, the… my… my storyteller instinct goes,
37 00:04:06.090 ⇒ 00:04:23.050 Greg Stoutenburg: not only do I want to know if that 10% can become the 20%, right? Should the 22% actually… should that be my standard? Should that be what I expect? And then everything else is lagging beyond that? So I have that question. and then my other question is going to be.
38 00:04:23.080 ⇒ 00:04:26.230 Greg Stoutenburg: are these numbers accurate? In the sense of…
39 00:04:26.700 ⇒ 00:04:33.869 Greg Stoutenburg: you know, maybe that 22% has far fewer people clicking on it. After all, you know, there’s…
40 00:04:33.890 ⇒ 00:04:48.120 Greg Stoutenburg: I think there are… maybe I think, if I’m the executive, right? Maybe I have this suspicion, justified or not, that, fewer people come to my platform hoping for hair loss treatments than do for weight loss treatments.
41 00:04:48.120 ⇒ 00:04:54.320 Greg Stoutenburg: And, and I’ve got, you know, several different weight loss products. Maybe my weight loss products are those bottom
42 00:04:54.320 ⇒ 00:04:55.290 Greg Stoutenburg: 6.
43 00:04:55.740 ⇒ 00:05:04.690 Greg Stoutenburg: that are, you know, 12 or less, but if you add them all up, then collectively, it’s actually a lot more business. So I think I’d want to know those things. Does that make sense?
44 00:05:04.690 ⇒ 00:05:05.510 Henry Zhao: Yeah,
45 00:05:05.510 ⇒ 00:05:19.629 Greg Stoutenburg: So, I think what I’d want to do then is I would… I would… I’d use what you just said, which is look at each of those slugs and see what they actually represent, and then I would look at the total number of users that are beginning those sequences as well.
46 00:05:20.760 ⇒ 00:05:34.859 Greg Stoutenburg: You know what I mean? Because it could be that, again, this is sort of what I was hinting at before, right? That the top slug for hair loss could be, I don’t know, let’s pick a number, it’s, you know, it’s 122 completed, right? Whereas the bottom is,
47 00:05:34.920 ⇒ 00:05:41.409 Greg Stoutenburg: you know, 1,000 and 10% completed, that’s actually a lot more sales, you know what I mean? So…
48 00:05:41.410 ⇒ 00:05:43.100 Henry Zhao: Hopefully that is the case.
49 00:05:43.470 ⇒ 00:05:44.879 Greg Stoutenburg: Oh, cool, you got it right here.
50 00:05:44.880 ⇒ 00:05:49.340 Henry Zhao: 248 users. This one is, like, this one is a big one, though, 2,105.
51 00:05:50.060 ⇒ 00:05:56.119 Henry Zhao: So, it looks like, if it’s a big, big enough group, it’s gonna be around this 12-13% range.
52 00:05:56.300 ⇒ 00:05:57.620 Greg Stoutenburg: Yeah, yeah. That’s what I’m saying?
53 00:05:57.620 ⇒ 00:06:00.480 Henry Zhao: Like, it doesn’t give me confidence that, like…
54 00:06:00.610 ⇒ 00:06:02.539 Henry Zhao: I’m finding something useful, you know, for them.
55 00:06:02.780 ⇒ 00:06:03.310 Henry Zhao: Yeah.
56 00:06:03.580 ⇒ 00:06:04.780 Henry Zhao: That’s where I get stuck.
57 00:06:05.060 ⇒ 00:06:07.630 Greg Stoutenburg: Well, I mean, I don’t know that…
58 00:06:09.480 ⇒ 00:06:27.420 Greg Stoutenburg: I mean, I don’t know that for the client meeting, you actually have to have a solution here. I think something you could say is, we could… we could nuance the data in the way that we’re discussing right now, right? So… so one of the things that we just basically agreed on is that the most successful and the least successful are outliers.
59 00:06:28.260 ⇒ 00:06:28.800 Greg Stoutenburg: Right?
60 00:06:28.800 ⇒ 00:06:29.780 Henry Zhao: Yeah,
61 00:06:29.970 ⇒ 00:06:34.240 Henry Zhao: So, we can actually use… I probably want to show them… I probably want to show them only the non-outliers.
62 00:06:34.850 ⇒ 00:06:50.550 Greg Stoutenburg: Yeah. Or even show them all, and then say, you know, actually, kind of as a demonstration of your expertise and also Brainforge’s competence, right? Like, hey, this is what MixedPanel says, but we’ve got an insight here. We found there are differences from these top and bottom numbers.
63 00:06:50.780 ⇒ 00:06:53.220 Henry Zhao: And we think that your true median.
64 00:06:53.220 ⇒ 00:07:05.709 Greg Stoutenburg: is probably around 15%, or 14%, you know, you’ll look at it. It’s around 15% or 14%, so the challenge then will be, how do we improve
65 00:07:06.310 ⇒ 00:07:09.840 Greg Stoutenburg: How do we improve those numbers, and where are the drop-offs, right?
66 00:07:10.650 ⇒ 00:07:20.569 Greg Stoutenburg: Because, you know, as you noted, right, these are… this is just looking at the first step, and then 24 steps away. To my mind, actually, as far as, like.
67 00:07:20.970 ⇒ 00:07:27.499 Greg Stoutenburg: as far as, like, purchase intent goes, that’s incredible. I mean, like, a product tour, everybody bails on step two, you know what I mean?
68 00:07:27.500 ⇒ 00:07:28.120 Henry Zhao: Yeah.
69 00:07:28.120 ⇒ 00:07:35.299 Greg Stoutenburg: even finished, so the fact that users are this engaged means that they’ve got a lot of motivated potential customers, so,
70 00:07:35.520 ⇒ 00:07:37.669 Greg Stoutenburg: Yeah, I mean, that’s where my mind goes to.
71 00:07:38.160 ⇒ 00:07:50.809 Henry Zhao: Yeah. Another thing I wanted to propose… so, the conclusion here is I don’t need to propose a solution, but just showing the status quo right now, with the data points that you said to, like, be prepared for, would be enough at this initial stage.
72 00:07:51.140 ⇒ 00:07:54.100 Greg Stoutenburg: Yeah, I think for the initial stage, what I would do is, I would…
73 00:07:54.140 ⇒ 00:08:11.749 Greg Stoutenburg: take that and turn it into a problem, right? So, here’s what my initial findings say, and I’m gonna build up to, I know where to look for a solution for you. It’s gonna take more analysis, but to get to… let me show you why I think that.
74 00:08:11.800 ⇒ 00:08:26.500 Greg Stoutenburg: this is an outlier, this is also an outlier. You’re pretty consistently in a median of around, you know, 13-15%. Now I want to look at what those products are, who the users are that are making it through versus not.
75 00:08:26.500 ⇒ 00:08:39.180 Greg Stoutenburg: where the sticking points are, right? I mean, maybe they’ll find out that for all these forms, people just start dropping off at step 8, right? And only the really high intent ones go through. And the difference between 13% and 14%
76 00:08:39.179 ⇒ 00:08:54.409 Greg Stoutenburg: out of 2,000 users is, like, nobody, you know what I mean? So, yeah, I would just kind of propose some of those things, and say, these are all things that we can look at for you, and then, you know, down the line, start proposing some places where you’d experiment to improve them.
77 00:08:55.230 ⇒ 00:08:55.820 Henry Zhao: Yeah.
78 00:08:56.130 ⇒ 00:08:57.439 Henry Zhao: Okay, that makes sense.
79 00:08:57.870 ⇒ 00:08:58.460 Greg Stoutenburg: No.
80 00:08:58.990 ⇒ 00:09:11.839 Henry Zhao: The other thing I wanted to propose also is, like, retargeting and increasing the number of impressions, so the thing that I’ve noticed is they’ll advertise, like, a burst, so I’ll see a bunch of reading ads when I was, like, researching this client, and then there’s no more.
81 00:09:12.020 ⇒ 00:09:15.119 Henry Zhao: And then I started getting a lot of competitor,
82 00:09:15.220 ⇒ 00:09:31.409 Henry Zhao: ads, and I’m still getting them, like, even months later, so the chance of me going to a competitor is very high. So I thought I might say, like, you are doing an average of X impressions, but that’s not enough, because, like, the industry standard is, like, 5 to 7 impressions…
83 00:09:32.390 ⇒ 00:09:37.140 Henry Zhao: So here, like, 6 to 12 paid social impressions before first purchase.
84 00:09:37.140 ⇒ 00:09:37.580 Greg Stoutenburg: Yeah.
85 00:09:37.590 ⇒ 00:09:42.760 Henry Zhao: e-commerce. I was thinking of telling something like this, but then I was like, can I actually get…
86 00:09:43.640 ⇒ 00:09:47.709 Henry Zhao: the distribution of impression count before first purchase.
87 00:09:47.870 ⇒ 00:10:00.439 Henry Zhao: And I don’t know that I can have that. Like, I have this table that has, like, number of impressions and number of new customers, but I don’t know, like, each customer is seeing how many impressions before they actually purchase something, you know what I mean?
88 00:10:00.440 ⇒ 00:10:07.330 Greg Stoutenburg: Yeah, yeah, and that… yeah, that kind of stuff is always hard to… to tell. If I can ask, where does this data come from?
89 00:10:08.160 ⇒ 00:10:09.259 Henry Zhao: This is from Eden.
90 00:10:10.580 ⇒ 00:10:14.030 Greg Stoutenburg: Oh, Eden gave this to you, is this from their Google Analytics, or… Okay. Yeah.
91 00:10:16.230 ⇒ 00:10:22.659 Greg Stoutenburg: do you… is there a marketing person there you can talk to? I mean, I might just ask them, hey, the way you have your campaign set up.
92 00:10:23.210 ⇒ 00:10:25.110 Greg Stoutenburg: Like, what’s the structure of your campaigns?
93 00:10:25.930 ⇒ 00:10:45.230 Greg Stoutenburg: Like, what do you do for targeting? I mean, I think you’re asking… I think you’re asking a good question. Like, and even just the experience that you had of looking into it a little bit, getting a bunch of Eden content, and then no more might suggest that they’ve got some kind of, like, you know, blitz marketing going on, where they just, you know, depending on your search term, you just get this
94 00:10:45.570 ⇒ 00:10:47.039 Greg Stoutenburg: A couple of ads.
95 00:10:47.200 ⇒ 00:10:53.590 Greg Stoutenburg: And then that’s it, right? Once you’ve seen my 3 ads, that’s it. If you didn’t buy anything, you didn’t, you know,
96 00:10:54.350 ⇒ 00:11:06.240 Greg Stoutenburg: So I don’t know. Sorry if I sound like I’m meandering. I don’t have an answer here. I… I would be curious, because I think this kind of attribution is really difficult, I would ask them what their marketing campaign structure looks like.
97 00:11:06.790 ⇒ 00:11:07.370 Henry Zhao: Okay.
98 00:11:08.300 ⇒ 00:11:13.190 Henry Zhao: Cool. Anything else you think I should add to the slides, based on what you’ve read, like, when you looked through this for me?
99 00:11:13.850 ⇒ 00:11:16.850 Greg Stoutenburg: Yeah, so I think, I mean, I guess I just have,
100 00:11:17.190 ⇒ 00:11:24.329 Greg Stoutenburg: Well, yeah, like, a question and a thought. I think… I think my… well, my question is, what’s the status of this engagement? Like, is this a new engagement?
101 00:11:24.330 ⇒ 00:11:29.160 Henry Zhao: No, it’s our longest engagement and our biggest engagement. This was, like, our big oyster.
102 00:11:29.380 ⇒ 00:11:30.250 Greg Stoutenburg: Okay.
103 00:11:30.250 ⇒ 00:11:32.329 Henry Zhao: The beginning, yeah, so we want to do a good job with them.
104 00:11:32.510 ⇒ 00:11:39.019 Greg Stoutenburg: Okay, got it, cool. And is this, like, a new… was this from a new, SOW, or…
105 00:11:39.020 ⇒ 00:11:41.339 Henry Zhao: No, this is just the continued SOW.
106 00:11:41.340 ⇒ 00:11:43.829 Greg Stoutenburg: This is where we are. Okay.
107 00:11:45.420 ⇒ 00:11:57.540 Henry Zhao: Okay, looks like you made some structural changes, that’s cool. Yeah, before we were just doing, like, the attribution stuff for them, and, like, cleaning up stuff, running their Tableau reports. Now we want to, like, kind of add new things to them, like, so we can continue their business, you know?
108 00:11:57.750 ⇒ 00:12:00.309 Greg Stoutenburg: Right, right, right. No, that makes sense.
109 00:12:01.060 ⇒ 00:12:03.760 Greg Stoutenburg: Yeah, this sounds good. I think…
110 00:12:05.670 ⇒ 00:12:18.560 Greg Stoutenburg: Yeah, I think the only other thing I was gonna say is, I took another look at it, and I thought, some of these sections look like there was, like, just a lot packed in together, but now I can see that actually you broke out a lot of that stuff, so I no longer have that comment.
111 00:12:18.560 ⇒ 00:12:19.320 Henry Zhao: Okay, cool.
112 00:12:19.520 ⇒ 00:12:20.230 Greg Stoutenburg: Yeah.
113 00:12:21.490 ⇒ 00:12:22.390 Greg Stoutenburg: Cool. Okay.
114 00:12:22.600 ⇒ 00:12:24.559 Greg Stoutenburg: Awesome. Yeah, I hope that was helpful.
115 00:12:24.560 ⇒ 00:12:29.539 Henry Zhao: Yeah, definitely. Sometimes it’s even just good to, like, just bounce ideas off another person, right? So…
116 00:12:29.540 ⇒ 00:12:44.939 Greg Stoutenburg: Oh, absolutely, yeah, and I think that’s something that’s really valuable. I mean, it’s a test run, it’s thinking out loud, it’s… sometimes you’re in the middle of explaining something, and then you hear yourself explain it, you go, oh, I think I know what the solution is now, you know? Just hear yourself say it out loud, so yeah, anytime, happy to.
117 00:12:44.940 ⇒ 00:12:48.630 Henry Zhao: Yeah, thank you, thanks, Rick, and count on me anytime you also want to bounce ideas.
118 00:12:48.830 ⇒ 00:12:51.860 Greg Stoutenburg: I will do that. Yeah, cool. Alright, see ya, thanks.
119 00:12:51.860 ⇒ 00:12:52.390 Henry Zhao: Yeah.