Meeting Title: Eden Weekly Planning and OKRs Review Date: 2025-12-22 Meeting participants: Fireflies.ai Notetaker Joshua, Zoran Selinger, Mitesh Patel, Robert Tseng
WEBVTT
1 00:05:03.240 ⇒ 00:05:04.280 Zoran Selinger: I’mitesh.
2 00:05:04.640 ⇒ 00:05:05.760 Mitesh Patel: Hello!
3 00:05:06.880 ⇒ 00:05:07.899 Zoran Selinger: How’s it going?
4 00:05:07.900 ⇒ 00:05:09.019 Mitesh Patel: Good, how are you?
5 00:05:09.290 ⇒ 00:05:10.509 Zoran Selinger: Yeah, yeah, good.
6 00:05:10.760 ⇒ 00:05:11.610 Zoran Selinger: Good.
7 00:05:12.740 ⇒ 00:05:15.680 Zoran Selinger: No? Planning for the start of the week?
8 00:05:15.910 ⇒ 00:05:20.790 Zoran Selinger: As always, and I think this is gonna be an important part, of that.
9 00:05:22.520 ⇒ 00:05:26.519 Mitesh Patel: Yeah, so definitely it is, and
10 00:05:27.020 ⇒ 00:05:34.269 Mitesh Patel: I think, you know, I don’t know if Josh is gonna join, we’ll wait for Robert as well. But, yeah, so…
11 00:05:34.720 ⇒ 00:05:35.809 Mitesh Patel: Hey, Robert.
12 00:05:36.390 ⇒ 00:05:39.930 Mitesh Patel: How are you?
13 00:05:41.900 ⇒ 00:05:43.130 Robert Tseng: Good, how are you?
14 00:05:43.130 ⇒ 00:05:45.370 Mitesh Patel: Good, good.
15 00:05:46.030 ⇒ 00:05:52.800 Mitesh Patel: I’m not sure if we need Josh as Firefly’s note-taker, I think it just gets invited by, so maybe if you want to kick him out… Okay.
16 00:05:53.290 ⇒ 00:05:59.349 Robert Tseng: Yeah, I invited Josh, because I told him we were meeting, and he’s like, he won’t… but I don’t think he’s actually available, so…
17 00:05:59.680 ⇒ 00:06:03.949 Mitesh Patel: Okay, no, that’s fine, that’s fine. Yeah, so…
18 00:06:04.820 ⇒ 00:06:10.229 Mitesh Patel: Zarin just started saying this is an important, meeting to kick off the week, and
19 00:06:10.230 ⇒ 00:06:33.780 Mitesh Patel: wanted to just sort of, I think we all agree to that, but I wanted to re-emphasize that, not only sort of this meeting, but, Zaran, you know, we, as you know, after discussions, we, we, with both of you, we increased your, you know, hours dedicated to Eden. We really need your help in a, you know, in a kind of a more broader, I’ll say, proactive
20 00:06:34.030 ⇒ 00:06:44.009 Mitesh Patel: manner, right? Not just, hey, here’s some reports, analysis, dashboards we need, go make it happen. Part of it is more…
21 00:06:44.350 ⇒ 00:06:48.559 Mitesh Patel: Where we have a gap and a need is…
22 00:06:48.560 ⇒ 00:07:05.179 Mitesh Patel: you know, and I’ll call it on the mark on this side of the business, right, versus on the other side of the database. And that’s where you have done a really good job of understanding our business, our business goals.
23 00:07:05.180 ⇒ 00:07:11.729 Mitesh Patel: and… and now we need to, we need your help to say, okay, how do we proactively.
24 00:07:11.810 ⇒ 00:07:28.839 Mitesh Patel: monitor to the MarTech data, and make sure that, you know, things are being communicated correctly. You know, I’ll give you an example. I don’t think any of us were involved in this, right? So it’s an example from the past, is at some point, I don’t know, it was a few months ago.
25 00:07:29.050 ⇒ 00:07:37.500 Mitesh Patel: I happened to be just watching somebody show me a transaction on their screen share.
26 00:07:37.600 ⇒ 00:07:45.289 Mitesh Patel: And what I was struck by is, the data that we were sending for order total.
27 00:07:46.080 ⇒ 00:07:48.079 Mitesh Patel: Was not the order total.
28 00:07:48.540 ⇒ 00:07:51.810 Mitesh Patel: We were sending the product amount before the discount.
29 00:07:52.250 ⇒ 00:07:56.599 Zoran Selinger: And I was like, wait a minute, wait a minute, why does it… and I just happened to notice it.
30 00:07:56.700 ⇒ 00:08:07.839 Mitesh Patel: Right? If I hadn’t been on that call, or if I hadn’t noticed it, because it kind of went by, you know, zoomed by, and I’m like, go back, go back. And it’s not… it’s not something that… and we’ve been…
31 00:08:07.990 ⇒ 00:08:12.940 Mitesh Patel: In that channel, we were reporting the wrong order size
32 00:08:13.920 ⇒ 00:08:15.929 Mitesh Patel: For as long as anybody can remember.
33 00:08:16.810 ⇒ 00:08:31.460 Mitesh Patel: And those kinds of things are gonna happen, right? We’re gonna… no matter how much we sort of detail our plan and focus on execution, there are going to be things that get overlooked, right? So, it just…
34 00:08:32.159 ⇒ 00:08:42.950 Mitesh Patel: you know, we need someone who understands the business and the data to say, hey, when I QA this, I’m not just QAing it to make sure the data gets there.
35 00:08:43.230 ⇒ 00:08:51.980 Mitesh Patel: Right? But also the… the amounts are correct, or whatever, right? The data points are correct. And we’ve had…
36 00:08:52.220 ⇒ 00:08:58.190 Mitesh Patel: a few instances of that, right? And really, if you’re just on the data side.
37 00:08:58.700 ⇒ 00:09:05.260 Mitesh Patel: you won’t know, right? Because you don’t know the business may not…
38 00:09:05.790 ⇒ 00:09:11.510 Mitesh Patel: you know, you don’t understand what the business is. That’s why… and then you can’t help proactively.
39 00:09:11.690 ⇒ 00:09:20.170 Mitesh Patel: Anyway… That’s all to an example, just to say, hey, this aspect of…
40 00:09:20.390 ⇒ 00:09:31.650 Mitesh Patel: increasing your hours is not just for more work, but it’s so that you can help us more on… proactively on the business side of what all this MarTech data means.
41 00:09:33.580 ⇒ 00:09:34.620 Zoran Selinger: Make sense?
42 00:09:35.030 ⇒ 00:09:35.680 Zoran Selinger: Yep.
43 00:09:35.680 ⇒ 00:09:36.710 Mitesh Patel: Robert, that’s cool.
44 00:09:36.710 ⇒ 00:09:37.380 Zoran Selinger: He does.
45 00:09:37.740 ⇒ 00:09:38.250 Robert Tseng: Yep.
46 00:09:38.250 ⇒ 00:09:45.139 Mitesh Patel: Okay. Alright, so with that, I’m gonna share stuff with you,
47 00:09:45.470 ⇒ 00:09:51.870 Mitesh Patel: that I haven’t yet shared with the marketing team. A lot of this came… all of this came out of…
48 00:09:51.870 ⇒ 00:10:07.069 Mitesh Patel: that off-site planning, and it’s not that it’s a secret for the marketing team, I just have to sort of, you know, things have changed with Cutter stepping out, and me stepping in, and then, back into marketing, if you will, and kind of the…
49 00:10:07.250 ⇒ 00:10:20.229 Mitesh Patel: the state of where we are, where all the channels are. And so what I’m gonna go over with you is sort of our strategy.
50 00:10:20.740 ⇒ 00:10:36.740 Mitesh Patel: our over high-level goals, and as well as then sort of how those translate into OKRs and KPIs, right? Again, because I want you to know our business strategy, what we’re after, and I think you can help us a lot more effectively.
51 00:10:37.010 ⇒ 00:10:38.649 Mitesh Patel: If we… if we do that.
52 00:10:39.990 ⇒ 00:10:44.240 Mitesh Patel: Alright, can I share?
53 00:10:44.240 ⇒ 00:10:49.610 Zoran Selinger: start, Orion and myself, we have a catalyst meeting.
54 00:10:50.240 ⇒ 00:10:52.519 Zoran Selinger: In 20 minutes.
55 00:10:52.520 ⇒ 00:10:54.530 Mitesh Patel: Okay, I’ll try to be quick.
56 00:10:54.530 ⇒ 00:11:01.940 Zoran Selinger: gonna be doable. I think Robert can finish the meeting, you know, that one is really important. We have a meeting with them, and
57 00:11:02.250 ⇒ 00:11:14.280 Zoran Selinger: kind of keep the relationship there as much as possible. Yeah, I’ll stay on. We are in a good place. We are in a good place right now, so I expect that meeting to go well, but we… I need to be there.
58 00:11:14.810 ⇒ 00:11:21.230 Mitesh Patel: Okay, no, I understand. I got it, I understand. I’ll try to be fast, and then, again, Robert can stay on, and or Zern…
59 00:11:21.230 ⇒ 00:11:22.880 Zoran Selinger: Hours are recording, obviously.
60 00:11:22.880 ⇒ 00:11:33.579 Mitesh Patel: Yeah, and we can continue afterwards, too. This is too important for you to just say, okay, we’ll talk about it later, you know, it’s okay if you miss it. I’m happy to repeat it or continue it with you later.
61 00:11:34.740 ⇒ 00:11:35.800 Zoran Selinger: Okay, thank you, thank you.
62 00:11:35.800 ⇒ 00:11:40.689 Mitesh Patel: So this is… this was my general, sort of, here’s how I plan, here’s how I teach
63 00:11:40.690 ⇒ 00:11:58.900 Mitesh Patel: teams to plan, you know, because a lot of people don’t know what… you know, how do you define a strategy for the whole year, right? And so this is a template, if you will, a framework, that’s worked in, you know, multi-billion dollar business, as well as in, like, $10 million startups, and certainly mid-sized companies. I think that’s where we are.
64 00:11:58.900 ⇒ 00:12:03.080 Mitesh Patel: In terms of how to take, you know, go from goals.
65 00:12:03.080 ⇒ 00:12:11.800 Mitesh Patel: to, KPIs, to initiatives, right, and prioritize them. So our 26 goals, everything is by end of year. Right now.
66 00:12:11.940 ⇒ 00:12:25.880 Mitesh Patel: you know, we had a little bit slower December than we had, November and October, or October and November, I said it backwards. But we were, you know, we were sort of 29,000 to 30,000 total orders per month.
67 00:12:26.810 ⇒ 00:12:35.129 Mitesh Patel: Our goal is not, you know, we’re gonna increase by 20% or 50%. We’re gonna triple the number of orders per month.
68 00:12:35.370 ⇒ 00:12:47.560 Mitesh Patel: across all drugs, not just GLP-1s, alright? So that gives you your target revenue. Now, refill… I, I…
69 00:12:47.870 ⇒ 00:13:00.669 Mitesh Patel: realized this was a confusion, confusing, a source of confusion when I asked for refills, but refills slash repeat orders need to make up 85% of those 100,000 orders.
70 00:13:00.950 ⇒ 00:13:05.020 Zoran Selinger: So what we’re doing is getting about 15,000 new orders.
71 00:13:05.190 ⇒ 00:13:06.139 Mitesh Patel: per month.
72 00:13:06.300 ⇒ 00:13:16.150 Mitesh Patel: Right? So, to do that, wow, we gotta get, what is that? 500 new orders per day.
73 00:13:16.290 ⇒ 00:13:27.409 Mitesh Patel: And we’re, like, hovering somewhere around… somewhere between 100 and 200, okay? And what this does is setting goals like this gives all the channels
74 00:13:27.410 ⇒ 00:13:39.930 Mitesh Patel: here’s what I, you know, if we gotta get to 500 new orders a day, it tells the acquisition channels, and so now, within marketing, I’m breaking it down by each of the channels. Google, you gotta get this much per day, affiliates this much per day, and so on, right?
75 00:13:40.530 ⇒ 00:13:50.140 Mitesh Patel: operating income discussion, and then Eden Pharmacy needs to fulfill a third, because this was an Eden Pharmacy and Eden Telehealth joint sort of session.
76 00:13:50.170 ⇒ 00:14:05.209 Mitesh Patel: So, Eden Pharmacy needs to have… build up their capacity, whether it’s through expansions or more, you know, optimizations or whatever, to be able to do 1,000 a day. Right now, they’re about 200 a day, and that’s just…
77 00:14:05.380 ⇒ 00:14:12.890 Mitesh Patel: No, they’re $250 a day, but 1,000 a day is just for Eden Telehealth. They have other customers as well, right?
78 00:14:13.550 ⇒ 00:14:28.300 Mitesh Patel: So, strategic imperatives. We’re generally, right now, we are a one product, GLP-1s, one channel company, Google, right? 90% of our sales are GLP-1s, 90% of our sales come from Google.
79 00:14:28.610 ⇒ 00:14:52.350 Mitesh Patel: That is a very, very risky proposition for many reasons, right? Other competitors are winning in affiliates, winning in influencers, and so on, right? And it’s about… 26 is about building trust with customers. You know, over the summer, when we had supply chain issues, and our shipments were late, and whatever, whatever, we lost a lot of trust.
80 00:14:52.690 ⇒ 00:15:00.339 Mitesh Patel: And, and so not only getting meds to patients on time, but, you know, through…
81 00:15:00.340 ⇒ 00:15:14.729 Mitesh Patel: sort of influencers, right? Influencers, affiliates, like Forbes sites. These are… they all have brand equity that… when an influencer recommends us, or, you know, Forbes has us in the top 3, that’s…
82 00:15:14.730 ⇒ 00:15:21.189 Mitesh Patel: Trust that we get, you know, that trust is transferred to us, right?
83 00:15:21.200 ⇒ 00:15:40.040 Mitesh Patel: And then, through the end, you know, we have a somewhat disjointed end-to-end experience, right? Marketing, customer care, op, farm ops, even when they… when a customer interacts, when a patient interacts with our provider’s network, right? Third-party provider’s network.
84 00:15:40.040 ⇒ 00:15:46.449 Mitesh Patel: It’s not a cohesive Eden experience. Packaging. So these are all the things we’re working on.
85 00:15:46.580 ⇒ 00:15:54.689 Mitesh Patel: Eden, we’ve been, I think, good in general at perform… bottom-of-the-funnel performance marketing.
86 00:15:54.950 ⇒ 00:16:11.239 Mitesh Patel: We’re gonna change it, we’re gonna take it up to, you know, we have different personas. Each persona has a different need, different cha- different channels influence their purchase decision. And so, persona and end-to-end journey-based marketing.
87 00:16:11.280 ⇒ 00:16:20.300 Mitesh Patel: These are maybe just words, but they’re gonna go very deep into how we execute, and what are the initiatives we prioritize, and so on.
88 00:16:20.870 ⇒ 00:16:23.219 Mitesh Patel: Right? Any questions about this stuff?
89 00:16:28.060 ⇒ 00:16:36.439 Zoran Selinger: Yeah, I mean, at some point, we’ll have to understand where do you expect us exactly to,
90 00:16:36.440 ⇒ 00:16:47.190 Mitesh Patel: to assist you here? Yeah, exactly, so I’m getting to that, but I wanted to give you, kind of, you know, what we’re doing, and why these are the things that are really important for us to track, and so on.
91 00:16:47.470 ⇒ 00:16:50.189 Mitesh Patel: Let’s see… not this one.
92 00:16:52.150 ⇒ 00:16:53.000 Mitesh Patel: Alright.
93 00:16:53.300 ⇒ 00:16:57.309 Mitesh Patel: So… What I have, and then I added a sixth one here.
94 00:16:57.490 ⇒ 00:16:59.629 Mitesh Patel: Brand, and…
95 00:17:04.270 ⇒ 00:17:08.379 Mitesh Patel: Alright, so these are quarterly sort of numbers, if you will, but,
96 00:17:08.440 ⇒ 00:17:27.080 Mitesh Patel: I have month-by-month projections. So now, people, you know, that’s not really… so we might need some data there, but these are people and principles and performance reviews, and everybody’s on their OKRs and KPIs. Let’s get to more of the data side, right?
97 00:17:27.710 ⇒ 00:17:43.919 Mitesh Patel: So, when we say OKRs and KPIs, this is where we need your help to help, you know… and a lot of this you’re already doing, and we need, and that’s okay, but we need, sort of.
98 00:17:44.060 ⇒ 00:17:51.020 Mitesh Patel: enhanced version of the things we’re already doing, and an expanded version of what we’re doing.
99 00:17:53.590 ⇒ 00:18:00.540 Mitesh Patel: the way I have teams execute, and I’m trying to think about what is the best
100 00:18:01.750 ⇒ 00:18:07.950 Mitesh Patel: place to show you. I’m gonna… this is draft, this is work in progress.
101 00:18:08.530 ⇒ 00:18:12.139 Mitesh Patel: So I didn’t want to really jump to it, but I will.
102 00:18:17.240 ⇒ 00:18:24.900 Mitesh Patel: Hold on, let me see where it is. What we do is I have every team… okay, it’s not in here.
103 00:18:25.010 ⇒ 00:18:37.899 Mitesh Patel: I have every team produce… so, based on that end-of-year goals, right, every channel, every function by drug needs to give us a monthly projection.
104 00:18:38.020 ⇒ 00:18:55.399 Mitesh Patel: That takes seasonality into consideration, too, now, right? Because that’s kind of an overlay, it’s not just kind of gradual growth every month. In January, February, because of seasonality, it’s going to spike, then it’s going to slow down a bit, and then September, October are kind of our next spike months. So we’re doing monthly projections.
105 00:18:56.110 ⇒ 00:19:05.359 Mitesh Patel: And then we’re taking granular metrics, like really, really painfully granular metrics, and I’m having them project those on a weekly basis.
106 00:19:05.460 ⇒ 00:19:19.269 Mitesh Patel: So to hit those monthly numbers, like, so let me use email as an example. If email… I’m just gonna… I’m using totally made-up numbers, but if email has a revenue goal of $1 million in a given month.
107 00:19:20.010 ⇒ 00:19:32.600 Mitesh Patel: you know, the granular metrics are for flows and for campaigns, two different types of emails, right? They gotta break it down into, well, here’s how many emails I need to send per week.
108 00:19:32.760 ⇒ 00:19:42.690 Mitesh Patel: And again, they’re averages, right? Here’s the click-through rate I need, here’s the conversion rate I need, here’s the open rate I need. So, when I say granular metrics, they’re really granular.
109 00:19:42.740 ⇒ 00:19:58.690 Mitesh Patel: Right? And in Google, for example, it’s broken up by… between search and shopping, and even within search, it’s brand and non-brand, right? So, these… all of the metrics that a channel needs to,
110 00:20:00.130 ⇒ 00:20:17.349 Mitesh Patel: be, you know, a channel owner typically watches certain metrics, so all of those metrics at a very detailed level, they need to project on a weekly basis. And then weekly, we compare
111 00:20:17.450 ⇒ 00:20:21.019 Mitesh Patel: projections versus actuals, right? It’s…
112 00:20:21.020 ⇒ 00:20:39.270 Mitesh Patel: not about, oh, being off, because no, we don’t have a crystal ball when we’re doing these projections, so it’s not about being plus or minus, it’s more about the trends. How are we trending actual versus projection, right? We can’t wait till the end of the month or end of the quarter to say, oh, well, our open rate was no good.
113 00:20:39.910 ⇒ 00:20:53.869 Mitesh Patel: And when we, when we have a projection, a weekly projection, and we watch it weekly, and we compare, we’re gonna know trends within 2 or 3 weeks maximum, right? And if something… if one of the metrics is trending better than we expected.
114 00:20:53.980 ⇒ 00:21:07.110 Mitesh Patel: Great, let’s do more of that. Let’s apply that somewhere else. Learnings other places. If something is trending not as good, now we know exactly… it’s not just, oh, our revenue is not where it needed to be, now we know why.
115 00:21:07.450 ⇒ 00:21:17.679 Mitesh Patel: Click-through rate is, you know, the reason why we’re not hitting our… we’re not pacing per the rabbit, you know, per the goal.
116 00:21:17.800 ⇒ 00:21:37.580 Mitesh Patel: And so now they can say, hey, looking at click-through rate, and here’s what I need to A-B test, you know, or ABCD test, whatever it is, to improve the click-through rate. That is why I have teams track these metrics at a painful, tedious level, okay?
117 00:21:37.580 ⇒ 00:21:43.479 Mitesh Patel: And, and so… that’s where, from a MarTech data perspective.
118 00:21:43.730 ⇒ 00:21:53.820 Mitesh Patel: we’re gonna need the most help. I do not want the teams suspending… I want to minimize the time that the teams have to spend on collecting that data.
119 00:21:54.190 ⇒ 00:22:06.629 Mitesh Patel: Right? I want them to spend time saying, you know, analyzing the data, looking at the trends, and saying, okay, here’s what I need to do next to turn this trend around, because it’s not going in the right direction.
120 00:22:10.060 ⇒ 00:22:10.910 Zoran Selinger: Understand, okay.
121 00:22:10.910 ⇒ 00:22:12.050 Mitesh Patel: Okay. So.
122 00:22:12.050 ⇒ 00:22:12.920 Zoran Selinger: like this.
123 00:22:13.090 ⇒ 00:22:21.680 Mitesh Patel: Yeah, so I’m a storyteller, but there’s, you know, I want to make sure you guys have the context, behind, behind it, yeah.
124 00:22:22.140 ⇒ 00:22:26.500 Mitesh Patel: Yep, I will, and it is work in progress, but you got, you know…
125 00:22:26.990 ⇒ 00:22:31.530 Mitesh Patel: You can see I haven’t shared it with anybody.
126 00:22:32.580 ⇒ 00:22:34.700 Mitesh Patel: Robert, does that work? Dear Trident?
127 00:22:34.700 ⇒ 00:22:35.689 Robert Tseng: Yep, that works.
128 00:22:36.710 ⇒ 00:22:40.830 Mitesh Patel: And… I’m not sure if I have… if I’ve shared any documents with you, Zoran.
129 00:22:43.500 ⇒ 00:22:44.120 Mitesh Patel: That one was…
130 00:22:44.120 ⇒ 00:22:45.680 Zoran Selinger: The second one, maybe?
131 00:22:46.350 ⇒ 00:22:47.100 Zoran Selinger: Yeah.
132 00:22:47.800 ⇒ 00:22:49.469 Zoran Selinger: Okay, thank you.
133 00:22:54.940 ⇒ 00:22:56.990 Mitesh Patel: Okay,
134 00:22:57.830 ⇒ 00:23:16.679 Mitesh Patel: And then I’m gonna share some Monday… so once we have… right now, I’m collecting from all of the teams these weekly forecasts, right? I think that will be as good as this, you know, I want you to have this, but that will be, I think, better to share with you.
135 00:23:17.000 ⇒ 00:23:23.069 Mitesh Patel: And then the other… one second… I’m gonna change windows here.
136 00:23:25.200 ⇒ 00:23:28.199 Mitesh Patel: I’m gonna share these Monday boards with you as well.
137 00:23:28.380 ⇒ 00:23:37.550 Mitesh Patel: Although, again, they’re work in progress, but I want to make sure that I show you what we, you know, what we convert all of this, sort of.
138 00:23:38.020 ⇒ 00:23:41.299 Mitesh Patel: high-level OKRs and KPIs into, right?
139 00:23:41.710 ⇒ 00:23:46.329 Mitesh Patel: Alright, you see my Monday window, right?
140 00:23:47.220 ⇒ 00:23:47.870 Zoran Selinger: Yep.
141 00:23:47.870 ⇒ 00:24:05.800 Mitesh Patel: Okay. So, then each channel or function basically take their OKRs, their objectives and key results, and say, here are the KPIs that we’re going to be tracking. So we have all of that for marketing in one Monday board.
142 00:24:05.990 ⇒ 00:24:13.429 Mitesh Patel: And then, I don’t know, Tiguan did this thing, I don’t know if it’s the most effective, but basically, for each person, you know, they have a…
143 00:24:13.640 ⇒ 00:24:15.690 Mitesh Patel: I don’t think they’re filling it in.
144 00:24:15.900 ⇒ 00:24:20.620 Mitesh Patel: Well, here’s… so Matt… Matt is filling in.
145 00:24:20.770 ⇒ 00:24:35.220 Mitesh Patel: his KPIs. But again, we’re collecting this, sort of manually, right? Or from exports and things like that. So that’s where I think, on the marketing side, we can automate through
146 00:24:35.910 ⇒ 00:24:39.660 Mitesh Patel: The integrations that we have, or integrations we might need.
147 00:24:40.100 ⇒ 00:24:51.240 Mitesh Patel: So this is based on the influencer, so upfluence integration here. And… so, it’s not just the… the roll-up.
148 00:24:51.490 ⇒ 00:24:59.039 Mitesh Patel: sort of NCAC and spend that we do, but you have the data already behind a lot of this, so this is sort of a weekly version of it.
149 00:25:02.100 ⇒ 00:25:02.740 Zoran Selinger: Okay.
150 00:25:02.740 ⇒ 00:25:09.079 Mitesh Patel: Alright, so that’s on the marketing side. I’m gonna change also to…
151 00:25:10.220 ⇒ 00:25:29.620 Mitesh Patel: On the telehealth ops side, as well as care, you know, there are other metrics. So, those OKRs and KPIs, you know, they’re not just marketing OKRs and KPIs. We got, we got basically 3 functions in telehealth. Marketing.
152 00:25:30.050 ⇒ 00:25:38.679 Mitesh Patel: telehealth Ops and care. And I showed you the marketing ones, and
153 00:25:39.190 ⇒ 00:25:49.659 Mitesh Patel: we also have similar, OKRs and KPIs for telehealth ops and for care. So these are for ops.
154 00:25:50.020 ⇒ 00:26:03.860 Mitesh Patel: And they have to do with SLAs and COGS. Really, those are the two key, KPIs, right? And the SLAs, there’s different… different SLA numbers that we track.
155 00:26:04.300 ⇒ 00:26:13.700 Mitesh Patel: It’s by pharmacy, how many, you know, what percent of orders are out of SLA, which is more than 3 business days, from
156 00:26:13.990 ⇒ 00:26:24.339 Mitesh Patel: And what that is, what is from prescription sent to the pharmacy to the time it was fulfilled. That SLA for each pharmacy has to be under 3 days.
157 00:26:25.860 ⇒ 00:26:34.680 Mitesh Patel: And then, what is the total number of orders that is over 10 days old in the system? From order placed.
158 00:26:34.730 ⇒ 00:26:47.910 Mitesh Patel: to, delivered, or, or sorry, fulfilled or shipped, we want, we want that number to go to zero. And if we look at the chart versions of these, I think it’s a little bit more…
159 00:26:47.910 ⇒ 00:27:03.720 Mitesh Patel: sort of… we want that, so the red line is our goal, right? The numbers before here, we weren’t even tracking them, they’re really bad and much higher. But you can see we got it down and then kind of jumped back up, and maybe it’s for the, you know.
160 00:27:03.800 ⇒ 00:27:20.910 Mitesh Patel: what we do is we take every one of these 40 orders, and the team prioritizes them, and they manually go through each of the 40 orders and saying, what’s the holdup? Right? So our goal, their goal is no orders should be more than 10 days in the system.
161 00:27:21.630 ⇒ 00:27:22.430 Mitesh Patel: Right.
162 00:27:22.560 ⇒ 00:27:41.819 Mitesh Patel: And so once we get this number down to zero, what they’re going to be looking at is how many orders are more than 5 days, and then really, you know, treat those as emergency, right? Because it’s all about getting, you know, remember I told you about the trust, right? Customer trust. It’s getting meds to patients on time.
163 00:27:41.820 ⇒ 00:27:44.879 Mitesh Patel: That’s what all of these KPIs kind of roll back to.
164 00:27:45.190 ⇒ 00:27:46.050 Mitesh Patel: Boom.
165 00:27:46.530 ⇒ 00:27:54.939 Mitesh Patel: Medops, this is a number of, orders, out of…
166 00:27:55.030 ⇒ 00:28:13.039 Mitesh Patel: SLA. And so these are ones that are stuck. It might be pending payments, or for, you know, waiting for a provider, or a doctor, or waiting for a patient to give the doctor more information. There are a lot of reasons, and we hovered for the longest time around 14-15%.
167 00:28:13.060 ⇒ 00:28:26.670 Mitesh Patel: Our goal, the red line is at 5… well, we increased it to 7%, but originally it was at 5%. But what we realized is there’s just more than 5, like, 5.5% of all orders have payment failed.
168 00:28:27.030 ⇒ 00:28:27.870 Mitesh Patel: Right?
169 00:28:27.870 ⇒ 00:28:28.239 Zoran Selinger: Oh, right.
170 00:28:28.240 ⇒ 00:28:39.920 Mitesh Patel: different reasons, and we gotta pursue those, and we gotta figure those out with our payment processors, but… so, I can’t give the MedOps team… I can’t enforce a 5% goal.
171 00:28:39.920 ⇒ 00:28:40.500 Zoran Selinger: Yeah.
172 00:28:40.500 ⇒ 00:28:52.429 Mitesh Patel: more than 5% is, you know, related to payments and payment processing, right? So that’s why we changed that goal to 7. But we wouldn’t have known that if we weren’t looking at it and executing at this level.
173 00:28:52.720 ⇒ 00:29:10.240 Mitesh Patel: So, I think over the last 2-3 months, when I went to go focus on, ops, and since the beginning of September, right? No, shit, now it’s 4 months, ops and care, this is the level of execution and tracking and OKRs, KPIs that we implemented there.
174 00:29:10.880 ⇒ 00:29:13.720 Mitesh Patel: We’re gonna bring that same discipline to marketing.
175 00:29:14.560 ⇒ 00:29:15.310 Mitesh Patel: Okay.
176 00:29:15.820 ⇒ 00:29:19.939 Mitesh Patel: Cogs, as a percent of,
177 00:29:20.810 ⇒ 00:29:32.549 Mitesh Patel: sales is also, and this is why we’re constantly saying, oh, we need COGS info, we need COGS, right? And I know some of that data you gotta get from, from BASC, but…
178 00:29:32.780 ⇒ 00:29:35.609 Mitesh Patel: This is, you know, that’s why this is so important.
179 00:29:35.820 ⇒ 00:29:41.909 Mitesh Patel: Because every point we save here goes directly to, you know, a point of profitability.
180 00:29:43.260 ⇒ 00:29:43.840 Zoran Selinger: Yeah, right.
181 00:29:43.840 ⇒ 00:29:44.400 Mitesh Patel: a minute.
182 00:29:45.020 ⇒ 00:29:51.279 Mitesh Patel: These are otters going to eat in pharmacy.
183 00:29:51.930 ⇒ 00:29:55.150 Mitesh Patel: Per week.
184 00:29:55.870 ⇒ 00:30:05.499 Mitesh Patel: Alright, customer care… And these are, I think, all available in Zendesk. That’s where this data comes from.
185 00:30:05.750 ⇒ 00:30:13.719 Mitesh Patel: Back in the day, we had over 16% of our, you know, there were a number of tickets in queue as a…
186 00:30:13.830 ⇒ 00:30:17.970 Mitesh Patel: Was, like, between… hovering between 15… 16% and 20%.
187 00:30:18.180 ⇒ 00:30:30.800 Mitesh Patel: we were frustrating the shit out of our customers. Our goal was to get it down to under 2%, which we did by the end of September. We started at the beginning of September, we got it down by the end of… and we’ve been doing fa… the team’s doing.
188 00:30:30.800 ⇒ 00:30:32.630 Zoran Selinger: That’s amazing. Yeah.
189 00:30:32.690 ⇒ 00:30:44.619 Mitesh Patel: Right? I told them I’m gonna… I told them I’m gonna move that red line down to half a percent. But… and then chargebacks and refunds, these are, you know, contra revenues, right?
190 00:30:44.770 ⇒ 00:30:54.150 Mitesh Patel: chargebacks are very important. Obviously, they cost the company a lot of money. And, you know, our goal was…
191 00:30:54.380 ⇒ 00:30:58.210 Mitesh Patel: 0.05, and we’re maintaining well below that.
192 00:30:58.480 ⇒ 00:31:02.529 Mitesh Patel: And even this .05 is well below,
193 00:31:02.690 ⇒ 00:31:06.489 Mitesh Patel: Like what Jonah from finance would say our goal is, right?
194 00:31:07.110 ⇒ 00:31:12.400 Mitesh Patel: Internally, we are holding ourselves accountable to a more stricter goal.
195 00:31:12.900 ⇒ 00:31:18.960 Mitesh Patel: Same thing as, you know, refunds as percent of, revenue.
196 00:31:20.140 ⇒ 00:31:32.780 Mitesh Patel: You can see they spiked, and these are monthly right now. We’ll break them down into weekly, and I don’t know why we don’t have October and November, but it’s just a matter of them having to update it, right?
197 00:31:32.880 ⇒ 00:31:45.710 Mitesh Patel: But you can see, when we had the supply chain issues, how bad things got, these months. And that actually, you know, had had an impact on CAC, right? Because CAC is based on number of
198 00:31:45.770 ⇒ 00:31:58.239 Mitesh Patel: real orders, right, in our system. So if someone places an order and then cancels it, well, that decreases the number of customers, so the CAC goes up.
199 00:31:59.800 ⇒ 00:32:06.450 Mitesh Patel: Right? And so, I know October and November numbers are even better than this.
200 00:32:07.500 ⇒ 00:32:12.640 Mitesh Patel: I’m gonna get Tigran’s help to consolidate all of these
201 00:32:12.880 ⇒ 00:32:19.249 Mitesh Patel: KPIs that we have in marketing boards… sorry, marketing… on Monday boards, I should say.
202 00:32:19.720 ⇒ 00:32:24.980 Mitesh Patel: and then share those boards with you guys. Really, what we need help on is…
203 00:32:25.190 ⇒ 00:32:45.110 Mitesh Patel: collecting them, putting them in one place, I’d love them in a spreadsheet so they can compare against their projections. You know, I don’t know, it doesn’t have to be a spreadsheet, could be Tableau, could be… doesn’t matter to me, but I want the trends versus actuals, right? Sorry, actuals versus projection trends to be, like.
204 00:32:45.300 ⇒ 00:32:48.580 Mitesh Patel: Super obvious just by looking at the data.
205 00:32:49.120 ⇒ 00:32:49.740 Zoran Selinger: Okay.
206 00:32:50.440 ⇒ 00:32:51.540 Zoran Selinger: I’ll…
207 00:32:51.880 ⇒ 00:33:05.570 Zoran Selinger: I’ll have to go at this point. Mitesh, I’m gonna, look over the recording of this, and I’m gonna look at the document… documents in detail. So…
208 00:33:06.340 ⇒ 00:33:16.980 Zoran Selinger: what’s your availability? For example, tomorrow, if we need to discuss anything, I’m gonna have a sh… Christmas Eve is gonna be a short day for me.
209 00:33:17.040 ⇒ 00:33:25.499 Mitesh Patel: Yeah, I think tomorrow’s gonna be tight for me, just given my schedule. If you are working in the morning of Christmas Eve, that’s pretty open for me.
210 00:33:26.970 ⇒ 00:33:33.540 Mitesh Patel: And I know, I know we got time difference and all that, so, if that were… tomorrow’s not gonna work for me at all, Zaran.
211 00:33:33.710 ⇒ 00:33:35.430 Mitesh Patel: Yeah, okay. Yeah. Okay.
212 00:33:35.470 ⇒ 00:33:45.400 Zoran Selinger: Okay, I’ll, I’ll try to, I’ll try to get you, on, on Wednesday, if, with, with any questions. In terms of,
213 00:33:46.300 ⇒ 00:33:50.019 Zoran Selinger: When do you expect us to have a clear roadmap for this?
214 00:33:52.010 ⇒ 00:33:57.100 Mitesh Patel: I just need to get this to you. Let me have Tigran put it all in one place and share it with you.
215 00:33:57.360 ⇒ 00:33:58.410 Zoran Selinger: Okay, okay.
216 00:33:58.410 ⇒ 00:33:58.990 Mitesh Patel: Okay.
217 00:33:58.990 ⇒ 00:34:02.320 Zoran Selinger: Okay, excellent. Well, thank you.
218 00:34:02.320 ⇒ 00:34:02.760 Mitesh Patel: He’s around.
219 00:34:02.760 ⇒ 00:34:10.770 Zoran Selinger: I’m gonna leave the meeting on, right? And we’ll… Yeah, I’ll stay on. I have a few questions, yeah. Sure. Okay.
220 00:34:10.929 ⇒ 00:34:11.760 Zoran Selinger: Alright.
221 00:34:11.760 ⇒ 00:34:12.710 Robert Tseng: Okay, see you tomorrow.
222 00:34:12.710 ⇒ 00:34:13.630 Mitesh Patel: Thanks, Aaron.
223 00:34:13.820 ⇒ 00:34:14.480 Zoran Selinger: Thank you.
224 00:34:16.520 ⇒ 00:34:20.369 Robert Tseng: Hi, can I… can I take over the screen share?
225 00:34:20.370 ⇒ 00:34:21.480 Mitesh Patel: Yeah, yeah.
226 00:34:21.489 ⇒ 00:34:22.219 Robert Tseng: Okay.
227 00:34:23.509 ⇒ 00:34:24.939 Mitesh Patel: I, I, how do I… I just…
228 00:34:24.940 ⇒ 00:34:25.979 Robert Tseng: Oh, I got it, yeah, no worries.
229 00:34:25.989 ⇒ 00:34:26.649 Mitesh Patel: Yeah.
230 00:34:26.650 ⇒ 00:34:34.420 Robert Tseng: Okay, I replaced it. Yeah, so I just pulled up, yeah, I think I’ve seen these before. Yeah, I hear that you’re gonna consolidate these ops metrics from the…
231 00:34:34.420 ⇒ 00:34:51.930 Robert Tseng: with Tigran, and then you send them over to us. Yeah, I’m curious, like, how this, if there… what discrepancies there are with, kind of, this versus, like, what we have in Tableau already. I think for, like, what you’re describing, I mean, we just…
232 00:34:52.210 ⇒ 00:35:02.360 Robert Tseng: I think a spreadsheet ends up being kind of the best version of this, in terms of, like, actuals versus projections, and being able to, like,
233 00:35:02.640 ⇒ 00:35:18.159 Robert Tseng: you know, just… you have pivot tables and, like, everything kind of broken out nicely, more like an accounting or, like, a more finance or ops kind of focused report. So, I don’t really think I see this living in a tableau.
234 00:35:18.160 ⇒ 00:35:26.939 Robert Tseng: Yeah. Kind of moving forward. But I’d love to just kind of share, like, we were discussing some things on our roadmap, and so I think there’s some overlap.
235 00:35:27.030 ⇒ 00:35:40.279 Robert Tseng: of things that we wanted to kind of put in front of, ELT, and we’ve been going back and forth with them in this, so I wanted to just get some alignment here. I guess, like, if you haven’t seen this yet, I’ll share this with you, but…
236 00:35:40.500 ⇒ 00:35:50.999 Robert Tseng: yeah, I basically had, like, our team internally kind of just go through all the things that we feel like are high priority, and I mean, we’ve just roadmap… just planning out what the next…
237 00:35:51.100 ⇒ 00:36:02.810 Robert Tseng: I guess quarter, two quarters could be, and so, yeah, I guess these are kind of already arranged in the way that we think that we should also kind of be allocating our time.
238 00:36:02.810 ⇒ 00:36:23.480 Robert Tseng: I guess, like, kind of what I observed from what you were discussing with, with, with Zeron, everything around ops, like, I feel like is kind of in that, what you, what, you know, what you just talked about. You know, you’re monitoring certain operational KPIs and then trying to, like, kind of continue to optimize for them. And I think…
239 00:36:23.480 ⇒ 00:36:24.520 Robert Tseng: you know.
240 00:36:24.520 ⇒ 00:36:42.959 Robert Tseng: there’s one thing about, like, doing the, kind of, what I… I think there’s, like, a weekly business review kind of piece to it that you’ve shared, which is just, like, what is the actual status at each of these different, of each of these different metrics, greater than 10 days, SLA? I mean, I think,
241 00:36:42.960 ⇒ 00:36:54.950 Robert Tseng: Yeah, like, so there’s… there’s some of that. I know that, like, SLA is under 3 business days. Like, this actually, like, is that, like, making sure that this is actually true, that, you know, we,
242 00:36:54.950 ⇒ 00:37:09.210 Robert Tseng: like, the core operational metrics, we know what those targets should be, those KPIs should be, and we’re actually, like, hitting those. I guess, like, what we’ve queued up here is more like, okay, like, you know, how do we blend, like, kind of
243 00:37:10.830 ⇒ 00:37:19.270 Robert Tseng: we’re consolidating some of the questions. It’s like, Jonah wants to know, like,
244 00:37:19.420 ⇒ 00:37:29.480 Robert Tseng: is there a leading indicator for what operational burden we’re gonna have to take on? Like, if we were to be able to adjust marketing inputs and, like, understand, like.
245 00:37:29.850 ⇒ 00:37:35.949 Robert Tseng: Differences in spend, check reduction, whatever, like, how does that actually, like.
246 00:37:35.950 ⇒ 00:37:57.740 Robert Tseng: kind of model out to the type of, like, to… to what… what supply we have to be able to serve this… this type of demand. So, it’s kind of like a supply elasticity model. So, I think that’s, like, that’s like one project that we’ve… we’ve kind of, scoped out at this point. This one is more kind of like, you know.
247 00:38:00.320 ⇒ 00:38:25.040 Robert Tseng: Yeah, this is more of the SLA targeting, like, what are the specific analyses that we can do to try to drive, like, you know, SLA performance efficiency? Like, I mean, I’d look at this, because this is what we’re anchored to right now, 22% above 3 business days, like, I… it seems like that that’s not true. So, I don’t know, there’s, like, some, like, there’s some things that are off where I’m like, I saw what you…
248 00:38:25.040 ⇒ 00:38:34.530 Robert Tseng: you shared, and I was like, I don’t feel like these are the same takeaways that we have, and so I think we just need to get on the same page around this and make sure we’re aiming at the same target, I guess.
249 00:38:34.530 ⇒ 00:38:35.329 Mitesh Patel: Yeah, awesome, kind of…
250 00:38:35.330 ⇒ 00:38:38.119 Robert Tseng: Like, our take at going after the same problem.
251 00:38:38.660 ⇒ 00:38:44.329 Robert Tseng: This one is more kind of like, okay, there’s certain…
252 00:38:45.050 ⇒ 00:38:53.899 Robert Tseng: bottlenecks in… in, like, headcount planning and, like, supply, like, kind of how to get…
253 00:38:54.070 ⇒ 00:39:02.019 Robert Tseng: shipments through different stages of the supply chain faster that, like, kind of Rebecca has talked about, but it’s… it’s still very, like, kind of abstract, in my opinion, so…
254 00:39:02.020 ⇒ 00:39:21.650 Robert Tseng: We were trying to, like, size the opportunity there of, like, what money are we leaving on the table by not being optimized there? So, I think this is still, like, kind of pending discovery. Maybe you have more context on, like, some of these, like, bottlenecks that she’s describing, but, I think that seemed to be another need as well. Yep.
255 00:39:21.730 ⇒ 00:39:37.090 Robert Tseng: But, yeah, so those are, like, operationally, those are kind of, like, the three main kind of areas that we’ve kind of thought about. SLA improvements, kind of just, like, operational excellence kind of…
256 00:39:37.560 ⇒ 00:39:39.990 Robert Tseng: Ad hoc analyses, and then this, like.
257 00:39:40.140 ⇒ 00:39:45.489 Robert Tseng: more finance-focused, you know, model of, like, a supply elasticity model.
258 00:39:45.490 ⇒ 00:39:56.249 Mitesh Patel: Yeah, COGS is a… COGS is another one that’s super important, because COGS, based on which pharmacy fulfills, you know, which order, is it refilled first, 1, 2, 3, 4?
259 00:39:56.250 ⇒ 00:40:04.310 Robert Tseng: is hugely important for us. Yeah, so for COGS, we’ve been, like, spinning on that for 2 months, like, I’ve seen it on our, like.
260 00:40:04.310 ⇒ 00:40:09.690 Robert Tseng: Yeah, it’s just, like, I’ve been seeing it on our everyday stand-up, we always talk about it.
261 00:40:09.690 ⇒ 00:40:25.630 Robert Tseng: that the… like, it’s always about asking Bass to add this, or whatever. I’m telling the team, like, we need to move on and just use a proxy. Like, we know enough of, like, okay, 25% of orders do not, like, are multiple vials,
262 00:40:25.630 ⇒ 00:40:42.409 Robert Tseng: And, like, we don’t have the vial size, and therefore, like, you know, we don’t… we don’t really know, like, what the quantities are specifically. But we should be able to make some assumptions, and even if we’re not, like, precise about, like, what the cogs are, like, I think we can… we can still, impact
263 00:40:42.410 ⇒ 00:40:56.030 Robert Tseng: I mean, it’s still, like, the takeaway is still going to be the same. Like, go route orders to the pharmacies that have lower COGS, right? We may not know what the actual number should be, but, like.
264 00:40:56.030 ⇒ 00:41:11.900 Robert Tseng: without the actual data, but, like, I don’t know if we’re gonna get any better data on that, before now, between now and, like, when Eden OS has stood up. So, like, is there… can we do anything in the meanwhile to just, like, to move the needle on that anyway?
265 00:41:12.130 ⇒ 00:41:23.880 Mitesh Patel: Yeah, and that’s a great… that’s a great approach, because that’s what we do now. We sort of estimate where we don’t know, right? So… Yeah. But just put us… put that in one place, and then, yeah, got it. Okay, good.
266 00:41:24.690 ⇒ 00:41:34.620 Mitesh Patel: Yeah, and all the sort of KPIs and OKRs, KPIs I went through, they’re very operational, right? It’s to manage the channels, because
267 00:41:34.620 ⇒ 00:41:51.609 Mitesh Patel: that type of data will really… on one hand, it sounds like it’s just the basics, right? And it is, but without it, we can’t sharpen our execution. That’s our issue right now. We’re good at a lot of it, but we’re not great, we’re not excellent at any of it.
268 00:41:51.610 ⇒ 00:41:56.290 Mitesh Patel: A lot of it is all based on, you know, being able to track at this level.
269 00:41:57.760 ⇒ 00:41:58.440 Robert Tseng: Yeah.
270 00:41:58.570 ⇒ 00:41:59.170 Mitesh Patel: No.
271 00:41:59.410 ⇒ 00:42:05.430 Robert Tseng: Yeah, no, definitely on the operational side, I think you’d have to be a lot more… yeah, I mean, I think…
272 00:42:05.920 ⇒ 00:42:06.960 Robert Tseng: we have…
273 00:42:07.370 ⇒ 00:42:13.480 Robert Tseng: I think we’re tracking it as best as we can with the data that we do have. I do think that there’s always…
274 00:42:13.560 ⇒ 00:42:22.760 Robert Tseng: I mean, things like, we have enough to build this forecast, like, I believe that we have all of these inputs, it’s fine. Like, but, like, yeah, I think vial… vial size, like.
275 00:42:22.760 ⇒ 00:42:35.669 Robert Tseng: like, COGS at that granular level, we don’t have, and that’s just, like, a limitation of the system. We have to rely on assumptions, but I still think that there are very tangible ways that we can move the needle, even with the data that we have.
276 00:42:36.530 ⇒ 00:42:46.599 Robert Tseng: And then I think for this, this is really just, like, a… that’s a scoping problem. I don’t think the people who are kind of involved in this right now, Rebecca and Henry, like, are able to articulate exactly, like.
277 00:42:46.600 ⇒ 00:43:01.480 Robert Tseng: where the… where the areas of opportunity are. So, like, I don’t know, if it’s like, I need to go and spend some time there to just go and, like, identify, these are the bottlenecks that we’re gonna go and… go after, like, I will… I will… I will do that.
278 00:43:01.480 ⇒ 00:43:02.640 Mitesh Patel: Rather than not, like, SL.
279 00:43:02.640 ⇒ 00:43:05.750 Robert Tseng: I think is more straightforward. So, like, I think we can actually do this.
280 00:43:05.750 ⇒ 00:43:23.679 Mitesh Patel: Brad and I went and spent some time before the off-site at the pharmacy. Okay. And, like, you know, they haven’t done time studies, right, for each station, how much time it’s gonna take. So these are the kinds of things, like, I think Pete, their pick, pharmacist in charge, he knows, he’s done them before, and we’re.
281 00:43:23.680 ⇒ 00:43:24.000 Robert Tseng: Okay.
282 00:43:24.000 ⇒ 00:43:38.830 Mitesh Patel: well, let’s just do it here, right? And that helps us to understand exactly… because they’re like, oh, we gotta increase our output, but they’re not measuring anything yet. Yeah. And how can we optimize something that we don’t even, you know, we don’t have current
283 00:43:39.430 ⇒ 00:43:54.630 Mitesh Patel: measurements on. Anyway, and so they have a list of things to follow up. I think that’s where Brad and I will be able to help more than I think, Rebecca is able to up to this point.
284 00:43:54.810 ⇒ 00:43:55.530 Robert Tseng: Okay.
285 00:43:56.100 ⇒ 00:44:14.439 Robert Tseng: Yeah, I mean, like, and, you know, as I’m thinking about staffing on my team, like, I mean, I think… I want Henry on that, like, and, you know, proximity-wise, he lives, like, you know, if he needed to go on-site to the pharmacy, he’s in the same state. He could go hop over there, it’s, like, very easy for him.
286 00:44:14.440 ⇒ 00:44:28.519 Robert Tseng: And, like, I just… you know, all the marketing stuff is kind of… we’ve pushed over to Zoran, so, like, ops and finance is, like, his main directive at this point. So, like, I do want us to get really good at this. So…
287 00:44:28.520 ⇒ 00:44:43.110 Robert Tseng: I think if it’s just about, like, making sure that the people who are making the decisions here are all on the same page about the type of support that we can provide here, I think, like, that’s kind of what I’m hoping, like, we’ll get to, you know, heading to the new year.
288 00:44:43.200 ⇒ 00:44:43.790 Robert Tseng: Yeah, but.
289 00:44:43.790 ⇒ 00:44:45.320 Mitesh Patel: And the last item…
290 00:44:45.770 ⇒ 00:45:01.159 Mitesh Patel: sorry, that last item on the intake form improvements, I think Zoran should be on that, too. I think Henry, he’s a really good guy. He’s just struggled understanding and really helping with,
291 00:45:01.620 ⇒ 00:45:20.579 Mitesh Patel: you know, marketing in general. Yeah. And like, really, I’ll give you an example. Not to pick on him, I don’t want to… this is not a pick-on-anybody kind of a session, but just give you… I realize that I haven’t given you direct feedback, but, you know, like, a month, two months into…
292 00:45:20.650 ⇒ 00:45:26.350 Mitesh Patel: working on the Catalyst integration, he’s asking questions like, what’s CVR?
293 00:45:26.540 ⇒ 00:45:34.000 Mitesh Patel: Yeah. And, you know, I don’t expect him to come with already, you know, marketing knowledge, but…
294 00:45:34.310 ⇒ 00:45:41.309 Mitesh Patel: couple of months into a project, and as, you know, it’s just… I think it was just challenging for him.
295 00:45:41.600 ⇒ 00:45:42.230 Robert Tseng: Yeah.
296 00:45:42.230 ⇒ 00:45:42.800 Mitesh Patel: Yeah.
297 00:45:43.160 ⇒ 00:45:53.780 Robert Tseng: Yeah, I hear you on that, which is why I’ve moved all marketing stuff over to Zuran. Even, like, the relationship with Judd, I feel like, you know, we weren’t really making much momentum there, so I moved that over to…
298 00:45:53.780 ⇒ 00:45:54.250 Mitesh Patel: Yep, yep.
299 00:45:54.350 ⇒ 00:46:05.630 Robert Tseng: Like, Zuron understands all the data that we have, like, customer-wise, customer identity-wise, like, you know, these are the same projects I’ve been talking about for months that I feel like
300 00:46:05.660 ⇒ 00:46:27.300 Robert Tseng: low-hanging fruit. If we just, like, have better customer data, we should be able to drive better personalization. We’re able to impact lifecycle campaigns. So, I think, you know, we’ve moved that over to him, and yeah, I mean, at this… at this point, I think everything marketing, like, like, I think Zoran is the… is the… is the guy that I go to on our team to kind of… to press on.
301 00:46:27.300 ⇒ 00:46:28.290 Robert Tseng: Okay.
302 00:46:28.290 ⇒ 00:46:28.890 Robert Tseng: Yeah.
303 00:46:29.060 ⇒ 00:46:46.560 Robert Tseng: I guess, like, you know, I’m… you know, we’re still kind of negotiating, like, where people are staffed on our team internally. I’ve already kind of moved people in and out. And so for Henry, it’s kind of like, well, if he doesn’t have a place in operations, then I would also want to get to that conclusion, too, and if the team doesn’t want to work with him there.
304 00:46:46.560 ⇒ 00:47:06.519 Robert Tseng: that’s… that’s fine, like, I can… I can, we can… we can adjust. So between that and, like, this last tier, which is, like, product insights, or, like, A-B, like, experimentation, kind of what Adam was talking about all the time, how do we get the team to drive that, like, more, like, velocity on…
305 00:47:06.520 ⇒ 00:47:13.710 Robert Tseng: experiments in… across not just a single platform, but in V… in VWO, in… in,
306 00:47:13.710 ⇒ 00:47:38.240 Robert Tseng: in Customer I.O, on the intake side, too, like, I think there’s just, like, a process… this is, like, a change management kind of, like, project, where, like, I think the team just maybe doesn’t know how to run experiments, and, like, I think that’s… that’s, like, another area that I feel like we could still, direct Henry’s attention to. So, that’s how I’m thinking about this, like.
307 00:47:38.240 ⇒ 00:47:57.850 Robert Tseng: roadmap and planning heading into 2026, like, I want to know if these are all the areas that we’re going to be focused on, like, who the owner is, and internally, who our counterpart is on the Eden side, so that, like, you know, we’re… I think that’s what I’d like to get kind of figured out, you know, between…
308 00:47:57.850 ⇒ 00:47:59.920 Robert Tseng: That, as we’re… as we close out this year.
309 00:48:00.080 ⇒ 00:48:08.790 Mitesh Patel: Okay. Yeah. That sounds good. This whole patient care relations, that’s another really, really important area in terms of
310 00:48:09.220 ⇒ 00:48:18.430 Mitesh Patel: not just NPS, but getting feedback from patients at every, what I call, customer experience driver, right? So…
311 00:48:18.430 ⇒ 00:48:42.869 Mitesh Patel: which is, you know, everything from what our marketing campaign says is a customer experience driver, what our return policy is, what our price points are, what the packaging looks like, what, you know, those are all customer experience drivers. And we need to constantly get feedback on those, right? Yeah. Now, where it gets really interesting and valuable, I did this at Newegg, Robert, when I was in, at Newegg for a couple years.
312 00:48:43.070 ⇒ 00:48:43.620 Robert Tseng: Okay.
313 00:48:43.620 ⇒ 00:48:47.980 Mitesh Patel: As what we did is we mapped
314 00:48:49.060 ⇒ 00:48:55.370 Mitesh Patel: the… each cus… like, our score, like, we put it on a 2x2 matrix, right? Here’s our score for the…
315 00:48:55.490 ⇒ 00:49:05.410 Mitesh Patel: The… each of the customer experience drivers. And then, here’s how important each driver is to repeat order.
316 00:49:05.750 ⇒ 00:49:08.170 Mitesh Patel: Right? So, for example.
317 00:49:08.250 ⇒ 00:49:16.150 Robert Tseng: Yeah. Price is very important in the purchase decision, but it’s not at all important what we figured out in a repeat order. Yep.
318 00:49:16.150 ⇒ 00:49:29.989 Mitesh Patel: Where shipping time is… wow, right? And until I had the data analyst, like, we collected all this survey, we had so many data points, because just the company’s big, right?
319 00:49:29.990 ⇒ 00:49:30.500 Robert Tseng: Yeah.
320 00:49:30.500 ⇒ 00:49:36.890 Mitesh Patel: But until I ask them to say, okay, well, which of these matters more, for lifetime value?
321 00:49:36.990 ⇒ 00:49:51.870 Mitesh Patel: That’s the only question I asked, and they came back with this fantastic analysis, and now we know which are the customer experience drivers we need to invest in, because we know what the ROI of those are going to be.
322 00:49:51.870 ⇒ 00:49:52.210 Robert Tseng: Yeah.
323 00:49:52.210 ⇒ 00:50:10.780 Mitesh Patel: And so, you know, we didn’t talk, although I saw it on your list, you didn’t talk about it, but that, to me, is one of the big, you know, most important, sort of, line items on here. I think everything that I talked about and the other line items are… they’re obviously important, but they’re more about…
324 00:50:11.560 ⇒ 00:50:21.649 Mitesh Patel: I’ll call it measuring our performance, and making, yes, making, you know, channel investment decisions, right?
325 00:50:21.650 ⇒ 00:50:35.599 Mitesh Patel: Yeah. But when, like, when we get to stuff like this, and more predictive modeling based on, hey, this kind of customer behavior, this kind of check-in schedule, or them putting it on pause, or interacting with our email.
326 00:50:35.600 ⇒ 00:50:39.119 Mitesh Patel: Whatever, you guys figure it out. But then, based on…
327 00:50:39.310 ⇒ 00:50:58.579 Mitesh Patel: some parameter, like, you know, several parameters, you should be able to start predicting churn for us. Yeah. And saying why, and then we can go try to address that, right? That’s when I think things get really interesting, and your value, which is, you know, goes from here to, like, it skyrockets, right?
328 00:50:58.580 ⇒ 00:50:59.190 Robert Tseng: Yeah.
329 00:51:00.790 ⇒ 00:51:05.220 Robert Tseng: Yeah, I hear you. I think on the… on the customer…
330 00:51:05.220 ⇒ 00:51:29.450 Robert Tseng: I mean, this is pretty rough, we’ll kind of clean it up, but, yeah, I mean, as far as, like, what we currently gather, we don’t currently gather that much, so, like, I think even just putting surveys out there, doing, like, the basics of the NPS, and… I mean, we do have, like, their check-ins, we have check-in… patient check-in data, we have that. But yeah, other than that, we’re not really getting that much data compared to, I’m sure, what you had at Newegg.
331 00:51:29.450 ⇒ 00:51:41.100 Robert Tseng: So I do think this is really just, like, more of a greenfield opportunity right now, just to kind of start collecting more of this from customers. And I do think we should be pushing, pushing for this, yeah.
332 00:51:41.100 ⇒ 00:51:41.790 Mitesh Patel: Cool.
333 00:51:41.790 ⇒ 00:51:42.180 Robert Tseng: Yeah.
334 00:51:42.180 ⇒ 00:51:51.070 Mitesh Patel: Yeah, because, like, I mean, you have, like, side effects for drugs, you know, for 26. All of these things are, you know, based on the original, sort of.
335 00:51:51.340 ⇒ 00:52:09.550 Mitesh Patel: you know, customer data that they put in, like, about their own health and their… and then what they do. You know, because with check-ins, there are… we always ask questions about not only their dosages, but their lifestyle, how they improve their… increase their activity, whatever, whatever. Now, we get, you know.
336 00:52:09.730 ⇒ 00:52:23.980 Mitesh Patel: we have a lot of data. Not as much as we had at Newegg, but we have a lot of data. Yeah. I think that could be insightful, and we’re just not… right now, we’re not at a point of… some of it we don’t have access to, because it’s behind the frickin’ BASC, you know.
337 00:52:23.980 ⇒ 00:52:26.730 Robert Tseng: black door box, right? Yeah. But…
338 00:52:26.730 ⇒ 00:52:43.189 Mitesh Patel: You know, when we can start getting to… given the patient had this, this, these conditions, this, you know, TERS with these additives was more effective, had better efficacy, wow, that’s a kind of…
339 00:52:43.760 ⇒ 00:52:48.220 Mitesh Patel: data and analysis that help… not, you know, that gives us…
340 00:52:48.770 ⇒ 00:52:54.110 Mitesh Patel: So much more insights to help patients with their outcomes that much more.
341 00:52:54.110 ⇒ 00:52:54.680 Robert Tseng: Yep.
342 00:52:54.850 ⇒ 00:52:55.480 Mitesh Patel: Yeah.
343 00:52:56.570 ⇒ 00:53:00.120 Mitesh Patel: That, to me, I think, is a really exciting unlock.
344 00:53:00.400 ⇒ 00:53:01.080 Robert Tseng: Yeah.
345 00:53:01.080 ⇒ 00:53:01.690 Mitesh Patel: No?
346 00:53:02.240 ⇒ 00:53:17.529 Robert Tseng: Okay, got it. Yeah, I think, like, last thing I’ll say is, like, I’m gonna split this up into… there’s stuff that’s just, like, more measurement, like you said, just, like, measurement, operational, right, reviews, like, being able to kind of just continue to dial in on, like,
347 00:53:17.780 ⇒ 00:53:36.689 Robert Tseng: what… what business as usual looks like, and how do we continue to level up there? And then there’s another category that’s more of just, like, what are, like, the big bets or the greenfield opportunities where we’re able to really, like, you know, put out kind of… this is more my world when I was, like, leading data at Ruggable, like.
348 00:53:36.720 ⇒ 00:53:41.590 Robert Tseng: Yeah, data and insights. Like, I wasn’t really doing, like, the operational, like, reporting or whatever. I was, like.
349 00:53:41.590 ⇒ 00:53:42.290 Mitesh Patel: Yeah.
350 00:53:42.290 ⇒ 00:54:04.260 Robert Tseng: launching new product lines and, like, kind of, you know, opportunity-sizing markets, kind of doing what you’re saying, like, kind of finding out who are the customers that we should be talking to, that we should, you know, build, like, a whole program around so that they’re informing, like, our product strategy. Like, those types of, like, driver-based, kind of analysis, like, that’s my… that’s my wheelhouse, so…
351 00:54:04.260 ⇒ 00:54:16.429 Mitesh Patel: Yeah, that sounds like it from our previous conversations. That’s why I wanted to make sure I mentioned it to you, but yeah, and, you know, I guess I separate them into two, right? Yeah. Measurement things, like business as usual things.
352 00:54:16.430 ⇒ 00:54:16.860 Robert Tseng: Yeah.
353 00:54:16.860 ⇒ 00:54:23.899 Mitesh Patel: And then, what are the insights that give us, you know, a competitive advantage?
354 00:54:23.900 ⇒ 00:54:24.560 Robert Tseng: Yes.
355 00:54:24.730 ⇒ 00:54:25.340 Mitesh Patel: Right?
356 00:54:25.700 ⇒ 00:54:34.120 Robert Tseng: Okay, that’s a great way to frame it, and I’m going to… yeah, I’m gonna… I’m gonna go and kind of, you know, make this. We… yeah, we’ll make… we’ll make this prettier, but yeah, hopefully this will…
357 00:54:34.120 ⇒ 00:54:40.820 Mitesh Patel: Also, I don’t think you’ve shared this with me, so yeah, I’d love to kind of take a look at it in more detail.
358 00:54:40.820 ⇒ 00:54:44.759 Robert Tseng: Okay, yeah, will do. I’ll share with you,
359 00:54:47.180 ⇒ 00:54:52.490 Robert Tseng: Actually, I’ll just… I’ll just drop it in the Slack link. We’re not really sharing. Okay, yeah, so I’ll.
360 00:54:52.490 ⇒ 00:54:52.969 Mitesh Patel: Sounds good, Don.
361 00:54:54.350 ⇒ 00:54:55.250 Mitesh Patel: Cool, thank you.
362 00:54:55.250 ⇒ 00:54:56.499 Robert Tseng: Thanks, Mitesh.
363 00:54:56.500 ⇒ 00:54:57.430 Mitesh Patel: Alright, talk to you.
364 00:54:57.430 ⇒ 00:54:58.429 Robert Tseng: Talk to you soon. Bye.