Meeting Title: Eden Health Roadmapping Date: 2025-02-24 Meeting participants: Uttam Kumaran, Bo Yoon, Robert Tseng, Sahana Asokan, Awaish Kumar, Caio Velasco
WEBVTT
1 00:10:07.390 ⇒ 00:10:08.839 Uttam Kumaran: Hey? Everyone! Good morning!
2 00:10:09.570 ⇒ 00:10:09.940 Sahana Asokan: Morning.
3 00:10:11.030 ⇒ 00:10:11.430 Caio Velasco: Morning.
4 00:10:12.980 ⇒ 00:10:20.500 Uttam Kumaran: Cool. So spoke a little bit about this to the Ae team this morning. But basically today we are
5 00:10:20.610 ⇒ 00:10:22.240 Uttam Kumaran: gonna try our 1st
6 00:10:22.630 ⇒ 00:10:30.670 Uttam Kumaran: sort of go around at a bit of like a prioritization, but also like high level roadmap for Eden.
7 00:10:31.870 ⇒ 00:10:46.870 Uttam Kumaran: I sort of want to go through the existing roadmap that Robert manages, which is really like the client facing deliverables. I think what we’re gonna do after that is start to break that up into tasks across
8 00:10:47.843 ⇒ 00:10:49.110 Uttam Kumaran: data edge.
9 00:10:49.290 ⇒ 00:10:58.210 Uttam Kumaran: an analysis data modeling. And then also, I put a little bit of like up space for strategy and then basically start to break that up into timeline.
10 00:10:59.640 ⇒ 00:11:03.270 Uttam Kumaran: I think an hour is sort of plenty of time to go through that
11 00:11:03.823 ⇒ 00:11:07.540 Uttam Kumaran: but if anyone has any questions before we begin.
12 00:11:07.840 ⇒ 00:11:11.979 Uttam Kumaran: let me know. Otherwise I just sent the link in
13 00:11:12.280 ⇒ 00:11:17.500 Uttam Kumaran: zoom. But yeah, so then, is there any questions on
14 00:11:17.990 ⇒ 00:11:21.089 Uttam Kumaran: this or the goal, or anything?
15 00:11:26.670 ⇒ 00:11:29.009 Uttam Kumaran: Okay, cool. So
16 00:11:29.508 ⇒ 00:11:40.479 Uttam Kumaran: one thing that I would suggest you do is if you click on my face in the top right? Actually, I’ll I’ll just. I’ll just do this now you should be following me. If you have figma open.
17 00:11:41.560 ⇒ 00:11:43.240 Uttam Kumaran: Figmas multiplayer
18 00:11:43.480 ⇒ 00:11:52.130 Uttam Kumaran: sort of stuff is really really nice. If you click my face in the top right? And you hit Spotlight. You can start to follow me, and it’ll be helpful if you could just follow my cursor
19 00:11:52.330 ⇒ 00:11:53.300 Uttam Kumaran: for a
20 00:11:53.700 ⇒ 00:12:02.260 Uttam Kumaran: for a sec. Basically, I think everybody here, probably, except for Kyle, is familiar with the Eden client. But just to give a brief
21 00:12:02.862 ⇒ 00:12:29.857 Uttam Kumaran: brief overview, Eden is a e-commerce health company. They sell medications and plans are all related to weight loss. Particularly related to glp, one which you may have heard of recently, and we are sort of coming in to both. Take on an environment that was built, but with kind of in a very fragmented way, but also move from sort of this, like
22 00:12:30.530 ⇒ 00:12:58.560 Uttam Kumaran: data engineering cleanup world to actually actionable insights. To give you a sense of like the state of the client. I think we have been struggling a little bit over the last 2 months, not really in terms of understanding what the problem is, but just on execution. I think. Partly, we was sort of a capacity issue. So I think, of course, now we have a couple more people on the team but also we for this client in particular short term. We need to continue to work on daily
23 00:12:58.860 ⇒ 00:13:07.520 Uttam Kumaran: things that we can send to them, that we’ve that we’ve executed and done. That will be sort of the probably the mode for the next 2 weeks, while we buy some more. Trust to then
24 00:13:07.650 ⇒ 00:13:16.730 Uttam Kumaran: have a little bit longer. Sla’s the nice thing is, you know, I think we’ve been pushing out even a couple of stuff today. And so I sort of want to talk a little bit about
25 00:13:17.380 ⇒ 00:13:20.910 Uttam Kumaran: both the next 2 weeks at least.
26 00:13:21.411 ⇒ 00:13:28.049 Uttam Kumaran: I would also like to get a sense of what the next like few months are gonna look like. And then
27 00:13:28.200 ⇒ 00:13:34.160 Uttam Kumaran: at the last 10 min of the call today, we’re just gonna talk about what needs to get done between today and tomorrow.
28 00:13:34.677 ⇒ 00:13:37.182 Uttam Kumaran: So I’ll stop there. I think.
29 00:13:38.620 ⇒ 00:13:46.419 Uttam Kumaran: I mean, Robert. I could. I can just go through from my words on what I’m seeing on this this timeline. Is there anything else like I should cover.
30 00:13:47.966 ⇒ 00:13:51.689 Robert Tseng: Yeah, I mean, I kinda got a scrum it and add some stuff from
31 00:13:52.454 ⇒ 00:14:05.399 Robert Tseng: last week but none of that is really like the press. Yeah, I think all the urgent priorities are kind of reflected in that in the view now. But I could probably speak more to what’s coming, you know, a month from now.
32 00:14:07.070 ⇒ 00:14:13.457 Uttam Kumaran: Okay, great. So yeah, I think originally, right now, we are working on
33 00:14:15.040 ⇒ 00:14:24.909 Uttam Kumaran: right now, we’re working on 2 core dashboards. The product ad spend the performance dashboard and then starting to close out work around Ltv and Cac.
34 00:14:25.400 ⇒ 00:14:37.120 Uttam Kumaran: On the data modeling side. We’ve been working on a lot of stuff regarding Zendesk, bringing in Zendesk data, bringing in ship O data and then sort of building out data marts across sales, customer experience and marketing
35 00:14:37.559 ⇒ 00:14:43.310 Uttam Kumaran: so maybe I’ll just stop there if you can. You give us a sense of like what the next yeah, like, month or 2
36 00:14:43.600 ⇒ 00:15:02.399 Uttam Kumaran: is gonna look like. And then I think we can probably move into this like prior prioritization exercise. I think primarily, probably most of the people creating stuff will be between me, wish. I guess, like most of the people on this call will probably end up creating some tickets. But yeah, if you can give a sense of like what the next sort of like
37 00:15:03.020 ⇒ 00:15:08.270 Uttam Kumaran: month or 2 is gonna look like. And then we can move in into that to start to figure out what’s important right now.
38 00:15:09.180 ⇒ 00:15:26.169 Robert Tseng: Yeah, I mean, I’ll just break it down for each of these sections. I’ve kind of split it out. There’s yeah. There’s like 6 different workflows kind of that are happening. In in parallel. The way that I’ve kind of built this out is, I kind of show the team. These are like the few things that are happening at the same time.
39 00:15:26.170 ⇒ 00:15:46.919 Robert Tseng: And yeah, we obviously, we can’t work through all 6 at this simultaneously. So ends up being like maybe 2, 2 or 3 data is the perennial. It’s kind of just always there. But then, beyond that, like every function, we’re only kind of working with 2 2 groups at the same time. It’s kind of generally how
40 00:15:46.920 ⇒ 00:15:54.640 Robert Tseng: I’ve designed this. So from a marketing perspective, marketing is still their bit is still the most important client. So
41 00:15:55.275 ⇒ 00:16:02.710 Robert Tseng: just all the all the eyeballs are on on that on the developer pulls there. So any any time that there’s an adjustment we need to make
42 00:16:02.820 ⇒ 00:16:20.240 Robert Tseng: to work that we’ve already done so like the performance marketing dashboard. Yes, I understand. Our team has gone through multiple iterations at this point. But whenever there’s an adjustment that we’re asked to do there that needs to be that needs to be done asap. So it kind of the the.
43 00:16:20.900 ⇒ 00:16:36.906 Robert Tseng: With that I’m able to push other things on the roadmap back because we need to get get some of that. We need to get that done. So definitely deploying the ad spend. And Ltv kind of dashboard today is the biggest priority.
44 00:16:37.490 ⇒ 00:16:40.552 Robert Tseng: and yeah. So I’m I’ve I’m
45 00:16:41.460 ⇒ 00:16:52.600 Robert Tseng: I’m in the process of converting us to tableau like paid or whatever. And so our team will have 2 creator seats, and then we’re gonna just deploy the stuff that we’ve already been building.
46 00:16:53.440 ⇒ 00:16:55.669 Uttam Kumaran: Mind if I move these into 2 rows.
47 00:16:56.040 ⇒ 00:16:58.190 Uttam Kumaran: Yeah, yeah. Okay. Okay, cool. Okay.
48 00:16:58.190 ⇒ 00:17:00.490 Uttam Kumaran: yeah. But keep talk. Keep talking. Yeah, that’s that’s helpful.
49 00:17:01.190 ⇒ 00:17:13.429 Robert Tseng: Yeah. And then, just like other things that are coming up for for marketing, I think I sent a screenshot to Bo. There’s like this, profitability.
50 00:17:14.625 ⇒ 00:17:27.300 Robert Tseng: Kind of dashboard that’s that’s from the from the legacy looker dashboards. And that view we need to bring over. So I think that’s that’s like a piece of the migration work. I know Bo is already like
51 00:17:27.680 ⇒ 00:17:54.299 Robert Tseng: done some of the build out for the remaining tabs on the previous looker dashboard. So we just need to connect on that and and figure out like what like, how we’re going to to trickle that out. I don’t think every view needs to be brought into tableau, because most of them are not being used. But there are like are a couple views that are being used. So we just gotta make sure that those are. Those are through.
52 00:17:54.550 ⇒ 00:17:58.939 Robert Tseng: So my, in my perspective, like, this is just like the catching up work that we’re just.
53 00:17:58.940 ⇒ 00:17:59.320 Uttam Kumaran: Yes.
54 00:17:59.320 ⇒ 00:18:13.599 Robert Tseng: Bringing legacy work into this new environment with with some changes that you know are supposed to point them in the in a better direction. But there is net new work coming up as well on the on the marketing side.
55 00:18:14.277 ⇒ 00:18:42.429 Robert Tseng: So everything that we’ve been doing has really been focused on the acquisition and paid growth side. After that we’re going to move into lifecycle and retention. So some of the retention work that Bo’s already done before is gonna kind of come back. And that’s why, like Ltv. Is kind of the the next thing that we’ve been doing. So I would say for the next couple of weeks the sprint work. I’m really just going to be working with them to figure out
56 00:18:42.530 ⇒ 00:18:46.060 Robert Tseng: what we need for lifecycle retention, reporting
57 00:18:47.097 ⇒ 00:18:52.349 Robert Tseng: and then, probably a couple of weeks after that they
58 00:18:52.460 ⇒ 00:19:01.559 Robert Tseng: we’ll be doing more on the conversion and conversion rate optimization experimentation side. With Eden.
59 00:19:02.350 ⇒ 00:19:25.460 Robert Tseng: A big black box for them up to this point has been, there the intake form. I recommend everybody go on the website and just click around the website. I feel like, there, we’re not all like, yeah, I feel like there’s some basic things about this client that not everybody here understands that if you just went on the website and you understood the user experience, you would, you would you would start to pick it up.
60 00:19:26.018 ⇒ 00:19:55.379 Robert Tseng: But yeah, it’s not a traditional ecom experience, and that you just add something to checkout. And that’s it. Every every checkout requires you to fill out this pretty extensive form, because you’re getting approved by health provider, and you have to go through and talk to a health provider before you can actually make your purchase or before the order gets shipped to you. You can still make the purchase before then. But anyway, that form process we call that intake
61 00:19:55.610 ⇒ 00:20:00.379 Robert Tseng: and we have had no data around intake up until this point. And so.
62 00:20:00.380 ⇒ 00:20:00.890 Uttam Kumaran: Okay.
63 00:20:01.229 ⇒ 00:20:27.010 Robert Tseng: The Eden team has been trying to push out a custom intake form that allows them to start to extract the answers from that form. So there, there, that’s gonna be rolled out soon, and as soon as that’s rolled out, I think that will end up kind of coming to us to to need to measure. So yeah, on the marketing side, those are kind of the main things that are coming.
64 00:20:29.730 ⇒ 00:20:43.220 Robert Tseng: and then on the customer support side. Yeah, I would go. I would lump customer support or like customer experience whatever, and and a pharmacy ops together. I think
65 00:20:43.380 ⇒ 00:20:46.980 Robert Tseng: I think Sahana did a really good job with the the dashboard design.
66 00:20:47.130 ⇒ 00:20:51.229 Robert Tseng: I think she was like, I think she handled it correctly in that
67 00:20:52.006 ⇒ 00:21:07.839 Robert Tseng: there’s a lot of overlap. And what we actually need is a unified view of these 2 of these 2 functions, because right now they both report on metrics separately, like Member experience only uses Zendesk reporting. And it’s like.
68 00:21:07.960 ⇒ 00:21:10.190 Robert Tseng: Okay, you could be looking like
69 00:21:10.430 ⇒ 00:21:31.109 Robert Tseng: a rock star in Zendesk, and that you’re responding to support tickets very quickly. But if on the pharmacy upside, you’re way behind sla on fulfilling like the the requests, then you’re actually like overall doing a bad job right? And there isn’t really like that synchronization right now, where there’s like a
70 00:21:31.440 ⇒ 00:21:55.199 Robert Tseng: holistic view of like customer experience, because one side only looks at tickets and ticket resolution. And the other side is all about fulfillment. So I think Sahana’s dashboard is really about bringing those 2 data sources kind of together in one view, so that they can start to like, think about customer experience more holistically.
71 00:21:55.563 ⇒ 00:22:03.420 Robert Tseng: So I’m sure once she puts out a v, 1 of that. There’s gonna be a lot of eyes on that. And that’s gonna kind of start to take off.
72 00:22:07.240 ⇒ 00:22:14.770 Robert Tseng: yeah. So I think those are the main things. I don’t wanna overwhelm with too much information. But yeah, I would say.
73 00:22:15.110 ⇒ 00:22:22.219 Robert Tseng: that’s kind of how I see the next few weeks coming for all the work that we’re already touching right now.
74 00:22:22.880 ⇒ 00:22:28.339 Uttam Kumaran: Do you have a sense of like for marketing like is a lot is like, what’s next
75 00:22:28.740 ⇒ 00:22:30.420 Uttam Kumaran: on the marketing side?
76 00:22:32.430 ⇒ 00:22:42.720 Uttam Kumaran: or should we push that conversation to a different time. I kind of get how the customer experience and farm ops like a lot of march, is going to be them, probably just iterating on
77 00:22:44.150 ⇒ 00:22:46.350 Uttam Kumaran: like the dashboard.
78 00:22:46.550 ⇒ 00:22:50.730 Uttam Kumaran: But is there anything passed on on marketing?
79 00:22:51.260 ⇒ 00:22:55.329 Uttam Kumaran: Or and also this met Ops piece, which I don’t think we we covered right now.
80 00:22:59.200 ⇒ 00:23:02.510 Robert Tseng: Yeah, Medops was really just like
81 00:23:03.440 ⇒ 00:23:07.210 Robert Tseng: order fulfillment tracking and stuff. I I think.
82 00:23:10.810 ⇒ 00:23:16.100 Robert Tseng: yeah, I I mean, we’re gonna bring. We’re bringing Chippo data in. And we’re gonna start to have like A,
83 00:23:16.390 ⇒ 00:23:18.480 Robert Tseng: we need to have like an order
84 00:23:18.600 ⇒ 00:23:22.740 Robert Tseng: journey end to end model. I
85 00:23:23.630 ⇒ 00:23:39.750 Robert Tseng: I feel like I don’t want to commit to that. I feel like our hands are very full with just the marketing and the other stuff. Like, I, yeah, I’m comfortable pushing that off, and I think we can scope it out. And I know what this, what this data model should look like. I built the same thing at at ruggable. So
86 00:23:39.750 ⇒ 00:23:56.940 Robert Tseng: I think I wanna I wanna use it as an opportunity to to upsell. And it’s like, Hey, if you want us to take on this work in parallel as well. We need, we need, we need more from from your budget as well. So, that’s yeah, like, that’s kind of how I want to handle that that work. But yeah, I mean.
87 00:23:57.170 ⇒ 00:24:16.669 Robert Tseng: once the shipbo data comes in like it will unlock some work on the marketing side, too. Bo needs. Bo wants to run that geolift study. It’s yeah. It’s kind of important for us to to do some of the attribution work on the to the next stage. So I think the marketing work
88 00:24:16.990 ⇒ 00:24:19.117 Robert Tseng: like I mentioned is,
89 00:24:19.920 ⇒ 00:24:24.830 Robert Tseng: you know, up to this point we’ve been really focused on acquisition and paid growth reporting.
90 00:24:24.940 ⇒ 00:24:30.750 Robert Tseng: There’s gonna continue to be iterations of that. As we bring in more data and run some of these studies.
91 00:24:31.679 ⇒ 00:24:46.100 Robert Tseng: so the attribution model is gonna get more complex. The Ltv, the Ltv model is gonna gonna need an improvement as well, because we’re not doing any predictive. Ltv, right now. And once we can bring like
92 00:24:46.470 ⇒ 00:24:56.819 Robert Tseng: Bose, predicted Ltv. Or forecasted Ltv. Into the model, and have, like a predicted Ltv. Field like that, will change the way that we report on it as well.
93 00:24:57.199 ⇒ 00:25:16.690 Robert Tseng: And then after that, it’s lifecycle and retention which I don’t have specific projects to kind of queue up there yet. I know what metrics that they would be looking at, and I have to start scoping all that out this week. Probably. But yeah, it’s just that we’ve we’ve still been lagging and catching up on stuff. So I haven’t really
94 00:25:16.780 ⇒ 00:25:18.849 Robert Tseng: queued up everything on the marketing side.
95 00:25:19.410 ⇒ 00:25:27.549 Uttam Kumaran: Okay? And then I see this is finance like, do you see sales broken out as part of that like cause? I see finance more as like.
96 00:25:27.910 ⇒ 00:25:31.420 Uttam Kumaran: like, potentially like even the accounting team. Or do you see this as like.
97 00:25:31.740 ⇒ 00:25:37.120 Uttam Kumaran: is this like, would you see this as like growth or sales? Or is this that legit like the finance team.
98 00:25:37.570 ⇒ 00:25:40.709 Robert Tseng: Well, I think I put finance there, because, like the whole.
99 00:25:40.710 ⇒ 00:25:41.779 Uttam Kumaran: How they talk about it!
100 00:25:41.780 ⇒ 00:25:44.260 Robert Tseng: Between like
101 00:25:44.890 ⇒ 00:26:01.363 Robert Tseng: orders and transactions and getting all that really clear, I think, is important for the finance team, because right now, you know, before our work. They were just running everything off of like order totals. And I mean, the finance team didn’t really like that at all. Obviously, so.
102 00:26:01.860 ⇒ 00:26:14.779 Robert Tseng: yeah, I don’t think there is really a traditional like sales function here. It’s all like marketing driven sales. So I figured the sales data. Mart was more of a like finance is the one that needs to really be okay with the way that we’re
103 00:26:15.270 ⇒ 00:26:16.940 Robert Tseng: kind of splitting this stuff up.
104 00:26:17.380 ⇒ 00:26:22.199 Uttam Kumaran: And then my last question is like, Do, where do? Who do these 2 roll up to.
105 00:26:23.270 ⇒ 00:26:25.900 Robert Tseng: Same same person. These are those are both. Rebecca.
106 00:26:27.020 ⇒ 00:26:31.690 Uttam Kumaran: But then like, do they roll? Is this, does this roll up to like? Is this product? Then kind of.
107 00:26:33.680 ⇒ 00:26:37.439 Uttam Kumaran: yeah, can you talk through like how we would, we would try to map this to like traditional
108 00:26:37.770 ⇒ 00:26:47.820 Uttam Kumaran: yeah, just like a traditional or cause. I I sort of want to try to categorize these on the data side by like, where are their data sources coming from and which mart they’re pulling from.
109 00:26:48.270 ⇒ 00:26:52.229 Robert Tseng: No, I think farm Ops, Med. Ops is just Ops, like, it’s a traditional Ops function.
110 00:26:52.230 ⇒ 00:26:55.659 Robert Tseng: Okay? Okay, yeah. One is like
111 00:26:56.415 ⇒ 00:27:04.080 Robert Tseng: that mean the operate farm Ops is more like bye.
112 00:27:04.840 ⇒ 00:27:08.070 Uttam Kumaran: They’re just keeping the lights on in terms of like executing. Yeah.
113 00:27:09.310 ⇒ 00:27:12.685 Robert Tseng: Yeah, I’m just saying, like, because, yeah, there’s there’s
114 00:27:13.300 ⇒ 00:27:27.730 Robert Tseng: There’s like the processing the order, getting the approval from the pharmacist. And like, there’s that part of the operation, and then the Med. Ops is really just the fulfillment side. It’s like, once you ship it out like, how does it get to your customer. So that’s kind of why they’ve broken it out. Yeah.
115 00:27:28.534 ⇒ 00:27:36.550 Robert Tseng: there isn’t really a formal product function yet. They just stood up. They just had a opened up a role for a product owner.
116 00:27:37.295 ⇒ 00:27:56.010 Robert Tseng: So I think it’s very silver closely tied to marketing. But that’s when they’re starting to think about holistic product profitability. And they they want that variant level product profitability model like in some of the reports now, so.
117 00:27:56.560 ⇒ 00:27:59.240 Uttam Kumaran: Okay, I’ll just leave one here
118 00:27:59.640 ⇒ 00:28:03.740 Uttam Kumaran: and that way, cause we are working on like
119 00:28:03.860 ⇒ 00:28:10.810 Uttam Kumaran: dim products, product level profitability. I sort of see that laddering to both marketing and
120 00:28:11.230 ⇒ 00:28:32.250 Uttam Kumaran: product. So I’ll sort of leave this one here. But this is great. So one exercise I want to go through, and maybe we can just spend a quick 10 min on it. And I think this will give us a really great visual of what’s important here is just filling these out, I think, for you, Robert, in particular. Anything that you’re we’re sort of like lifecycle and retention. We need to figure that out lump that into like
121 00:28:32.830 ⇒ 00:28:36.269 Uttam Kumaran: strategy. And maybe we can call this even strategy and planning
122 00:28:37.322 ⇒ 00:28:42.079 Uttam Kumaran: and then I would say, for a wish and for Kyle.
123 00:28:42.518 ⇒ 00:28:55.710 Uttam Kumaran: I think our world. Let’s talk about what needs to happen for data, engineering and data modeling. And then I think, Beau and Sahana, you’re on the phone if you can. If you guys can create stickies for analysis.
124 00:28:55.870 ⇒ 00:29:17.980 Uttam Kumaran: I wanna cover here any work that’s in flight. Right? So I mean a lot. I would say, some of this. What we’re gonna do now is just basically moving these to stickies. But also anything in anybody’s world that’s coming up. I want it to end up on a sticky and then we can basically, we’ll just run through with the data team on the call. How what the priority is versus
125 00:29:18.170 ⇒ 00:29:45.339 Uttam Kumaran: like how possible it is and feasibility I think we could maybe just talk briefly about before we sort of run into this. There’s both a like a time component as well as a do. We have all the ingredients on the data side to execute it. I think this is probably where I will chime in but then, also, anyone who’s sort of leading across data, modeling or analysis, that’s what we want to provide to the business. Aka, Robert is like.
126 00:29:45.420 ⇒ 00:29:52.330 Uttam Kumaran: Is this a really hard thing, or really easy thing? And of course, ideally, we will be working on these things first.st
127 00:29:53.056 ⇒ 00:29:54.289 Uttam Kumaran: And then
128 00:29:54.440 ⇒ 00:30:09.721 Uttam Kumaran: probably these things and then work our way down. Cool. So if we could just take 5 min, and we just like hammer a bunch of these ticket bunch of these post its out that would be really great. I’m gonna play some music. And then,
129 00:30:10.580 ⇒ 00:30:13.319 Uttam Kumaran: yeah, let’s just try to how to do that.
130 00:31:04.060 ⇒ 00:31:07.599 Uttam Kumaran: And you guys can just copy stickies and create new ones.
131 00:31:08.610 ⇒ 00:31:09.410 Uttam Kumaran: Yeah.
132 00:32:54.090 ⇒ 00:33:00.719 Bo Yoon: Hey, Tom? Sorry. It’s my 1st time using. Can you give me access to? To add it.
133 00:33:00.720 ⇒ 00:33:01.240 Uttam Kumaran: Yes.
134 00:33:01.240 ⇒ 00:33:02.169 Bo Yoon: I just created.
135 00:33:02.170 ⇒ 00:33:03.590 Uttam Kumaran: I I just did.
136 00:33:04.200 ⇒ 00:33:05.289 Bo Yoon: Okay. Thank you.
137 00:33:05.590 ⇒ 00:33:06.320 Uttam Kumaran: Yeah, no problem.
138 00:33:08.710 ⇒ 00:33:09.960 Uttam Kumaran: I love figma.
139 00:33:11.520 ⇒ 00:33:16.440 Uttam Kumaran: Well, actually, I just love the fig jam. I’m I don’t use figma that much. It’s hell. It’s actually really hard.
140 00:33:17.940 ⇒ 00:33:19.166 Bo Yoon: Seems cool.
141 00:33:22.090 ⇒ 00:33:27.149 Uttam Kumaran: Otherwise. Yeah, I would buy a big whiteboard, and we could do this on a whiteboard. But this is nice.
142 00:35:04.270 ⇒ 00:35:06.619 Uttam Kumaran: okay, we have 15 seconds.
143 00:35:19.570 ⇒ 00:35:22.700 Uttam Kumaran: Okay, how do we feel? Do we need more time
144 00:35:25.590 ⇒ 00:35:27.729 Uttam Kumaran: like another 30 seconds, are we good?
145 00:35:28.400 ⇒ 00:35:35.089 Uttam Kumaran: But I like, I wanna I want everything here, even if, like, we don’t talk about everything. It’s fine, just like brain dump.
146 00:35:36.820 ⇒ 00:35:39.170 Uttam Kumaran: We’ll probably try to do this again next week. So
147 00:35:39.820 ⇒ 00:35:46.289 Uttam Kumaran: just like flow like like you’re in the Sauna, just like expunge all the toxins onto this board, please.
148 00:35:51.040 ⇒ 00:35:52.359 Uttam Kumaran: How do you feel? We feel good.
149 00:35:52.550 ⇒ 00:35:54.140 Robert Tseng: Run it like Yoga studio.
150 00:35:54.530 ⇒ 00:35:55.400 Uttam Kumaran: Yes.
151 00:35:56.291 ⇒ 00:36:05.300 Uttam Kumaran: I I could. I could play more ambient music if that’s what we want. I I like that. You know. It’s like, yeah, you’re in the if you’re at the Spa, you’re in the lobby of the Spa.
152 00:36:08.253 ⇒ 00:36:30.036 Uttam Kumaran: Okay, cool. So I think, let’s go through this. I’m just gonna work on dragging items into here in order of importance for me, I want to go through analysis first, st then data modeling, then strategy, then data engineering. So let’s talk about like analysis pieces,
153 00:36:30.800 ⇒ 00:36:39.990 Uttam Kumaran: farm Ops, areas of opportunity, worse versus weakness. I guess if if you want, if whoever created the ticket. Of course, if, unless it’s like really obvious, you want to just take
154 00:36:40.110 ⇒ 00:36:45.490 Uttam Kumaran: like 5, 10 seconds, just to give me, like the elevator pitch on it, and then we’ll move it to the board.
155 00:36:46.640 ⇒ 00:36:50.580 Sahana Asokan: Yeah, I think this one is more. The requirements aren’t really
156 00:36:50.760 ⇒ 00:36:59.990 Sahana Asokan: set my idea. For once I actually built out the cx and pharmacist ops dashboards to kind of understand like
157 00:37:00.090 ⇒ 00:37:09.089 Sahana Asokan: from like an efficiency or workflow perspective, like, where should what like? What should that specific team be focusing on like what is working, what is not really working?
158 00:37:09.130 ⇒ 00:37:35.009 Sahana Asokan: What pharmacies do we see a lot of success with like, are there any trends there? So that’s kind of what I was thinking like, more of like an exploratory analysis. And then, similarly, like, what’s not working right like from an sla perspective? Where do they need to focus on all that fun stuff. So that’s kind of what that analysis was for. But again, I don’t know what exactly the scope is, just cause. I don’t know the data. So yeah.
159 00:37:35.010 ⇒ 00:37:38.959 Uttam Kumaran: Okay, I’m just gonna move this to this area. This seems like
160 00:37:39.180 ⇒ 00:37:48.140 Uttam Kumaran: before we can. I mean, we are starting to work on stuff for them already. But I think this is helpful to to put in like a high priority area.
161 00:37:48.360 ⇒ 00:37:49.740 Uttam Kumaran: Great. And then
162 00:37:50.230 ⇒ 00:37:57.300 Uttam Kumaran: and then we’ll also group. I’m sure there’s some duplication. So cx churn analysis. If you could just yeah talk through that. So I wanted to.
163 00:37:57.850 ⇒ 00:38:06.350 Sahana Asokan: I think that one is more for customers who are turning like, how has like customer experience engaged like what has engagement really look like.
164 00:38:06.450 ⇒ 00:38:29.300 Sahana Asokan: you know, I really understanding what they should be working towards to help retention or stickiness for these specific customers. I think the the sec. The segue is also understanding. If if people are churning because of the product, or if they’re churning for other reasons, and how can we quantify that? Where that what I was thinking for that exercise.
165 00:38:29.670 ⇒ 00:38:40.059 Uttam Kumaran: So I kind of put all these in as like customer churn. I think it would probably requires some work on us to give. Put all the customer related data orders.
166 00:38:40.320 ⇒ 00:38:58.669 Uttam Kumaran: the time between different order phases, all of where they came from, what they bought and support. Like, we sort of need to have all that for you to begin to look at the features. So I’m gonna kind of lump these all these 3 together. I feel like these are the only kind of like customer
167 00:38:59.590 ⇒ 00:39:03.359 Uttam Kumaran: specific ones if I’m seeing everything. So I’m gonna sort of
168 00:39:03.590 ⇒ 00:39:09.170 Uttam Kumaran: keep this here. I I’m just gonna keep moving to where I think things things.
169 00:39:09.760 ⇒ 00:39:16.140 Uttam Kumaran: Things are like things are, and we can sort of reassess as we go. I think we actually have a lot of this data.
170 00:39:16.715 ⇒ 00:39:30.700 Uttam Kumaran: I think there’s probably one piece I’m still concerned about. But like, I feel like we, we have a lot of these. I think it probably requires a little bit of modeling work to hand that over to you. For then you to be able to see every customer and the events they went through. So I’m gonna leave that here.
171 00:39:30.990 ⇒ 00:39:33.560 Uttam Kumaran: I think, yeah, go ahead.
172 00:39:33.850 ⇒ 00:39:37.490 Sahana Asokan: Brought this up is, I have created the mock ups for
173 00:39:38.070 ⇒ 00:40:00.937 Sahana Asokan: whatever data like modeling that we need to essentially get to this analysis portion. So I think going through those mock ups will give you a good idea of like, what is the data we need and like what it the format of how it needs to look like right? So I think forever, who is whoever is working on this like like Zendesk data ship? O data, bass data. What
174 00:40:01.670 ⇒ 00:40:14.409 Sahana Asokan: I think it’s really worthwhile to look at these mock ups. Understand? Like, okay, this is the kind of format we need all of this data in, because I think that’ll give you a starting point for what? Like what? You want the output to be? Yeah.
175 00:40:15.290 ⇒ 00:40:19.340 Uttam Kumaran: Can you? If you just have that link handy, I’m just gonna literally put the link.
176 00:40:20.220 ⇒ 00:40:20.640 Sahana Asokan: Jerry.
177 00:40:20.640 ⇒ 00:40:23.870 Uttam Kumaran: Or just underneath one of these, and then I’m gonna bookmark it as well.
178 00:40:24.380 ⇒ 00:40:30.599 Sahana Asokan: Yeah, sure, I put it in the Eden client, Eden Channel. Let me find it and send it over.
179 00:40:42.951 ⇒ 00:40:47.530 Sahana Asokan: Yeah, kind of the 2 ones for me, at least, I see
180 00:40:47.660 ⇒ 00:40:52.569 Sahana Asokan: in the in the next 2, 3 weeks. So it’s I would say. Both of them are pretty high priority.
181 00:40:53.730 ⇒ 00:40:56.750 Uttam Kumaran: Okay, I think the this is basically that as well.
182 00:40:59.790 ⇒ 00:41:01.749 Uttam Kumaran: Cool. And then,
183 00:41:04.270 ⇒ 00:41:11.190 Uttam Kumaran: yeah, we have a tableau profitability dashboard by metrics and products. So this is sort of like.
184 00:41:11.770 ⇒ 00:41:15.960 Uttam Kumaran: this is is this, this is all. Is this product and sort of channel
185 00:41:16.440 ⇒ 00:41:23.530 Uttam Kumaran: profitability? Or is this, I guess? Oh, this is just product and plan. Okay, so I’m gonna sort of consider this as everything related to
186 00:41:23.800 ⇒ 00:41:25.470 Uttam Kumaran: like product.
187 00:41:26.098 ⇒ 00:41:30.260 Uttam Kumaran: So let me lump this in. Let me lump this in.
188 00:41:32.230 ⇒ 00:41:35.370 Uttam Kumaran: I’m gonna lump this in because I’m still sort of like
189 00:41:35.630 ⇒ 00:41:41.450 Uttam Kumaran: every day I sort of after kind of like run through the exercise of like, how their product structure works.
190 00:41:42.013 ⇒ 00:41:47.389 Uttam Kumaran: So I’m gonna put this into here. I’m also gonna put in.
191 00:41:48.508 ⇒ 00:41:50.800 Uttam Kumaran: Okay, this is all fine.
192 00:41:52.710 ⇒ 00:41:58.690 Uttam Kumaran: Okay? So I feel like, that’s all related to product profitability.
193 00:42:00.080 ⇒ 00:42:09.770 Uttam Kumaran: this is more like customer. This is more like a. So this is all like Ltv related stuff. Right? Let this one predictive. Lt, ltv.
194 00:42:13.550 ⇒ 00:42:17.075 Uttam Kumaran: I think that’s probably that.
195 00:42:18.350 ⇒ 00:42:24.980 Uttam Kumaran: okay. And then we only have one piece here related to marketing.
196 00:42:26.430 ⇒ 00:42:28.350 Uttam Kumaran: This is north beam data.
197 00:42:29.060 ⇒ 00:42:31.430 Uttam Kumaran: Yeah, I’m gonna move this down here.
198 00:42:32.185 ⇒ 00:42:42.924 Uttam Kumaran: I hear you a wish. I actually also suggested this. I think we’re going to probably do this more piecemeal as we build confidence in just our marketing reporting in general.
199 00:42:43.370 ⇒ 00:43:03.830 Uttam Kumaran: we will then we can start to go direct to source to get that. Because right now, yeah, we are getting all of the data through north. Theme when we could go get this independently, and probably provide a little bit more structure. But I would. I think we. I want to pitch this and find out, like what exactly we could improve by getting these directly.
200 00:43:05.570 ⇒ 00:43:11.419 Uttam Kumaran: The other thing is, yeah, bass data really sucks. I think we need to figure out
201 00:43:11.890 ⇒ 00:43:13.921 Uttam Kumaran: like, I, I think this is
202 00:43:15.390 ⇒ 00:43:21.159 Uttam Kumaran: I think this is lower priority, although, like probably high feasibility to just like, get a really good ownership over, like
203 00:43:21.520 ⇒ 00:43:24.459 Uttam Kumaran: all the data we’re getting from them. And like what we’re missing.
204 00:43:24.580 ⇒ 00:43:28.327 Uttam Kumaran: I want to know things about basically, I want to know,
205 00:43:29.110 ⇒ 00:43:37.350 Uttam Kumaran: the like velocity, like, how are basically like timeliness, I guess, typically called freshness.
206 00:43:39.860 ⇒ 00:43:42.270 Uttam Kumaran: Do we have everything?
207 00:43:43.480 ⇒ 00:43:48.589 Uttam Kumaran: Is it accurate, I guess, has to be accurate. But like, I want to know those things about that
208 00:43:50.460 ⇒ 00:43:52.499 Uttam Kumaran: customer support mart.
209 00:43:53.040 ⇒ 00:43:55.150 Robert Tseng: Cx, data, modeling.
210 00:43:56.075 ⇒ 00:43:58.300 Uttam Kumaran: Those are probably the only
211 00:43:59.320 ⇒ 00:44:11.649 Uttam Kumaran: that’s probably everything related to customer experience. Right? Is the farm Ops data modeling. Is this real? Is this more related to like pharmacy, Sahana? Or is this all? Is this also related to sort of Zendesk
212 00:44:11.920 ⇒ 00:44:12.530 Uttam Kumaran: work?
213 00:44:13.690 ⇒ 00:44:23.020 Sahana Asokan: There’s definitely some overlap. Yeah, I think they’re interested in some of the Zendesk data, but also
214 00:44:23.230 ⇒ 00:44:30.380 Sahana Asokan: a lot of the data on the customer journey side. So I think, as Robert said, there’s definitely like some overlap between both.
215 00:44:30.590 ⇒ 00:44:46.870 Sahana Asokan: Essentially the pharmacy. The farm Ops people just kind of want to understand some of them, some of the same metrics sliced by the specific pharmacy, or given like looking at specific pharmacy. Sla, so that’s where there overlap between them.
216 00:44:49.330 ⇒ 00:44:51.439 Uttam Kumaran: Okay, I’m just gonna move this here.
217 00:44:51.620 ⇒ 00:45:04.780 Uttam Kumaran: I just requested access to this one. By the way, if you can, just if you have a second to just hit that. Okay? So I’m sort of gonna move this farm Ops data modeling also around
218 00:45:05.040 ⇒ 00:45:06.960 Uttam Kumaran: sort of customer support.
219 00:45:07.758 ⇒ 00:45:09.550 Uttam Kumaran: I think these are all
220 00:45:09.760 ⇒ 00:45:14.538 Uttam Kumaran: sort of related. I mean, I think they’re probably gonna want. They’re gonna just wanna see pharmacy related dimensions.
221 00:45:16.770 ⇒ 00:45:19.520 Uttam Kumaran: Right? So we probably need some sort of pharmacy model
222 00:45:19.760 ⇒ 00:45:23.799 Uttam Kumaran: as well. But I’ll kind of leave this now. So we can talk through this.
223 00:45:24.365 ⇒ 00:45:28.730 Uttam Kumaran: I think this is pretty feasible, because that data is actually pretty easy.
224 00:45:30.063 ⇒ 00:45:35.429 Uttam Kumaran: Okay. Let’s talk about a waste. Do you want to talk through
225 00:45:35.860 ⇒ 00:45:40.190 Uttam Kumaran: through this? This is sort of also my piece around?
226 00:45:42.280 ⇒ 00:45:47.950 Uttam Kumaran: so it’s something around like very clear understanding of, like the different scenarios.
227 00:45:48.702 ⇒ 00:45:50.829 Uttam Kumaran: But yeah, do you want to talk through this one.
228 00:45:51.980 ⇒ 00:45:53.360 Awaish Kumar: Sorry, which one.
229 00:45:54.110 ⇒ 00:45:56.320 Uttam Kumaran: Clearly clear definition of metrics.
230 00:45:57.814 ⇒ 00:46:07.530 Awaish Kumar: Yeah, like sometimes there are different scenarios. And and if we are maybe sure.
231 00:46:07.650 ⇒ 00:46:12.340 Awaish Kumar: like without examples, when we are talking in plain English, like.
232 00:46:13.020 ⇒ 00:46:13.420 Uttam Kumaran: Yeah.
233 00:46:13.420 ⇒ 00:46:15.180 Awaish Kumar: Sometimes we we.
234 00:46:15.490 ⇒ 00:46:18.720 Awaish Kumar: So I think of thing things differently.
235 00:46:18.960 ⇒ 00:46:21.929 Awaish Kumar: I just want that. We if we have some.
236 00:46:22.250 ⇒ 00:46:25.839 Awaish Kumar: a Google sheet or something where we take a sample and try to
237 00:46:26.270 ⇒ 00:46:31.710 Awaish Kumar: come up with some like. How we want the calculation to go go through.
238 00:46:31.870 ⇒ 00:46:33.810 Awaish Kumar: That would be nice.
239 00:46:34.810 ⇒ 00:46:45.269 Robert Tseng: Yeah, I think a wish last time we did like the the sample and the Google Sheet, I think, was that you said it looked like that was helpful for you, so I feel like we should be doing that more often.
240 00:46:45.890 ⇒ 00:46:47.629 Awaish Kumar: Yeah, yeah, it was helpful.
241 00:46:47.990 ⇒ 00:46:48.630 Robert Tseng: Yeah.
242 00:46:50.410 ⇒ 00:46:55.590 Uttam Kumaran: Yeah, I think, like, these, I’m going to put all in this right corner.
243 00:46:55.880 ⇒ 00:47:06.159 Uttam Kumaran: And then I I think it’s sort of is like basically going like this is how I would think of it in terms of priorities. This will solve like will buy us a lot of time and solve like a ton of problems.
244 00:47:06.789 ⇒ 00:47:18.509 Uttam Kumaran: Because I’m sure when, if we produce a version of this that works for us when we show it to them, there’s probably going to be some feedback on like, oh, this is actually how it works. And then this, I think
245 00:47:18.940 ⇒ 00:47:24.590 Uttam Kumaran: we want to just hit this over the head with as much as possible, and then we’re going to be working towards
246 00:47:24.890 ⇒ 00:47:27.550 Uttam Kumaran: farm Ops customer service things like that.
247 00:47:28.096 ⇒ 00:47:31.820 Uttam Kumaran: So then let’s keep going on this. So yeah, I also, I don’t know.
248 00:47:32.220 ⇒ 00:47:34.119 Uttam Kumaran: Robert, how you’re sort of doing
249 00:47:34.610 ⇒ 00:47:39.480 Uttam Kumaran: this, but I’m sort of gonna bring this up across all clients. This
250 00:47:41.620 ⇒ 00:47:48.679 Uttam Kumaran: today. But it’s mainly around like how we are presenting like, is there? Is there a good structure for you to not only present.
251 00:47:48.880 ⇒ 00:47:51.309 Uttam Kumaran: but more like reactive work.
252 00:47:51.610 ⇒ 00:47:53.409 Uttam Kumaran: It’s like, I kind of look at it as like
253 00:47:53.640 ⇒ 00:47:55.220 Uttam Kumaran: work. We’re on the hook for
254 00:47:55.360 ⇒ 00:47:58.450 Uttam Kumaran: data engineering stuff they may not like. See?
255 00:47:58.570 ⇒ 00:48:01.429 Uttam Kumaran: Like how the sausage is made type of work, and then also
256 00:48:01.970 ⇒ 00:48:30.870 Uttam Kumaran: proactive work. And I think that kind of covers all bases. I assume you’re you’re probably handling that in in some manner. But I’m I’m just gonna throw the question out on, like how we’re if we’re able to run these Qbrs or data reviews where we present you know something. My, I guess my last piece there is. I do think that it’s nice when we take on analysis is to basically run through the entire journey kind of like hypothesis. What do we do? What do we find out? Here’s the answer. Here’s a recommendation.
257 00:48:31.235 ⇒ 00:48:38.390 Uttam Kumaran: We aren’t doing, you know, kind of that full right now. But yeah, I guess that’s just a lot of stuff around data reviews and Qbrs.
258 00:48:41.430 ⇒ 00:48:48.286 Robert Tseng: Yeah, I mean, I think the best example of when we’ve done this hasn’t actually been with any of our active clients like I I think.
259 00:48:49.200 ⇒ 00:48:54.560 Robert Tseng: perhaps in more narrow scope, but like with some of our other like kind of past clients.
260 00:48:55.150 ⇒ 00:49:09.720 Robert Tseng: Yeah, we got to a point where there were like a couple sets of dashboards that we had just like locked in with the client. And weekly. I would just review with them. Okay, things went up or down and like why, and we would have ad hoc
261 00:49:09.820 ⇒ 00:49:19.869 Robert Tseng: analysis to explain why, and there’d be recommendations on like what to do to to reverse some of the the impact, some of those trends. So
262 00:49:19.920 ⇒ 00:49:45.950 Robert Tseng: to me, like, we’re, we’re not really at a place yet where we have like things to consistently show them on a weekly basis. That’s why, even in my timeline view, I kind of like based it out like, yeah, reporting insights. And like, I don’t really think we’re at the insights point that being said like, we do need to have, like some structure around it now, especially since we handle so many different data sources for clients like.
263 00:49:45.950 ⇒ 00:49:46.350 Uttam Kumaran: Yes.
264 00:49:46.350 ⇒ 00:49:47.450 Robert Tseng: And Bobby.
265 00:49:47.994 ⇒ 00:49:53.080 Robert Tseng: I think there was like, yeah, I think every time I mentioned to you there’s a request to be like
266 00:49:53.170 ⇒ 00:50:11.309 Robert Tseng: I don’t know what this would look like, but if we could just show them like some metrics on every, some kpis on every data, source on like this is how often it’s coming in. This is how much you trust data. This is what we’re working on, just so that they have like a data health check. I feel like that should be part of the review as well.
267 00:50:11.726 ⇒ 00:50:26.210 Robert Tseng: So that we can kind of show over time how confidence in these data sources is like is is increasing or whatnot. So, yeah, I I think we need to like figure out what that structure looks like. But to include both
268 00:50:26.550 ⇒ 00:50:41.249 Robert Tseng: like visibility into the data engineering side and what we’re doing to improve confidence there and then also like, get to a point where we’re reviewing just a set of reports regularly, and using that to guide
269 00:50:41.400 ⇒ 00:50:44.280 Robert Tseng: what ad hoc analyses, we run.
270 00:50:46.950 ⇒ 00:50:58.959 Uttam Kumaran: Okay. Great, yeah, that’s that’s really helpful, like, I wanna know, because I think it’s often hard for clients to empathize with this other work. It’s just really technical, however, it is.
271 00:50:59.460 ⇒ 00:51:04.050 Uttam Kumaran: it is, I would say, roughly equivalent to the amount of work we’re doing in the analysis side. However.
272 00:51:04.530 ⇒ 00:51:08.190 Uttam Kumaran: in order to remove release. Some of that pressure I want to start to have.
273 00:51:08.480 ⇒ 00:51:36.869 Uttam Kumaran: even if we sort of come to the table like, here’s an item we worked on this entire pop section. And we’re always going to be working on stuff that like may not be entirely obvious. So yeah, I think one thing is a data health and sort of data pricing like vendor. I don’t know data source or something where we have all the sources are like confidence, which is like green, yellow, red. And then information about how much data is coming in. What sort of data it is? How the cost.
274 00:51:37.365 ⇒ 00:51:46.234 Uttam Kumaran: Who’s our contact? Things like that? I have. You know, I have that already in mind. So I will. We can get that. I think that’s super feasible.
275 00:51:47.550 ⇒ 00:51:48.790 Uttam Kumaran: okay, perfect.
276 00:51:49.200 ⇒ 00:51:50.010 Robert Tseng: Okay.
277 00:51:50.270 ⇒ 00:51:56.969 Uttam Kumaran: Yeah, I’m I’m also gonna just move these 2 1, i think, for the aes on the call, we need to start thinking about some
278 00:51:57.200 ⇒ 00:52:01.020 Uttam Kumaran: degree of data testing I don’t know
279 00:52:01.360 ⇒ 00:52:07.790 Uttam Kumaran: what the feasibility is, but I do want to do something here. I’m gonna put this right in the middle
280 00:52:09.280 ⇒ 00:52:28.560 Uttam Kumaran: because we’re gonna we want to start doing this across all clients. I think during our Ae. Calls, probably sometime later this week we can do a bit of a call, or I can schedule time to just talk about how we want to do testing across all clients. Starting with testing on the dim and fact tables, basically looking at nulls.
281 00:52:29.273 ⇒ 00:52:31.540 Uttam Kumaran: Counts things like that. So
282 00:52:31.670 ⇒ 00:52:36.019 Uttam Kumaran: I’m gonna put that here, although this will be something that will cascade across all clients.
283 00:52:36.508 ⇒ 00:52:43.310 Uttam Kumaran: And then I’m gonna move this cleaning up. Gcp, stuff like that. I think this is somewhere like here.
284 00:52:43.590 ⇒ 00:52:44.350 Uttam Kumaran: like
285 00:52:44.630 ⇒ 00:52:50.270 Uttam Kumaran: we’ll keep. We’ll keep building as we go, and we’ll buy us some more time to take on some of these tech debt type tasks.
286 00:52:52.100 ⇒ 00:53:11.603 Uttam Kumaran: monthly cogs reconciliation. Yeah, this is something that we’re doing across all of our Ecom clients where they cogs is like the most manual, but yet like a huge component into the profitability equation. We’re seeing this with pool parts right now where they haven’t up. They don’t have a process for updating cogs across all of their
287 00:53:12.020 ⇒ 00:53:34.210 Uttam Kumaran: like storefronts. And basically like they don’t. They may be like 10 or to 30% more profitable or less profitable as those change. And there’s no because those are all inputs from like a factory or from like somebody who just says or from like a Po, there’s it that you don’t really get it. It doesn’t get updated sometimes. Because also.
288 00:53:34.710 ⇒ 00:53:41.030 Uttam Kumaran: if cogs is just a number meaning they’ve already paid for it. So there’s no one in the company that really cares about
289 00:53:41.310 ⇒ 00:54:08.769 Uttam Kumaran: owning that except for the executives who want to look at the profitability equation and us. And so I think probably something we need to do with this team, and we’ll do this with job. We’ll do. Probably every Ecom is have like a monthly cogs reconciliation process, unless they’re very well defined on how cogs and the cog values get permeated through. To give you example of what we’re doing now. We just have a spreadsheet with cogs for every product. And
290 00:54:09.118 ⇒ 00:54:15.080 Uttam Kumaran: we updated it once, and there’s no process to update it moving forward. So I guess, Robert, I’ll let you tell me like what the
291 00:54:15.300 ⇒ 00:54:21.129 Uttam Kumaran: feasibility, or like our prioritization of this is. But I do think that that’s something that could be valuable, that we’re not doing.
292 00:54:22.200 ⇒ 00:54:25.049 Robert Tseng: Yeah, I mean, I would say, definitely.
293 00:54:25.910 ⇒ 00:54:30.194 Uttam Kumaran: It’s not. I wouldn’t say it’s the highest priority, but it’s it’s important, I think.
294 00:54:30.740 ⇒ 00:54:37.685 Robert Tseng: But I feel like we’re feasible. We’re we’re there. We have all the cog modeling by product now. So we we should be able to do this.
295 00:54:38.390 ⇒ 00:54:41.510 Robert Tseng: yeah, like, I think I kind of have a
296 00:54:41.660 ⇒ 00:54:56.549 Robert Tseng: have a a strategy piece there where it talks about payback period, minus cogs and variable costs. Payback period will end up. That’s how you turn like a cogs metric into something marketing cares about now because they’re.
297 00:54:56.550 ⇒ 00:54:57.270 Uttam Kumaran: Yeah.
298 00:54:57.270 ⇒ 00:55:02.139 Robert Tseng: Yeah. So like, if if I can package those together, then this will be prioritized for sure.
299 00:55:02.660 ⇒ 00:55:07.330 Uttam Kumaran: Okay, great. So these 2 items are really go hand in hand. I think
300 00:55:07.560 ⇒ 00:55:10.010 Uttam Kumaran: I’m gonna basically try like.
301 00:55:10.220 ⇒ 00:55:19.479 Uttam Kumaran: we’re we’re doing cogs now across 3 clients like actively. So the Javi cogs process is probably the worst
302 00:55:20.384 ⇒ 00:55:32.340 Uttam Kumaran: and we’ve really modeled it in like a sucky way, because there’s like 5 different spreadsheets. I just did what we had to do. But like, it’s really horrible. So I need to figure that out and then
303 00:55:32.650 ⇒ 00:55:43.709 Uttam Kumaran: figure out like a scalable process for us to identify who the source of truth is for cogs, and then basically use this vehicle to then make sure that this is, data is right? That’s perfect.
304 00:55:44.250 ⇒ 00:56:03.319 Uttam Kumaran: I think that I, this is actually really great, that we have these pairs. And actually, that’s sort of how we wanna do. All this is where we have an overlying strategy. It then cascades into dashboards, data, model work. And I think for the most part, we do have like strategies around all these. So that’s really helpful. Like, nothing should be a priority where we’re just
305 00:56:03.860 ⇒ 00:56:11.260 Uttam Kumaran: okay. Let’s just take this on right? So let’s see, let’s move these in while we have another 10 min. So then we can talk about
306 00:56:11.380 ⇒ 00:56:15.839 Uttam Kumaran: like can talk about what what we’re doing today. So
307 00:56:16.893 ⇒ 00:56:21.489 Uttam Kumaran: reporting accuracy. Yeah, I’m gonna move this just so that literally the
308 00:56:21.920 ⇒ 00:56:29.710 Uttam Kumaran: the top right here, I really look at this alongside testing.
309 00:56:30.130 ⇒ 00:56:35.249 Uttam Kumaran: I think I’m gonna think I’m gonna I’m gonna move this and testing sort of like side by side.
310 00:56:36.440 ⇒ 00:56:42.350 Uttam Kumaran: So that’s really great. That’s a directive there, growth accounting active. Yeah, can you talk about
311 00:56:43.460 ⇒ 00:56:56.210 Robert Tseng: Yeah, so this is like a view. They’re not looking at their customers like this right now. But I think for a subscription. Ecom business, like, we need to kind of view it as like we would do in Sas, like, probably Sahana would know this. But like.
312 00:56:56.380 ⇒ 00:56:59.060 Robert Tseng: yeah, like I, you know, that’s where.
313 00:56:59.060 ⇒ 00:57:00.950 Uttam Kumaran: Inactive. Yeah, exactly.
314 00:57:00.950 ⇒ 00:57:06.590 Robert Tseng: So we need to. We need to get this kind of view of users or customers that we don’t have right now.
315 00:57:07.010 ⇒ 00:57:09.880 Uttam Kumaran: This is perfect. Yeah. Sorry. Go ahead, Sauna.
316 00:57:09.880 ⇒ 00:57:13.100 Sahana Asokan: You mind just explaining that again? I’m just trying to think about it.
317 00:57:13.100 ⇒ 00:57:20.769 Uttam Kumaran: Yeah, maybe I maybe I can take a I can take a pass at it. So we did this. You know, this is a huge part of our business at flow code, where you basically have a customer sign up
318 00:57:21.345 ⇒ 00:57:25.139 Uttam Kumaran: but they haven’t. They maybe use the product. But then they’re
319 00:57:25.280 ⇒ 00:57:34.439 Uttam Kumaran: they churned right and churn can be. They went from paying to non paying, but like they maybe still have some interaction. So you sort of have categories of customers.
320 00:57:34.440 ⇒ 00:57:34.899 Sahana Asokan: There we go!
321 00:57:34.900 ⇒ 00:57:36.720 Uttam Kumaran: You have people who are
322 00:57:36.950 ⇒ 00:57:45.569 Uttam Kumaran: active, you have people that are that return, but then they’re coming back. You then have people that are like inactive. And then people that are like
323 00:57:45.680 ⇒ 00:57:53.260 Uttam Kumaran: about to turn or or something like that. So we basically creating these sorts of segmentation. I’m not sure exactly what the right
324 00:57:53.690 ⇒ 00:57:55.820 Uttam Kumaran: categories are for for Eden, but
325 00:57:55.990 ⇒ 00:58:00.280 Uttam Kumaran: at least half or or like 3 out of 4 of these, I think, makes sense.
326 00:58:01.290 ⇒ 00:58:09.540 Sahana Asokan: Yeah, I think it would probably be like active, inactive ghost and then churned. I think those are kind of like the 4 main ones, but we can talk about it later.
327 00:58:10.610 ⇒ 00:58:15.709 Uttam Kumaran: Okay, yeah. And basically, one thing that it was really powerful
328 00:58:15.820 ⇒ 00:58:45.769 Uttam Kumaran: at flow code is, I basically showed that in a given month. Here’s how here it’s yours, like almost like you could do like a Sankey diagram of like. Here’s everyone that that ends up churning. Here’s everyone from the previously active that ends up going right? So I don’t. Every month you can categorize everyone into these categories. And then you sort of know from those categories like, it’s not just helpful about new customers, how they churn. But do, how do existing customers churn? Right? So you get to see these these changes. So I’m gonna lump that into everything around customer.
329 00:58:47.100 ⇒ 00:58:51.029 Uttam Kumaran: Let’s see, we also have 2 more, 3 more here. So
330 00:58:51.884 ⇒ 00:58:54.059 Uttam Kumaran: intake funnel form and checkout
331 00:58:54.210 ⇒ 00:58:58.320 Uttam Kumaran: abandoned. Yeah, I think these are both really good. I consider these.
332 00:58:58.520 ⇒ 00:59:04.650 Uttam Kumaran: Yeah, I kind of think about these as like both funnel related optimizations.
333 00:59:05.124 ⇒ 00:59:09.560 Uttam Kumaran: I’m gonna just take these and move these here to.
334 00:59:09.870 ⇒ 00:59:13.078 Uttam Kumaran: I think really the biggest thing here is to think about
335 00:59:13.740 ⇒ 00:59:15.449 Uttam Kumaran: I don’t know how feasible it is.
336 00:59:15.450 ⇒ 00:59:24.448 Robert Tseng: It’s not feasible yet until until the intake, until they launch the intake custom intake forms but once it once this launch, I’m sure it’ll be high priority.
337 00:59:25.150 ⇒ 00:59:27.199 Uttam Kumaran: Okay. Great. So I’m gonna leave that
338 00:59:27.760 ⇒ 00:59:29.509 Uttam Kumaran: I’m just gonna leave that in the middle. Then.
339 00:59:30.319 ⇒ 00:59:30.710 Robert Tseng: Yeah.
340 00:59:30.820 ⇒ 00:59:38.539 Uttam Kumaran: So that’s great. I sort of see a lot of that related to the customer journey. And then the last piece on yeah, experimentation.
341 00:59:38.700 ⇒ 00:59:41.290 Uttam Kumaran: Kpis, can you talk about this? Because I,
342 00:59:41.540 ⇒ 00:59:43.519 Uttam Kumaran: yeah, this is actually really interesting.
343 00:59:44.070 ⇒ 01:00:00.690 Robert Tseng: Yeah. So I mean, I would kind of lump it in with the intake stuff as well. But it’s like, okay, once we actually have the ability to like measure, like different landing pages and intake forms. And do this, do you actually do like Cro work? Yeah, we kind of need to like you.
344 01:00:01.200 ⇒ 01:00:13.730 Robert Tseng: There, there, we need to be able to measure that team or functions performance, because if they’re just like adjusting one word every like every day, and just like launching, launching a million tests, and, like
345 01:00:13.740 ⇒ 01:00:32.350 Robert Tseng: most of them, don’t do anything. Then that shows something about the efficiency of our experimentation. So what I’ve seen done is like this is kind of a way to hold the Cro team accountable to like really designing tests. That matter. You don’t want them to just be testing stuff all the time and everything they do. It’s like doesn’t really have an impact, you know. So.
346 01:00:33.250 ⇒ 01:00:40.209 Uttam Kumaran: This is great. I honestly think. Basically, one of the things we did. A flow code is like we always had a a test running on something.
347 01:00:40.390 ⇒ 01:01:02.349 Uttam Kumaran: and I do think that this could be a really good opportunity for the monthly, for the weekly call to be like, what tests are we running right? Every company wants to run pricing tents, discount tests, copy tests. That’s only in marketing, right? You don’t. You want to do like product differences. There’s I guarantee you. Everybody in the company is running a test. But of course they’re just like flipping something and being like.
348 01:01:02.640 ⇒ 01:01:10.289 Uttam Kumaran: And then there’s 10 variables right? There’s no test design. So I think that there’s definitely opportunity here across all of our clients to sort of
349 01:01:10.500 ⇒ 01:01:14.820 Uttam Kumaran: put them into a what do they call like test driven something, you know, or the other.
350 01:01:16.650 ⇒ 01:01:17.630 Robert Tseng: Exactly.
351 01:01:18.640 ⇒ 01:01:37.600 Uttam Kumaran: Cool. Okay, great. How do we feel? I mean, I think 1 1 sort of feedback for myself on this is, I think I’m gonna sort of kind of create little quadrants even within these. That’s probably. Besides the point. How do we feel about this? Is there any? Does anyone disagree with any of like the sort of placements of stuff here before I want to take the last
352 01:01:37.840 ⇒ 01:01:41.530 Uttam Kumaran: 6 or 7 min and talk about today and tomorrow
353 01:01:41.840 ⇒ 01:01:49.719 Uttam Kumaran: and I will. What I’m gonna do is sort of sorry. I’m just gonna talk over myself. I’m gonna move. Oh, move these to this down here.
354 01:01:50.298 ⇒ 01:02:03.860 Uttam Kumaran: We’re basically, we’re gonna get a very clear view of how these cascade into engineering timelines, and we will most likely just run the next week or 2 off this. I’m sort of like
355 01:02:04.030 ⇒ 01:02:17.929 Uttam Kumaran: I don’t. I don’t. Wanna kinda I I don’t care much about notion and sort of like how this gets organized. I. We have, like Eden. We have, you know, quite a bit of stuff like anywhere from 5 to 10 active priorities.
356 01:02:18.060 ⇒ 01:02:26.489 Uttam Kumaran: I don’t, but I don’t think we need like a hundred notion tickets to make that happen. I think we’re all smart and capable of just like taking on a task and and closing it all out.
357 01:02:26.772 ⇒ 01:02:50.780 Uttam Kumaran: I also want to blur the lines a little bit, meaning like, just because you’re on analysis doesn’t mean you can. Also, you can’t also assist a little bit on modeling where we need it. Same thing for the modelers. I actually expect if there’s dashboard tasks that you know, the analyst team is sort of swamped on. We can go take some of those. So I’ll be translating that here. But yeah, I guess I’ll pause any questions about this this process any of the stuff
358 01:02:51.320 ⇒ 01:02:52.960 Uttam Kumaran: we got it organized here.
359 01:02:53.280 ⇒ 01:03:23.199 Sahana Asokan: Are we gonna talk so this makes sense to me. And I I think I know you had mentioned you’re gonna like, look at this and see how this aligns with engineering timeline. So I guess I’m just curious, right, like just using my personal example. This week, I’m trying to create 2 new mock ups for the the 2 other dashboards, pharmacy and customer experience. So will you let me know this week? If the requirements like, when the requirements will be met for the other 2 mock ups. Is that how it’s gonna work.
360 01:03:23.830 ⇒ 01:03:38.840 Uttam Kumaran: Yeah, so let’s talk about. Let’s just take this example. And actually, I think this is really great. Because I just, we sort of talked high level about all the core categories. I want to talk about actual work like in flight right now. So for Sahana, for you, there’s there are both the
361 01:03:39.010 ⇒ 01:03:40.885 Uttam Kumaran: reviews of the
362 01:03:42.340 ⇒ 01:03:53.190 Uttam Kumaran: the farm Ops, like figma, right? The Cx turn analysis. So this is like under review right now. And basically we, the team is to make sure you have the data for these. Is that correct?
363 01:03:53.390 ⇒ 01:03:54.600 Sahana Asokan: Yes, yes.
364 01:03:55.120 ⇒ 01:03:57.850 Uttam Kumaran: Okay, so yeah, go ahead.
365 01:03:58.740 ⇒ 01:04:27.700 Sahana Asokan: Yeah, so that’s correct. And then, I know I had sent you guys that requirements dashboard for all customer experience, pharmacy, pharmacy, farm ops dashboards. So like, for example, the 3rd dashboard is operational efficiency. That’s also dependent on Bask and Zendesk, which is like a different set of metrics. So it’s like, I think it is like, I’m not saying, like the mockups are the source of truth. I actually think the requirements document is because
366 01:04:27.700 ⇒ 01:04:34.720 Sahana Asokan: that’ll give you the holistic picture of, like, how we’re using Zendesk, Bask, Shippo.
367 01:04:34.740 ⇒ 01:04:53.060 Sahana Asokan: and everything across all dashboards. Does that make sense? Because what I don’t want is that you guys are only looking at 2 mockups. And then we have this 3rd 3, rd like 3 and 4 dashboards coming out like next week. But then the data isn’t good enough like it doesn’t meet the requirements for those. Does that make sense like I feel like that’s in a
368 01:04:53.170 ⇒ 01:04:54.050 Sahana Asokan: or end.
369 01:04:54.360 ⇒ 01:04:58.729 Uttam Kumaran: No, that makes sense. Yeah. So we need the requirements. And what are the 2 called that are coming up.
370 01:04:59.736 ⇒ 01:05:09.149 Sahana Asokan: The 2 that are coming up are agent performance. So that’s just like Zendesk, and then customer journey. So that was more understanding, like the progression of orders.
371 01:05:09.390 ⇒ 01:05:10.890 Sahana Asokan: and all that fun stuff.
372 01:05:11.210 ⇒ 01:05:22.779 Uttam Kumaran: Okay. So I’m gonna make sure. I’ll just make sure that these 2 get reviewed and sort of, we move this into our new ae requirements flow. And then, yeah, we’re I’ll be looking at. I’ll be looking at these 2.
373 01:05:24.270 ⇒ 01:05:29.999 Uttam Kumaran: as well. So yeah, we’ll consider all this and sort of our data, modeling for our Zendesk and our customer, Mart.
374 01:05:30.490 ⇒ 01:05:35.260 Sahana Asokan: Cool. Yeah, sounds good. I just send you the link in the chat, too, if you just want to save it.
375 01:05:35.680 ⇒ 01:05:36.470 Uttam Kumaran: Okay, cool.
376 01:05:42.270 ⇒ 01:05:43.090 Uttam Kumaran: Okay.
377 01:05:43.280 ⇒ 01:05:44.846 Uttam Kumaran: What else is on our
378 01:05:45.850 ⇒ 01:05:54.969 Uttam Kumaran: our plates for for this week, I guess, for Beau and for Robert. What’s what’s today for Ltv. Like anything. Tell me what’s going on there for today.
379 01:05:55.700 ⇒ 01:06:09.839 Robert Tseng: Yeah. So the thing that needs to go out today is kind of the revision on the ad spend. And Ltv dash. So Bo knows that, like the Ltv chart, looks weird because of the way we stack the bars. So we need to figure out how to
380 01:06:10.330 ⇒ 01:06:16.469 Robert Tseng: to not view it that way because it’s misleading. It makes it look like Ltv. Fluctuates every month, which is not true.
381 01:06:17.222 ⇒ 01:06:34.179 Robert Tseng: And then I think we need to. We need to add a couple of tiles. Just to give them, not like a monthly change of Ltv. But like an Ltv target or number for the marketing team to use Bo. And I can talk about the assumption offline. But
382 01:06:35.463 ⇒ 01:06:37.830 Robert Tseng: yeah, so and then.
383 01:06:38.180 ⇒ 01:06:48.119 Robert Tseng: so we’re we’re gonna deploy that to tableau cloud, I guess, on the new paid account. So I’m like working through that right now. And
384 01:06:49.610 ⇒ 01:06:53.829 Robert Tseng: yeah, I’m trying to do it through like brain forge, so that we?
385 01:06:53.830 ⇒ 01:06:54.760 Robert Tseng: Yes, get
386 01:06:55.250 ⇒ 01:07:02.689 Robert Tseng: get partner status with tableau? Because but yeah. So that’s why there was a bit of a delay there. But I’m working through it.
387 01:07:03.200 ⇒ 01:07:08.170 Uttam Kumaran: If I can help on anything on sort of accounts or anything there, let me know.
388 01:07:08.900 ⇒ 01:07:09.550 Robert Tseng: Okay.
389 01:07:10.869 ⇒ 01:07:17.430 Uttam Kumaran: Cool. So on the is there anything else on the analysis front
390 01:07:17.920 ⇒ 01:07:20.439 Uttam Kumaran: that like is due in the next 48 h.
391 01:07:21.200 ⇒ 01:07:30.189 Robert Tseng: Yeah, actually, there’s a mixed panel thing that I’ve been doing. I didn’t ask Hana to do it because I wasn’t sure if she had the capacity for it. But
392 01:07:30.380 ⇒ 01:07:39.849 Robert Tseng: basically there’s like a web analytics dashboard that I’ve been building in mixed panel. And there’s like another revision that I need to set them today.
393 01:07:43.750 ⇒ 01:07:46.799 Uttam Kumaran: I think if you’re able to record this.
394 01:07:46.910 ⇒ 01:07:51.349 Uttam Kumaran: then yeah, we I can start to add it to the analysis roadmap.
395 01:07:52.666 ⇒ 01:07:57.700 Robert Tseng: Right now, I’m basically Sahana. I’m gonna try to unblock you as much as possible on these.
396 01:07:58.283 ⇒ 01:08:09.639 Uttam Kumaran: And then basically get a sense from you. I probably need another day or so to sort of get everything into here, and then I’ll get timelines from everybody. And then that way we can see if we can hand this off to you as well.
397 01:08:09.920 ⇒ 01:08:27.439 Sahana Asokan: Yeah sounds good. Cause it’s like, even if it’s like, for example, like the agent, one like that’s end desk, like, even if it’s some. If it’s like a rolling timeline. That’s fine. My only concern is I don’t want the data like I’m just concerned that the data is all gonna be ready at the same time, and then I’m gonna have 4 dashboards to build out.
398 01:08:27.800 ⇒ 01:08:33.579 Uttam Kumaran: No, I’m also yeah. I’m also like, basically preparing for that, too. So ideally, I mean.
399 01:08:34.000 ⇒ 01:08:41.019 Uttam Kumaran: I think that that’s probably going to be the case, so you probably won’t be able to hand this off to you. I also think we we may get you this like
400 01:08:41.760 ⇒ 01:08:45.300 Uttam Kumaran: the number one goal for the I guess I can talk about, for the
401 01:08:46.590 ⇒ 01:08:52.379 Uttam Kumaran: Ae team is, one is gonna be getting out our like customer service mart.
402 01:08:52.819 ⇒ 01:08:53.509 Sahana Asokan: Hold on!
403 01:08:55.979 ⇒ 01:09:03.999 Uttam Kumaran: and then we also are Comp, I want us to complete. We do have a sales. Mart. Oh, does that include like that is dim customers.
404 01:09:04.200 ⇒ 01:09:06.549 Uttam Kumaran: but I do kind of want to create like a
405 01:09:06.760 ⇒ 01:09:12.260 Uttam Kumaran: I kind of want to start to beef up towards like a customer. 360 related table.
406 01:09:14.149 ⇒ 01:09:30.620 Uttam Kumaran: maybe maybe me and you can work on like what the requirements are for that. Do you have any ideas on that? Because it looks like. For the most part we want to see for every customer almost all of our touch points with them. Where they came from. What have they bought? How many times do they interact with
407 01:09:30.899 ⇒ 01:09:32.760 Uttam Kumaran: the customer experience team?
408 01:09:34.830 ⇒ 01:09:36.050 Uttam Kumaran: So yeah, go ahead.
409 01:09:37.270 ⇒ 01:09:39.789 Awaish Kumar: Right now, I think it has things like
410 01:09:40.586 ⇒ 01:09:42.870 Awaish Kumar: like where they came from.
411 01:09:43.140 ⇒ 01:09:46.420 Awaish Kumar: the personal information, like contact numbers.
412 01:09:46.729 ⇒ 01:09:49.020 Awaish Kumar: maybe date of birth. Things like that.
413 01:09:49.260 ⇒ 01:09:50.399 Awaish Kumar: Okay?
414 01:09:50.899 ⇒ 01:09:57.690 Awaish Kumar: But kind of like the when they ordered 1st time. Yeah.
415 01:09:58.010 ⇒ 01:10:02.009 Awaish Kumar: But yeah, we don’t have customer support related data right now.
416 01:10:02.160 ⇒ 01:10:04.590 Awaish Kumar: But we can include that in also.
417 01:10:06.970 ⇒ 01:10:11.160 Uttam Kumaran: Okay, cool.
418 01:10:12.900 ⇒ 01:10:25.629 Uttam Kumaran: let’s just let me just do a quick look through about. So I think, Bo, you have priorities for today. So on a Us. Sort of stuff. I think a wish has stuff, I think, between Kyle, between me, you and a wish.
419 01:10:25.760 ⇒ 01:10:27.939 Uttam Kumaran: I think we could probably start.
420 01:10:28.040 ⇒ 01:10:31.980 Uttam Kumaran: I really want to work on. This
421 01:10:32.760 ⇒ 01:10:38.149 Uttam Kumaran: like this is still like, very sorry. I’m just like
422 01:10:38.400 ⇒ 01:11:00.257 Uttam Kumaran: moving this around. This is still very much like a little bit of a mystery to me. And I know, Kyle, the 1st thing you’ll suggest is like, let’s make this super super clear. So one of the things I think the 3 of us can work on this week while away takes. This is a very clear visual example of
423 01:11:00.920 ⇒ 01:11:05.080 Uttam Kumaran: of like how the customer journey works.
424 01:11:05.915 ⇒ 01:11:07.749 Uttam Kumaran: That would be really, really great.
425 01:11:09.380 ⇒ 01:11:28.019 Caio Velasco: Yeah. Oh, I agree. And I haven’t built anything related to like a source to target mapping kind of thing. But I think that’s probably what we want at the end of the day, like, even if we if we could go from, let’s say, front end front end actions that they have until everything gets to
426 01:11:28.160 ⇒ 01:11:32.429 Caio Velasco: the source tables. And how can we map those things to the metric? I think
427 01:11:32.680 ⇒ 01:11:35.290 Caio Velasco: this end to end would really be helpful.
428 01:11:36.120 ⇒ 01:11:39.170 Uttam Kumaran: Yeah, I think, this is a
429 01:11:39.900 ⇒ 01:12:08.459 Uttam Kumaran: cause. Cus, we basically have different scenarios. So I almost want to start with, like every sort of product purchasing journey they can go through, and then we’ll talk about also, like the end, the the churn journey. But this one, I think, as you start putting fresh eyes on this would be really, really helpful. I know you’re also working on stuff for Javi. But I kind of want. I. This is going to be a very similar thing we probably produce for them, although their stuff is like all one time subscription orders. These guys have a lot of touch points. So
430 01:12:08.730 ⇒ 01:12:22.718 Uttam Kumaran: I want this to live in figma, too. And basically in this big jam, we’ll we’ll work on this. So I think if I can hand these 2 to you ideally. Maybe me, you and Robert can spend 30 min like
431 01:12:23.600 ⇒ 01:12:28.460 Uttam Kumaran: I don’t know. Sometime in the next 2 days or an hour and sort of like at least give you all the
432 01:12:29.260 ⇒ 01:12:35.909 Uttam Kumaran: all the inputs on this. And then we can sort of work towards trying to have a version of this done by, you know. Wednesday, Thursday.
433 01:12:36.610 ⇒ 01:12:37.760 Caio Velasco: Perfect, alright.
434 01:12:38.650 ⇒ 01:12:39.600 Uttam Kumaran: Okay, cool.
435 01:12:41.120 ⇒ 01:12:54.550 Uttam Kumaran: And then I think, bo, I’ll probably have you just continue to iterate on these Ltv dashboards? I basically want to get to a point where those are finalized. And then we can sort of think about
436 01:12:55.107 ⇒ 01:12:58.509 Uttam Kumaran: basically trying to start to pick off items here.
437 01:13:00.030 ⇒ 01:13:03.580 Uttam Kumaran: Cause. I think Sahana will be basically mostly swamped with stuff here.
438 01:13:04.075 ⇒ 01:13:09.609 Uttam Kumaran: I want us to finish the marketing work and then start to take on big items
439 01:13:09.760 ⇒ 01:13:11.090 Uttam Kumaran: from this area.
440 01:13:12.420 ⇒ 01:13:14.219 Uttam Kumaran: So especially.
441 01:13:14.675 ⇒ 01:13:20.709 Uttam Kumaran: taking on the visual scenario and then some stuff on retention insurance. So we’ll work on that next.
442 01:13:21.100 ⇒ 01:13:21.830 Uttam Kumaran: Yeah.
443 01:13:21.830 ⇒ 01:13:22.710 Bo Yoon: Yeah, sure.
444 01:13:23.040 ⇒ 01:13:32.640 Uttam Kumaran: Cool any other questions. Anything else I can clarify.
445 01:13:37.750 ⇒ 01:13:38.540 Uttam Kumaran: Cool.
446 01:13:38.670 ⇒ 01:13:53.240 Uttam Kumaran: Okay. Good meeting. An hour seemed like just enough. If I can be helpful. Otherwise, please let me know. I think we’re gonna try to do a version of this every week. I do like this format again. I also want to run
447 01:13:53.470 ⇒ 01:13:57.441 Uttam Kumaran: our week from. Sort of this gives us a really clear sense of what’s going on.
448 01:13:57.800 ⇒ 01:14:05.220 Uttam Kumaran: Probably next week, also, we’ll run this, and then maybe we we don’t have like. We don’t do a full road mapping every week, and maybe it’s like once every 2 weeks or 3 weeks
449 01:14:05.564 ⇒ 01:14:15.009 Uttam Kumaran: but probably for the next one or 2 weeks we will run this for Eden as as just we start to get familiar. All of us on this call are just like, really, really know what the priorities are for them. So
450 01:14:15.200 ⇒ 01:14:18.449 Uttam Kumaran: thank you for. Thank you for being focused and spending the time.
451 01:14:18.660 ⇒ 01:14:24.610 Sahana Asokan: Yeah, then, this was really good. Actually, I think we were. Everyone’s aligned of what needs to be done. So I actually think this is good.
452 01:14:25.050 ⇒ 01:14:26.449 Uttam Kumaran: Cool, appreciate it.
453 01:14:28.190 ⇒ 01:14:29.230 Caio Velasco: Okay.
454 01:14:29.350 ⇒ 01:14:31.059 Uttam Kumaran: Thanks. Everyone. Talk to you soon.
455 01:14:31.060 ⇒ 01:14:32.519 Caio Velasco: Thank you. Thank you.
456 01:14:32.520 ⇒ 01:14:33.649 Uttam Kumaran: Yeah, thank, you.