Meeting Title: [Eden] Standup and Weekly Sprint Retro／Planning Date: 2025-03-29 Meeting participants: Aakash Tandel, Demilade Agboola, Robert Tseng, Josh, Nick G, Sahana Asokan, James Freire
WEBVTT
1 00:02:08.000 ⇒ 00:02:08.659 Aakash Tandel: There are!
2 00:02:09.100 ⇒ 00:02:10.080 Aakash Tandel: Hey, Akash!
3 00:02:17.830 ⇒ 00:02:19.110 James Freire: How’s it going
4 00:02:19.540 ⇒ 00:02:20.450 Aakash Tandel: Lou
5 00:02:20.620 ⇒ 00:02:21.260 James Freire: Hello!
6 00:02:22.270 ⇒ 00:02:23.179 James Freire: How’s it going? Robert?
7 00:02:24.090 ⇒ 00:02:25.119 Robert Tseng: Good! How are you?
8 00:02:25.290 ⇒ 00:02:26.310 James Freire: Doing well.
9 00:02:42.760 ⇒ 00:02:43.450 Aakash Tandel: Hey, Nick.
10 00:02:45.520 ⇒ 00:02:46.679 Nick G: Hey! Good morning!
11 00:02:47.370 ⇒ 00:02:48.640 Aakash Tandel: Good morning!
12 00:02:49.520 ⇒ 00:02:54.910 Nick G: I’ve got another meeting right after this. So I’m probably gonna jump out around 25 after
13 00:02:56.670 ⇒ 00:02:57.249 Robert Tseng: Right? Yeah.
14 00:02:59.650 ⇒ 00:03:07.549 Robert Tseng: I guess we haven’t asked. But anything that you’ve been working on that’s kind of relevant to any of the vast stuff that we’ve been talking about this week, Nick
15 00:03:08.070 ⇒ 00:03:20.639 Nick G: Unfortunately, nothing, nothing too related to y’all. I’m glad I was kind of looking forward to scraping shit for you. But you guys were able to wrangle Zack. So that’s definitely a better outcome.
16 00:03:22.880 ⇒ 00:03:34.779 Nick G: no. So yeah, this this week I’ve I’ve barely gotten anything done. I’ve just. I’ve just been pretty sick all week, but I’m I’m the past 2 days. I’ve finally been feeling better. So hopefully, some good news soon for you
17 00:03:35.380 ⇒ 00:03:36.010 Robert Tseng: Cool.
18 00:03:36.460 ⇒ 00:03:37.060 Aakash Tandel: Nice
19 00:03:40.670 ⇒ 00:03:42.879 Josh : What actually needs to get done. Nick.
20 00:03:43.490 ⇒ 00:03:45.720 Josh : like, what are you? What are you working on, man.
21 00:03:54.420 ⇒ 00:03:55.190 Josh : neck.
22 00:03:55.190 ⇒ 00:03:57.620 Nick G: Oh, sorry! Did you ask what I’m working on?
23 00:03:57.840 ⇒ 00:03:58.460 Josh : Yeah.
24 00:03:58.820 ⇒ 00:04:04.859 Nick G: Right now I’m working on some changes to the personalization intake
25 00:04:05.020 ⇒ 00:04:20.890 Nick G: with Ryan and all them. I’ve got a couple minor requests regarding the like Bmi question, which which we’ve been customizing. So that’s the 1st thing on my on my to do list today, just finishing up that question
26 00:04:21.230 ⇒ 00:04:21.829 Josh : Cool.
27 00:04:25.202 ⇒ 00:04:27.529 Robert Tseng: Think we get started. It’s probably just
28 00:04:27.530 ⇒ 00:04:33.750 Aakash Tandel: Yeah, alright. Let’s start with Demo Lade. Cause you’re here.
29 00:04:34.220 ⇒ 00:04:38.220 Aakash Tandel: And yeah, okay, yeah, what do you want to start with
30 00:04:40.023 ⇒ 00:04:54.940 Demilade Agboola: The high priority things on my table were the product mapping and just trying to figure out the way forward with that. So I’ve been able to update our sheets with the update from Basque
31 00:04:56.224 ⇒ 00:05:01.759 Demilade Agboola: and we should be getting that every Thursday by 9 am. Et
32 00:05:02.525 ⇒ 00:05:08.739 Demilade Agboola: I know, Zach, from Basque, requested, that we make that request every single week, but you know
33 00:05:08.740 ⇒ 00:05:13.179 Josh : I talked to him last night, guys, I talked to him. I was like dude. Come on, so he’s
34 00:05:13.865 ⇒ 00:05:14.550 Robert Tseng: Yeah.
35 00:05:14.550 ⇒ 00:05:20.319 Josh : Yeah, I was like, this is ridiculous dude. So I was like, at least like, write like a Cron job and just have it emailed or something.
36 00:05:20.820 ⇒ 00:05:29.160 Josh : You know. And so he he knows that he understands the use case like with Zack, like, before, you guys just like ask him for stuff
37 00:05:29.330 ⇒ 00:05:39.379 Josh : you have to like. Help him understand the why? Because, like he oftentimes will have a better solution for us like he likes to think that’ll come up with a better solution.
38 00:05:39.480 ⇒ 00:06:02.369 Josh : Right? So like right now, he, he hates doing exports like it’s like in his mantra that he hates doing exports for stuff, because he thinks that there’s a better way to do it. And so I’m like, Okay, whatever. So for the next, I don’t know. A week or so we might have to remind him a couple of times. But I basically told him, like Dude, if you don’t write a cron job for this, this is total waste of everybody’s time.
39 00:06:04.340 ⇒ 00:06:12.959 Robert Tseng: Yeah, I mean, Christiana spends like 5 to 10 HA month, just copy pasting, whatever it’s just like, I don’t understand what solution he has is better than this.
40 00:06:13.690 ⇒ 00:06:22.410 Nick G: Thank you for being on his case. Josh, I feel like coming from the top. He, like, you know, doesn’t have, doesn’t have too much room to squirm out, you know.
41 00:06:23.410 ⇒ 00:06:25.359 Demilade Agboola: Yeah, that’s that’s definitely helpful.
42 00:06:25.930 ⇒ 00:06:42.220 Josh : Yeah, no, I mean, that’s why I’m here, right? So I mean, and if it’s annoying to you guys like it’s annoying to me. So, you know, just bring it up to me, and I will go and get this shit fixed. But yeah, I was on the phone with them for like 2 h last night just talking about all this stuff. So
43 00:06:44.320 ⇒ 00:06:44.705 Robert Tseng: Okay.
44 00:06:45.090 ⇒ 00:06:49.166 Demilade Agboola: Yeah. So I’ll so we have a new sheet for that in our
45 00:06:49.720 ⇒ 00:06:58.540 Demilade Agboola: mapping sheets. And the new process is, Christiana will go in there and update it with the cogs.
46 00:06:59.571 ⇒ 00:07:06.180 Demilade Agboola: And just basically the numbers, the cogs, the service fee all that stuff.
47 00:07:08.300 ⇒ 00:07:22.770 Robert Tseng: Yeah. So she’s maintaining 4 fields, or whatever, as opposed to like 15 to 20 before. So I think that should be more doable for her, and we’re gonna tell her exactly which ones are missing because they’re not changing every week. It’s probably changing like once a month. Kind of thing
48 00:07:25.420 ⇒ 00:07:31.219 Demilade Agboola: Yeah. So we like cog shipping, fee, dispense, fee, and visit fee, whatever fee whatever the fees are.
49 00:07:31.380 ⇒ 00:07:36.230 Demilade Agboola: and whenever the changes occur she’ll obviously help us make those changes.
50 00:07:36.755 ⇒ 00:07:40.880 Demilade Agboola: And then we would use that in our product mapping
51 00:07:41.250 ⇒ 00:07:48.450 Demilade Agboola: in Dbt, and that would reflect in our dashboards. So that is the use case, and how things would run
52 00:07:48.640 ⇒ 00:07:49.290 Demilade Agboola: long term
53 00:07:49.290 ⇒ 00:07:59.649 Robert Tseng: Okay. Can you take it? One more step in a lot of and just record her. A loom just like, show her like where she needs to. I think she’s like a very visual person. So just writing her instructions doesn’t really like
54 00:07:59.780 ⇒ 00:08:08.039 Robert Tseng: do it so even if it’s just like you opening the tab and telling her these are the 4 columns you need to update. I think she’ll get the picture
55 00:08:08.650 ⇒ 00:08:10.870 Demilade Agboola: Gotcha yeah, I’ll put that
56 00:08:11.360 ⇒ 00:08:12.180 Robert Tseng: Thanks.
57 00:08:14.948 ⇒ 00:08:19.999 Demilade Agboola: Then the other thing that I’ve been handling is the ship or data
58 00:08:20.110 ⇒ 00:08:31.740 Demilade Agboola: effectively, I have been able to start testing the Api, but I’m trying to set up a large Api call for all the data for the last 6 months.
59 00:08:32.197 ⇒ 00:08:43.670 Demilade Agboola: But just even just set up that set up that and build the connection to segment so that our data hit segments and we have that available to then be routed to bigquery. So
60 00:08:44.213 ⇒ 00:08:48.579 Demilade Agboola: that’s kind of what I’ve been working on. It’s currently working
61 00:08:48.580 ⇒ 00:09:13.860 Robert Tseng: Guys let me just jump in there. So if if the payload is too big, I don’t. We don’t need the 6 months. We know that there is. There was a gap in early March of like 10 days, or they’re missing orders, I would say. Like, if we can get up to the 3 months back, filled, or whatever like I we can. Just we can keep rolling with that. We don’t. We don’t need to go patch like July 2024, or whatever like 6 months ago. Yet I think, yeah, just if that helps helps speed things up
62 00:09:14.260 ⇒ 00:09:25.260 Demilade Agboola: Alright. Yes, I’ll I’ll create the post request. In post mine. So I’m still also trying to set up the integration from postman into segment, so that the once we get
63 00:09:25.260 ⇒ 00:09:29.240 Robert Tseng: You’re using postman and not posting through segment. But then that’s not working
64 00:09:29.990 ⇒ 00:09:38.709 Demilade Agboola: So yeah, it’s it’s been tricky setting that up. And the way segment, because the current configuration with Shiple and segment is, it’s a live
65 00:09:39.130 ⇒ 00:09:56.240 Demilade Agboola: thing. It’s not a post request so like it’s just connected to the web hook, and it’s like listening. And in case getting the responses and storing them in. So this is like actually sending a post request, getting the response and then storing that response and sending it to the appropriate schema.
66 00:09:57.190 ⇒ 00:10:01.099 Demilade Agboola: So that yeah, that’s a like, a slightly different process.
67 00:10:01.740 ⇒ 00:10:03.579 Aakash Tandel: So the back fills the problem. Okay?
68 00:10:03.910 ⇒ 00:10:04.250 Demilade Agboola: Yeah.
69 00:10:04.250 ⇒ 00:10:08.280 Robert Tseng: Yeah, feel like the whatever. Okay? Sure?
70 00:10:13.930 ⇒ 00:10:17.089 Demilade Agboola: So that’s the to do for today
71 00:10:18.730 ⇒ 00:10:24.240 Aakash Tandel: Okay, okay, correct.
72 00:10:26.163 ⇒ 00:10:32.370 Aakash Tandel: Let me see, we’re still blocked on this guy. We have our product mapping
73 00:10:33.250 ⇒ 00:10:35.360 Robert Tseng: Yeah, no, that’s that’s clear. Yeah.
74 00:10:35.360 ⇒ 00:10:39.680 Demilade Agboola: Yeah, that’s the Zack from bask csv, that we’ve made headway on
75 00:10:41.480 ⇒ 00:10:43.930 Robert Tseng: I guess if you want to leave a comment.
76 00:10:44.240 ⇒ 00:10:51.799 Robert Tseng: I think just like the closeout was. When we have all the data we need from the vast thing we can programmatically build out like the different
77 00:10:52.450 ⇒ 00:11:01.530 Robert Tseng: product roles we want. So any membership naming product, subcategory, sub name whatever like, it’s all coming out of the variant name, which is what?
78 00:11:02.780 ⇒ 00:11:11.369 Robert Tseng: I know you don’t. You’re not familiar with this level of granularity. Akash. But it’s it’s yeah. I think that’s that. That was the that was the solve here
79 00:11:12.390 ⇒ 00:11:14.499 Aakash Tandel: Okay. And so this is no longer blocked. Then
80 00:11:14.760 ⇒ 00:11:16.210 Robert Tseng: Yeah. Good.
81 00:11:18.360 ⇒ 00:11:19.870 Josh : Is Rob on the call
82 00:11:20.910 ⇒ 00:11:23.100 Aakash Tandel: Rob is not on the call
83 00:11:23.490 ⇒ 00:11:26.580 Josh : Okay, can we make sure I’m gonna I’ll ping him real quick.
84 00:11:26.940 ⇒ 00:11:28.699 Josh : He should be on this call
85 00:11:31.730 ⇒ 00:11:36.480 Aakash Tandel: Let me make sure. I invited him
86 00:11:36.830 ⇒ 00:11:42.830 Demilade Agboola: I believe he wouldn’t be able to join this call today. He sent a message to defect
87 00:11:44.260 ⇒ 00:11:45.600 Aakash Tandel: Oh, did he? Okay.
88 00:11:45.600 ⇒ 00:11:50.190 Robert Tseng: Yeah, I mean, I don’t expect all this call every day, but it oh, yeah, again.
89 00:11:50.190 ⇒ 00:11:54.450 Josh : For now I do tell we get this shit into a good spot.
90 00:11:54.880 ⇒ 00:11:56.383 Josh : I actually do. So
91 00:11:56.930 ⇒ 00:12:03.141 Josh : I mean, I get it. If you can’t make it today. It’s fine, but I’m really trying to get this stuff into a really good spot.
92 00:12:04.660 ⇒ 00:12:07.650 Aakash Tandel: Yeah, I saw, he said. He’s out of office. But yeah, okay.
93 00:12:08.515 ⇒ 00:12:14.269 Aakash Tandel: alright, let me switch over to is the wishes
94 00:12:14.270 ⇒ 00:12:21.519 Josh : I was actually gonna give them give everyone kudos, because guess what? The 2 reports are. Only one sale off today.
95 00:12:21.820 ⇒ 00:12:23.679 Josh : It’s only one number off
96 00:12:26.730 ⇒ 00:12:38.999 Aakash Tandel: I know that this was an outcome from that issue. It’s assigned to away. She’s not here, but he’s looking into Ncaa market marketing model change. So he’s
97 00:12:39.320 ⇒ 00:12:42.700 Aakash Tandel: got this in progress. So once he
98 00:12:42.700 ⇒ 00:12:51.469 Robert Tseng: Yeah, we’re we already met yesterday to filter out what needs to be filtered out there. So it’s it’s not impacting that many orders. And yeah, we’re we’re clear. I’m reviewing the VR
99 00:12:51.910 ⇒ 00:12:52.450 Aakash Tandel: Great.
100 00:12:52.770 ⇒ 00:12:53.520 Aakash Tandel: Okay.
101 00:12:54.620 ⇒ 00:13:03.480 Aakash Tandel: okay. So let’s see, we can jump over to James. I thought Sahana was supposed to join. But I don’t see here yet. Let’s go, James.
102 00:13:07.140 ⇒ 00:13:16.149 Robert Tseng: Yeah. So I mean, I was looking at. I was trying to like, I guess. Qa, the retention dash work, I mean, I can. If I can just share my screen real quick.
103 00:13:16.300 ⇒ 00:13:16.680 Aakash Tandel: Sure.
104 00:13:16.680 ⇒ 00:13:21.300 Robert Tseng: Maybe with with Josh on here, you can kind of chime in on like what? What else he wants to see on that.
105 00:13:21.500 ⇒ 00:13:22.610 Robert Tseng: you. But
106 00:13:27.340 ⇒ 00:13:39.629 Robert Tseng: yeah. So I was just trying to read read through it from the top down. So we have the orders 1st by share of cohort. So like, how many like, what share of people are continuing to place orders afterwards?
107 00:13:39.770 ⇒ 00:13:46.800 Robert Tseng: I think we there’s a couple of cosmetic things, I think just kind of reordering this so that we’re we’re going most recent month down.
108 00:13:47.140 ⇒ 00:14:03.530 Robert Tseng: And then, yeah, I mean the size. I mean, I think I it’s like close. But I think it’s a little bit off. So I think this number is, I think we need to fix up a bit. I’m not seeing the 1st time versus repeat order. Kind of chunk chunk, unless.
109 00:14:03.990 ⇒ 00:14:11.879 Robert Tseng: yeah, is that a differentiation between like 1st time orders versus repeat, which is a very typical cut they want to see.
110 00:14:12.250 ⇒ 00:14:21.939 Robert Tseng: And then, if we could add a product filter in here, I think that would. Those are like the 2, the 2 more macro things that I think we should add to add to this
111 00:14:22.590 ⇒ 00:14:23.280 James Freire: Okay.
112 00:14:23.730 ⇒ 00:14:34.740 James Freire: the the bunching up is something with tableau, with the container like it does not let you have all those expanded and become scrollable I was trying to look into that last night.
113 00:14:35.240 ⇒ 00:14:40.360 Robert Tseng: Oh, yeah, I’m okay with this. I think it’s just rearranging
114 00:14:40.360 ⇒ 00:14:41.589 James Freire: Yeah, just change the address
115 00:14:42.063 ⇒ 00:14:52.389 Robert Tseng: Just flip the ascending order thing. And then, yeah, for anything. I’m just. I’m just caring about the this. The number the customer count here has to match up to what they’re expecting. So
116 00:14:54.590 ⇒ 00:14:55.190 James Freire: Got it
117 00:14:55.484 ⇒ 00:15:05.799 Robert Tseng: Okay, so what other access thing is just like, I don’t know the negatives here kind of weird. So I don’t know if we can remove that somehow, like we just shift the access by
118 00:15:07.370 ⇒ 00:15:10.509 Robert Tseng: yeah, I think because that doesn’t really make sense right to have
119 00:15:10.510 ⇒ 00:15:12.249 James Freire: Yeah. So when you filter on
120 00:15:12.250 ⇒ 00:15:17.635 josh: Super helpful through on this trial is like all the data is great.
121 00:15:19.250 ⇒ 00:15:21.299 josh: one of the things that would be like great
122 00:15:22.540 ⇒ 00:15:27.560 josh: beyond just this is to show, like, what is that single number
123 00:15:29.240 ⇒ 00:15:42.060 josh: for like retention, like value that we’re seeing like, I see that you see, like the Ltv. By cohort at the bottom. What if there’s like just like a single formula that we can get a green sound of like today’s Ltv.
124 00:15:42.410 ⇒ 00:15:45.200 josh: you know, blended by all cohorts
125 00:15:47.985 ⇒ 00:15:48.370 Robert Tseng: We.
126 00:15:48.370 ⇒ 00:15:48.980 josh: I gotta
127 00:15:48.980 ⇒ 00:15:57.650 Robert Tseng: We have that on the I could go back to the product OS dashboard oops.
128 00:16:01.860 ⇒ 00:16:03.210 Robert Tseng: So
129 00:16:06.500 ⇒ 00:16:12.200 Robert Tseng: yeah, this was like the when Adam was saying he wanted the
130 00:16:13.080 ⇒ 00:16:28.689 Robert Tseng: 3 month rolling. Ltv. With a 12 month or 10 month. Look back. Window like this is what you’re saying. Right like. That’s we have. We have what I think we have what you’re describing in this view, but it’s not in in James’s dashboard
131 00:16:29.930 ⇒ 00:16:31.850 josh: Got it. And like.
132 00:16:33.820 ⇒ 00:16:39.090 josh: okay, I can’t tell if it’s looking good or looking bad. She’s like there’s no timing.
133 00:16:39.825 ⇒ 00:16:40.210 josh: Okay.
134 00:16:40.210 ⇒ 00:16:47.539 Robert Tseng: Yeah, well, it’s always gonna look bad and more recent, because these customers haven’t really finished their like cycle yet. So
135 00:16:47.540 ⇒ 00:16:53.170 josh: Yeah. So like this number that doesn’t even like, okay. So it’s always gonna end up at 1.7 k.
136 00:16:54.270 ⇒ 00:16:58.030 Robert Tseng: Well as this moves. Yeah, I mean, if you could, I guess if anything
137 00:16:58.030 ⇒ 00:17:02.839 josh: It doesn’t. It doesn’t make sense. It doesn’t make sense. I don’t know why we need this.
138 00:17:04.680 ⇒ 00:17:09.980 josh: I don’t know. You guys think I’m crazy like, or is this just kind of a silly chart?
139 00:17:11.910 ⇒ 00:17:34.830 Robert Tseng: I think this view doesn’t make sense, but Adam wanted to see it over time and see if there were like Spike. I mean, what would make this make sense is if we had like a second line. That was the predicted Ltv, and you can see, like those 2 lines getting closer and it’s like, Okay, this is clearly gonna hit that target, or we’re way off here from where we think it should be at this point. So it’s like.
140 00:17:35.290 ⇒ 00:17:40.279 Robert Tseng: it’s it’s still like, at least you, you know, to me, when I read this, it’s like.
141 00:17:40.580 ⇒ 00:17:52.410 Robert Tseng: Okay, I mean, this spike is a little bit weird, but I would expect it to be consistently heading down like I’m not sure why, like the the customers that spent in February, they spent more a lot a lot more than
142 00:17:52.720 ⇒ 00:18:09.179 Robert Tseng: January. Maybe it has to do product launches or whatever. So that’s an interesting insight to me where it’s like, yeah, even though these are both early on customers, the February customers are spending more on average. So that’s I think there’s value in in seeing that. But I don’t know, I guess.
143 00:18:09.370 ⇒ 00:18:10.120 Robert Tseng: what.
144 00:18:10.250 ⇒ 00:18:12.209 Robert Tseng: What? What else do you think
145 00:18:13.040 ⇒ 00:18:17.840 josh: Yeah, so like, conceptually, when I think about ltv, right.
146 00:18:18.090 ⇒ 00:18:27.039 josh: I’m just trying to understand, like, what is the projected out to that cohort of people, and then are we hitting it like? Where are we at to plan?
147 00:18:27.190 ⇒ 00:18:28.409 josh: You know what I mean
148 00:18:28.530 ⇒ 00:18:56.030 josh: as opposed to just trying to look at where each one is, because, like these data points don’t, as a business like owner doesn’t really tell me a lot. I mean, that is an interesting one like you’re calling out like January. That makes me have some hypotheses that like, hey? Maybe customers that come in doing a weight loss. Therapy in January just aren’t gonna stick to it as long. So maybe like it looks good in that month for number of sales. But they’re not really good customers. You know what I mean. Like, they’re just probably
149 00:18:56.030 ⇒ 00:18:59.930 Robert Tseng: See all the New Year’s resolution. Folks that don’t stick with it. Yeah, maybe
150 00:19:00.080 ⇒ 00:19:12.809 josh: And that’s like kind of like where I can be like, okay, so that means that from a bi side, I need to go and activate way more on the email side to like, reactivate some of these people or try to cross sell them into something else because they’re like seasonal buyers.
151 00:19:13.910 ⇒ 00:19:15.750 josh: Yeah.
152 00:19:15.750 ⇒ 00:19:20.870 josh: I’m I’m trying to give you guys more context of like, how I’m gonna be using the data because, like
153 00:19:20.870 ⇒ 00:19:21.280 Robert Tseng: Yeah.
154 00:19:21.280 ⇒ 00:19:24.410 josh: You guys get put into a silo, and I don’t think that’s fair.
155 00:19:24.920 ⇒ 00:19:34.130 Robert Tseng: No, I get it. I think I like, I said. I think you need to see the predicted line for this view to make sense like, there’s limited stuff that you can really call out from this. But yeah.
156 00:19:34.630 ⇒ 00:19:36.720 Aakash Tandel: And the questions I think you ask are
157 00:19:36.880 ⇒ 00:19:45.120 Aakash Tandel: a lot more helpful for us to kind of work off than like a very tactical question because it kind of bubbles back up to actually the business level logic
158 00:19:46.390 ⇒ 00:19:56.920 josh: Yeah, exactly. Exactly. So like for me, like, this is, this is like somewhat helpful. But yeah, I mean, if there’s like a, we say that every single new cohort should be at a 1,700,
159 00:19:57.160 ⇒ 00:20:00.449 josh: you know TV. But I bet you, if you go even further to the left.
160 00:20:00.760 ⇒ 00:20:02.950 josh: like on that top graph here
161 00:20:03.080 ⇒ 00:20:10.859 josh: like the word says, 1.7 K, what month is that? Yeah, like, I bet you, if you go further to the left. It’s probably higher than 1.7 K.
162 00:20:15.210 ⇒ 00:20:19.350 Robert Tseng: No, we. We put a 12 month cap on this on this, so you can’t go back further.
163 00:20:20.610 ⇒ 00:20:30.009 josh: Yeah, cause we do have some customers that like, we’ve seen that are spending like 5 to 5. Like, I think there’s 19 customers we have to spend over $5,000 with us.
164 00:20:32.210 ⇒ 00:20:32.860 Robert Tseng: Okay.
165 00:20:33.650 ⇒ 00:20:38.410 Sahana Asokan: Would it be helpful to see it broken down by
166 00:20:38.860 ⇒ 00:20:45.190 Sahana Asokan: something like, for example, would it be helpful to see it broke, be broken down by product instead of filtering for it.
167 00:20:48.050 ⇒ 00:21:00.820 Sahana Asokan: The only reason, I say that is because I think if you had a view where you see it at the product level, then you would probably really understand what product is contributing to a pike or a a dip
168 00:21:02.461 ⇒ 00:21:08.969 josh: Sorry. I just got my wife. She just dropped off breakfast, so I just missed that last part
169 00:21:09.260 ⇒ 00:21:10.069 josh: when I say door
170 00:21:10.070 ⇒ 00:21:14.719 Robert Tseng: Yeah, just break whether you want to break it out by product. Because right now, right now, Mondays, yeah.
171 00:21:14.720 ⇒ 00:21:28.299 Sahana Asokan: Yeah, I think in the Ui, right, you have to essentially go in and filter for a specific product. So when you let’s just say, like you’re using this to diagnose. Let’s just say the example, Robert said. You want. We want to diagnose why that
172 00:21:28.380 ⇒ 00:21:57.670 Sahana Asokan: dip or that peak is happening within Feb, and we want to know, like what contributed to it right now, we we would essentially be having to work backwards like we would have to go click on every single product. Understand? Where we’re seeing the most fluctuations and then diagnose like that? I think what I’m proposing is, would you like to see an area chart being broken down by the product, so you don’t have to filter for it. So you would visibly see what product area
173 00:21:57.790 ⇒ 00:22:01.890 Sahana Asokan: is experiencing. The most fluctuation or volatility
174 00:22:02.300 ⇒ 00:22:08.870 josh: So I have a deep gut feeling about this. But I’m gonna I’m gonna try to help you on a little bit more about our business real quick.
175 00:22:09.140 ⇒ 00:22:14.750 josh: So let me ask you a question because you’re pretty close to the data. What is the variance
176 00:22:14.980 ⇒ 00:22:22.469 josh: of our number one selling product versus the number 2, selling like what is the total sales in terms of volume
177 00:22:23.390 ⇒ 00:22:24.926 Robert Tseng: And it’s it’s huge.
178 00:22:25.310 ⇒ 00:22:25.980 josh: Nice to meet you
179 00:22:25.980 ⇒ 00:22:26.550 Sahana Asokan: It is huge.
180 00:22:26.550 ⇒ 00:22:27.240 Robert Tseng: Yeah.
181 00:22:27.240 ⇒ 00:22:32.189 josh: So so does that today make a lot of sense? Probably not right
182 00:22:32.190 ⇒ 00:22:34.609 Robert Tseng: Yeah. It’s everything is driving everything.
183 00:22:34.610 ⇒ 00:22:37.169 josh: Demo is everything right now
184 00:22:37.170 ⇒ 00:22:38.890 Sahana Asokan: Have much impact. Okay.
185 00:22:39.480 ⇒ 00:22:40.110 Robert Tseng: Yeah.
186 00:22:42.180 ⇒ 00:22:50.219 Robert Tseng: yeah, I mean, so we are, we auto filter to Sema, because that’s the one that they’re looking at all the time. I mean, you do get the blended view, so we can tell
187 00:22:50.220 ⇒ 00:22:50.810 Sahana Asokan: Yeah, yeah, I mean.
188 00:22:53.980 ⇒ 00:22:59.779 Sahana Asokan: if it’s that significant, then the other ones, the small fluctuations wouldn’t impact it
189 00:23:00.060 ⇒ 00:23:02.569 Sahana Asokan: significantly. So it doesn’t even matter
190 00:23:03.140 ⇒ 00:23:10.670 josh: But I mean, it’s a good question. I don’t think it’s a bad question yet. I mean, I I just like again, like, I want you guys to understand like in my mind.
191 00:23:10.760 ⇒ 00:23:15.839 Sahana Asokan: Makes sense. Just I just wanna make sure I want to build this so you could use it. You know.
192 00:23:17.320 ⇒ 00:23:25.152 Robert Tseng: Okay. So I mean just to kind of close to the work going there. So on the retention side, yeah, James, maybe a couple of adjustments will make. But I mean, I think
193 00:23:25.540 ⇒ 00:23:43.139 Robert Tseng: I mean the the ask look for that was just to recreate the retention cohorts on the on the looker what wasn’t looker before. So I think we’re just missing the the 1st versus repeat, and then some cosmetic things. As far as like breaking out by product or having the timeline view we already have that. So I think we don’t need to rebuild that
194 00:23:44.320 ⇒ 00:23:44.950 josh: Yep.
195 00:23:45.180 ⇒ 00:23:45.770 James Freire: Okay.
196 00:23:46.430 ⇒ 00:23:47.890 Robert Tseng: Alright! Let’s keep going
197 00:23:48.750 ⇒ 00:23:49.340 Aakash Tandel: Great.
198 00:23:49.470 ⇒ 00:23:54.769 Aakash Tandel: And so Hannah is here. So that’s perfect, because we can move over to
199 00:23:55.579 ⇒ 00:23:59.570 Aakash Tandel: okay, actually, James, I think we cover everything right for yours. Looks like we did
200 00:24:00.050 ⇒ 00:24:00.660 Robert Tseng: Yeah.
201 00:24:01.110 ⇒ 00:24:01.650 Aakash Tandel: Cool.
202 00:24:03.770 ⇒ 00:24:07.409 Aakash Tandel: Let’s go to Sahana. I know you have some stuff in
203 00:24:07.570 ⇒ 00:24:12.660 Aakash Tandel: kind of pending client feedback. I know the this stuff that this is with Mattesh
204 00:24:13.407 ⇒ 00:24:20.379 Sahana Asokan: Yes, so waiting for Mattesh on the marketing dashboard that one, I believe. Robert shared with him for feedback
205 00:24:20.900 ⇒ 00:24:21.679 Robert Tseng: Yeah, I’ll pay them
206 00:24:22.320 ⇒ 00:24:23.130 Sahana Asokan: Sorry, what
207 00:24:23.806 ⇒ 00:24:25.739 Robert Tseng: Ping them right now, just to follow
208 00:24:25.740 ⇒ 00:24:29.650 josh: Oh, yeah, that’s fine. And then the second one for the sun right now.
209 00:24:30.060 ⇒ 00:24:35.200 josh: But he might be he might be able to take a peek at it. He’s been done
210 00:24:35.200 ⇒ 00:24:35.920 Robert Tseng: Okay.
211 00:24:37.385 ⇒ 00:24:50.450 Sahana Asokan: And then the second dashboard was the customer journey dashboard, the one we decide to prioritize for farm ups as well as a member experience, I was able to get that done as well. For the most part
212 00:24:50.450 ⇒ 00:25:08.230 Sahana Asokan: I did when I said in slack document, some of the nuances that we have to look into, especially on data quality. For csat scores is like the primary one. And then I’ve also included the nice to have. So including, like geographic level filtering.
213 00:25:08.540 ⇒ 00:25:13.960 Sahana Asokan: All of that fun stuff. So those are all nice to have. But the foundation of the dashboard is done
214 00:25:14.290 ⇒ 00:25:17.769 josh: And then so a lot of this stuff
215 00:25:18.130 ⇒ 00:25:24.199 josh: I I think Carlos has to take some family leave right now, and there might be some changes getting made
216 00:25:24.200 ⇒ 00:25:24.910 Sahana Asokan: Okay.
217 00:25:25.194 ⇒ 00:25:26.329 josh: Just a heads up
218 00:25:26.490 ⇒ 00:25:33.609 josh: so you can, you know I’ll I’ll kind of take over some of the stuff there and then, probably, so we’ll Rebecca
219 00:25:33.970 ⇒ 00:25:48.750 Sahana Asokan: Okay, yeah. So we’re still working with her on the farm side. So all of this has pharmacy level meta filtering. We just kind of created like one for both teams. So I’ll set up some time with you, maybe to just go over how it would support
220 00:25:48.750 ⇒ 00:25:52.669 josh: One for both teams. That’s fine. I’d probably just say like, Go through Rebecca, for now.
221 00:25:52.670 ⇒ 00:25:54.050 josh: Okay, sounds good.
222 00:25:54.050 ⇒ 00:25:55.879 josh: Help it all. That’s fine.
223 00:25:56.270 ⇒ 00:25:57.930 Sahana Asokan: Yeah, I think the farm
224 00:25:58.350 ⇒ 00:26:05.610 Sahana Asokan: the farm use case is different from the customer, like the member experience. So I don’t. We can wait on that. I guess
225 00:26:14.080 ⇒ 00:26:14.859 Aakash Tandel: Is this?
226 00:26:14.860 ⇒ 00:26:20.279 josh: And then, and then you said that the farm the farm one’s like pretty good. It’s they’re good to go.
227 00:26:20.800 ⇒ 00:26:21.440 josh: Yes.
228 00:26:21.440 ⇒ 00:26:29.029 josh: yesterday I had like a big. I had an issue that like it was a great use case for why? I really want those dashboards. Because
229 00:26:29.360 ⇒ 00:26:38.070 josh: here’s like, here’s a scenario that’s happening right now. So one of our pharmacy partners did not tell us that they’re having an issue sourcing the raw material. I think
230 00:26:38.070 ⇒ 00:26:38.670 Sahana Asokan: Okay.
231 00:26:38.670 ⇒ 00:26:40.699 josh: Shoot the bacteriostatic water.
232 00:26:41.080 ⇒ 00:27:06.479 josh: And now they’re coming to us when they’re 3 days delayed saying, Oh, hey! We’re not gonna be able to ship this stuff till Friday or Saturday. And so, because of that, we weren’t able to proactively get any communications at the customers. And so now our ticket numbers are just exploding, and so, being able to see that stuff proactively is going to be a massive win for us, because now our tickets are in like the 1,100 1,200 range
233 00:27:06.950 ⇒ 00:27:12.270 Sahana Asokan: Yes. So to answer your question, we’re gonna be able to see the orders
234 00:27:12.500 ⇒ 00:27:22.678 Sahana Asokan: that are associated with the high priority ticket as well as the orders that are not prior assigned to a high priority ticket, and the actual
235 00:27:23.190 ⇒ 00:27:39.639 Sahana Asokan: payment status States, as well as when the orders were actually placed dates at the granular level. So like, for example, right? If you’re interested in understanding, maybe like performance across all pharmacies to potentially understand like
236 00:27:39.640 ⇒ 00:27:56.200 Sahana Asokan: this situation that you’re asking for. You could essentially filter for some service level metrics or tickets by the specific pharmacy to understand which specific customers were affected and which products they were affected for
237 00:27:56.200 ⇒ 00:28:25.409 josh: And the most important thing out of this is that within the tableau I used to have this set up because I built my whole data infrastructure before in tableau, we set up like alerts. So something it’s like, so if there’s like a break or an sla breach, it’s emailing everybody, it’s like setting an alert. So like as you guys get these data points from everyone. And like they are establishing these sla’s and like these when they’re not in scope or not in the scale they are.
238 00:28:25.470 ⇒ 00:28:28.779 josh: My ask is for you guys to just automatically
239 00:28:28.780 ⇒ 00:28:29.550 Sahana Asokan: Wait, alarm.
240 00:28:29.550 ⇒ 00:28:30.770 josh: These alerts.
241 00:28:31.470 ⇒ 00:28:45.689 Sahana Asokan: Good to know. So we can definitely do that. I I think I would. I I’ll work with Rebecca on the sla’s to understand which ones are priorities for her. I think I I’m just curious on the ticket side. Would it be
242 00:28:46.260 ⇒ 00:28:57.189 Sahana Asokan: helpful or useful if the business, if we created alerts based on the number of high priority tickets that are being created? Or would that create too much noise
243 00:28:58.450 ⇒ 00:29:00.629 josh: Can you give me a little bit more context?
244 00:29:00.630 ⇒ 00:29:02.100 Sahana Asokan: Like, for example.
245 00:29:02.750 ⇒ 00:29:16.300 Sahana Asokan: if we’re basically seeing, you know, X amount of high priority tickets and Zendesk being created like for a specific given timeframe like, let’s just say a week like would, is that something you would want a ticket.
246 00:29:16.805 ⇒ 00:29:23.389 Sahana Asokan: an alert from right? And then maybe an alert. We could give you the the Zendesk agent information
247 00:29:23.740 ⇒ 00:29:26.459 josh: I would say, if there’s a way to make it
248 00:29:26.830 ⇒ 00:29:35.809 josh: so like it’s looking at, hey? Over the last week or 2 weeks the rolling average of tickets has spiked by a certain percentage like that
249 00:29:35.810 ⇒ 00:29:36.470 Sahana Asokan: Can do that
250 00:29:36.490 ⇒ 00:29:48.149 josh: Way to do it as opposed to just a raw number like there would have to be a little bit of logic put into like, Hey, you’re seeing a 20% increase in ticket creation. Something’s broken, basically
251 00:29:48.150 ⇒ 00:29:49.360 Sahana Asokan: Yeah, we can do that
252 00:29:50.760 ⇒ 00:30:12.399 Robert Tseng: So I mean one other thing, I’ll say so. This is like a more of a downstream kind of reaction. We’re using the tickets as a proxy, for you know, pharmacy operation. Sla’s but the the real fix is at the operational level. It’s when the pharmacies are missing their slas like that we need to quit be setting those alerts so
253 00:30:12.400 ⇒ 00:30:12.840 josh: Yeah.
254 00:30:12.840 ⇒ 00:30:37.769 Robert Tseng: I think we need to shift the paradigm a bit, and the and this is where the ship oh, and best kind of like order journey work is, I think, is a higher. I mean, we have the Zendesk data already, so maybe it’s easier for Sahana you to kind of build out what Josh is asking. But I view the the root cause of Sla for Sla performance reporting. It needs to not be from a from a ticket perspective it needs to be for
255 00:30:37.770 ⇒ 00:30:54.969 josh: I think I think there’s 2, though I do think that there is some things we’re not going to get from the pharmacy like we could have a raise in tickets because doctors are delayed. And right now I don’t have any kind of like sla stuff based on that, but we do, we would be able to see it from increased ticket volume.
256 00:30:55.340 ⇒ 00:30:58.019 Sahana Asokan: I think there’s also sorry. Go ahead.
257 00:30:58.020 ⇒ 00:31:10.549 josh: No, I was just gonna say, I do think that there’s both. And I I also agree with you, Robert, that, like I would prefer to have a huge like, you know, focus or emphasis on the sla breaches from the pharmacy, because that’s like a big early trigger warning for us
258 00:31:11.100 ⇒ 00:31:24.209 Sahana Asokan: Yeah, I think the sla breaches we definitely need to wait for the ship of ask especially when when it comes to thinking about the alerts. But I think from the ticket perspective when when it comes to like.
259 00:31:24.350 ⇒ 00:31:33.129 Sahana Asokan: are we seeing X percentage of an increase of high priority tickets for a set period of time like, that’s something we can just use zendesk for
260 00:31:33.350 ⇒ 00:31:40.609 Robert Tseng: Yeah, I mean, we’re we can. I feel like we could keep moving with that one. So that’s probably the easier one for us to go after. Now.
261 00:31:40.740 ⇒ 00:31:57.790 Robert Tseng: yeah. So okay, can we just be aligned on that priority then? So I mean, sounds like we have some additional things to add to the Zendesk side. But obviously, we’re a little bit. We’re yeah on the operate on the order on the order journey side. We’re we’re still.
262 00:31:57.950 ⇒ 00:32:03.760 Robert Tseng: I mean, we’re we’re we’re still kind of chugging along there so we can’t. We can’t really build on that side yet.
263 00:32:05.957 ⇒ 00:32:08.282 Demilade Agboola: I was gonna ask for the
264 00:32:09.040 ⇒ 00:32:14.679 Demilade Agboola: different pharmacies. Do they have different slas in terms of shipping, or is it just like one flat? Sla
265 00:32:16.300 ⇒ 00:32:20.380 josh: I think it’s just one flat, Sla. If it goes well, there’s there’s kind of 2 things.
266 00:32:20.850 ⇒ 00:32:21.760 josh: So
267 00:32:21.880 ⇒ 00:32:28.710 josh: if an order sits at a pharmacy longer than 3 business days. It’s a breach of the total Sla.
268 00:32:28.860 ⇒ 00:32:33.040 josh: If it sits at a pharmacy for 5 days, it’s a major issue
269 00:32:33.440 ⇒ 00:32:48.129 Sahana Asokan: So actually, we have all of those metrics in the order summary, because it we’re essentially able to understand at the hourly level and the day level. How long it takes for the order
270 00:32:48.800 ⇒ 00:33:00.889 Sahana Asokan: to be shipped after it was sent to the pharmacy. So we have all of those actually. So I don’t actually know if ship Obasc is a blocker. But just putting that out there, based on what I’ve seen.
271 00:33:02.790 ⇒ 00:33:13.414 Sahana Asokan: and we’re actually already including all of those communication metrics or like Sla type metrics. And they’re already being aggregated by pharmacy in the
272 00:33:13.970 ⇒ 00:33:15.749 Sahana Asokan: order. Journey dashboard.
273 00:33:17.941 ⇒ 00:33:31.530 Robert Tseng: I’ll review your order during dashboard. I haven’t see it. I don’t think I I mean, I think there are a couple of nuances that we’re like. I mean, we can talk about this offline. I just. I wouldn’t trust the
274 00:33:31.750 ⇒ 00:33:45.220 Robert Tseng: fast order statuses as like they’re not real time. They’re they’re just like web hooks that are sent randomly. So I mean, if anything, it’s like a lagging indicator of what’s going on. So I I like maybe 2 weeks back or something. But
275 00:33:45.850 ⇒ 00:33:49.669 Robert Tseng: yeah, I mean, if if- if that could still.
276 00:33:49.670 ⇒ 00:33:51.499 Robert Tseng: yeah, we’ll just chat about it later.
277 00:33:51.810 ⇒ 00:33:52.380 Robert Tseng: Yeah.
278 00:33:56.870 ⇒ 00:34:00.700 josh: I’m sorry. I don’t know if there’s a question. I just got a phone call. What’s up
279 00:34:00.900 ⇒ 00:34:03.290 Robert Tseng: No, no, there wasn’t a question. There. We’re we’re good.
280 00:34:03.450 ⇒ 00:34:06.569 Robert Tseng: Alright! Anything from. Yeah. Let’s just keep going
281 00:34:06.910 ⇒ 00:34:09.130 Aakash Tandel: Yeah anything else. Hannah.
282 00:34:10.198 ⇒ 00:34:18.560 Sahana Asokan: No, I think so. Those are kind of the 2 things I’m just kind of waiting on. So I think, for next week, just thinking about net new
283 00:34:19.340 ⇒ 00:34:20.580 Sahana Asokan: tasks.
284 00:34:21.889 ⇒ 00:34:23.479 Aakash Tandel: Yep, okay.
285 00:34:24.759 ⇒ 00:34:42.689 Aakash Tandel: I actually think that I think that’s it, for stand up then, because we’re away. She’s not on here. But he did message in the Channel for Demo. I think there’s a question on what table he’s looking at. So whenever you get a chance. Anyone else working on anything that needs to be highlighted here
286 00:34:46.070 ⇒ 00:34:56.050 josh: I mean with with the new tasks for next week. So I don’t know how you guys also approach this if it’s like just like a kanban, or like you guys are doing sprints or like what?
287 00:34:57.130 ⇒ 00:35:01.560 josh: But there’s a lot. I mean, there’s a never ending
288 00:35:01.790 ⇒ 00:35:06.969 josh: in a series of work. If you guys need it, I can definitely pass a lot of stuff on
289 00:35:08.030 ⇒ 00:35:10.450 Aakash Tandel: Yeah, we can. Well.
290 00:35:10.560 ⇒ 00:35:21.800 Aakash Tandel: I’ll do with Robert is. We’ll come up with a list of like this main stuff that we’ve got in the backlog, and then see if that’s appropriate for you, or you feel like we should reprioritize something else. We can get that over for you
291 00:35:21.800 ⇒ 00:35:28.740 josh: I’m happy to go do all the backlog grooming sessions and stuff, and if we want to let everyone else go, I mean, I have 12 min till my
292 00:35:28.740 ⇒ 00:35:44.049 Sahana Asokan: And sorry guys, just just so that you guys do go over it. Do you mind just talking about or prior, like having the conversation about the product analytics work only because I had that meeting with Joanna this week. So I just want more clarity on it
293 00:35:45.340 ⇒ 00:35:47.230 josh: What does that mean? Sorry.
294 00:35:47.230 ⇒ 00:35:49.099 Robert Tseng: Mixed panel segment stuff. Josh.
295 00:35:50.150 ⇒ 00:35:50.750 josh: Got it
296 00:35:50.750 ⇒ 00:35:53.160 Robert Tseng: For for Joanna’s product launch
297 00:35:54.360 ⇒ 00:35:55.360 josh: Got it.
298 00:35:57.220 ⇒ 00:36:16.749 josh: I am not a huge fan of doing any of that stuff until we have this core data. Really. Really. So like, here’s what here’s our actually flip it around. I would actually flip it to you to say, Hey, when do you think that I’ll be able to have enough trust in all these core data sets so that we can start doing that stuff
299 00:36:16.970 ⇒ 00:36:24.859 josh: like, what would give. What what can you guys do to give me the confidence like. That’s where I’d rather have you focused right now truthfully, as a business owner
300 00:36:25.460 ⇒ 00:36:26.070 Sahana Asokan: Okay.
301 00:36:27.750 ⇒ 00:36:32.837 josh: And I’ll take the heat from Joanna. Don’t worry, I’ll tell Joanna. Hey! It’s not it’s me
302 00:36:34.440 ⇒ 00:36:42.180 Sahana Asokan: Yeah, I think maybe something we could talk about is, maybe, yeah. You guys, why don’t you guys just chat and then let me know
303 00:36:43.800 ⇒ 00:36:51.790 Aakash Tandel: We’ll definitely reprioritize things. We’ve kind of paused a lot of the in flight work streams. So we’ll see which ones of those make the most
304 00:36:51.790 ⇒ 00:36:58.669 josh: Is it easier? Is it better? Do you prefer this like, you guys also give me feedback, too. So I can be better for you guys
305 00:37:00.560 ⇒ 00:37:01.430 Aakash Tandel: Yeah, I think.
306 00:37:01.430 ⇒ 00:37:03.840 Robert Tseng: One of these, yeah, go ahead. Yeah.
307 00:37:04.500 ⇒ 00:37:08.380 Aakash Tandel: Yeah, that’s what I was gonna ask. I was like, I think, are you talking about like, just stand ups or
308 00:37:08.380 ⇒ 00:37:12.490 josh: Yeah, just in general, like, like, this is the 1st week where I’m like trying to really
309 00:37:12.620 ⇒ 00:37:20.800 josh: dive in with you guys like, what do you guys need from me? So we can be even better like, I’m literally committing my time to this group like, I want to make this really good
310 00:37:20.800 ⇒ 00:37:21.500 Robert Tseng: That makes you great
311 00:37:21.500 ⇒ 00:37:38.960 Sahana Asokan: I think something that could help me is for maybe the dashboards for the dashboards we do have published right now. I know data quality is obviously like priority number one, I think priority number 2 would be to understand if the the layout is actually helpful to you.
312 00:37:39.560 ⇒ 00:37:43.209 josh: Got it for me. Everything I do.
313 00:37:43.490 ⇒ 00:37:47.550 josh: since I’m like a exact that’s always on the go. I travel a lot, is on my phone.
314 00:37:49.040 ⇒ 00:37:52.549 josh: And so, being able to think about everything mobile 1st stop.
315 00:37:52.670 ⇒ 00:38:13.290 josh: it sucks. It’s like way harder to do. But like that’s truthfully the use cases that make the most value, or add the most value for me, because, like me and Adam literally, I’m on the road like 26 weeks of the year, like I’m I’m traveling a lot, and so I don’t always have my laptop. And like I have, like a you guys should see my office. It probably looks like yours. I have 4 screens, like all
316 00:38:13.290 ⇒ 00:38:13.860 Sahana Asokan: Yeah.
317 00:38:13.860 ⇒ 00:38:22.209 josh: Like. So it’s a way easier for me to like use this stuff. But also I I, I also think that everything we built so far is like.
318 00:38:22.660 ⇒ 00:38:36.659 josh: good for like a core level one. But then it’s like, okay, as you guys are coming up with these concepts, I want you guys to try to get in my head of like thinking like, how am I going to probably use these dashboards like? What are the questions that I’m going to be asking, based on the results.
319 00:38:37.010 ⇒ 00:38:50.980 josh: So like, if I see that something is down like, Where is it going to cross? Where would I go next. So like for me, like when I look at like the sales and and like, you know, the updates from that, the next question I have is okay, do we have a down like, spend day on marketing?
320 00:38:51.130 ⇒ 00:39:18.000 josh: You see what I’m saying like, how do I start creating like this mothership approach where it’s like things are, boom, boom, boom, boom! And I can go from the next and like understanding, so that I know I can go talk to marketing. Say, Hey, why were you guys down in the spend yesterday? And then they’re gonna tell me something. And then I’m gonna see. Oh, because creatives were delayed. And then I’m going to go and look and see my creative okay, our dashboard. And the creatives were going to be like, oh, they’re delayed because of this this and this. See what I mean like, it’s always like a it’s like a rabbit hole that I
321 00:39:18.000 ⇒ 00:39:19.969 Sahana Asokan: Narrative right? And it’s like you want
322 00:39:20.170 ⇒ 00:39:28.010 Sahana Asokan: different parts of the dashboard to to be able to answer that sequence of questions. Yeah, that makes sense. I think
323 00:39:28.530 ⇒ 00:39:57.760 Sahana Asokan: as much as I want to know the way. Like all the sequence of questions. Come up. I just don’t. But I I’ll definitely try to keep that in mind when we are working on it. But I think that’s helpful, like other than the mobile like that’s more of a formatting issue. I think just understanding like, you’re literally like your use cases like, I know, I’ve seen some examples and snapshots of the dashboards you are using. So I know what you know. Metrics you’re looking for. But I think the workflow context is really helpful.
324 00:39:57.760 ⇒ 00:40:04.179 josh: And like, if you ever like, as you guys build these things like, I’m cool with like turning Fridays into like demo days.
325 00:40:04.500 ⇒ 00:40:11.219 josh: and like, I’ll sit here and I’ll explain to you exactly how my brain works. When I look at this stuff with you guys, you guys can just walk through whatever
326 00:40:12.290 ⇒ 00:40:20.540 josh: or like any random day. Like, if you guys just want to do a demo day like, I’m happy to just like, explain to you, okay, based on what I see. This is how I’m gonna use it
327 00:40:21.000 ⇒ 00:40:23.360 Sahana Asokan: Yeah, no, yeah, exactly. I think it’s like.
328 00:40:23.500 ⇒ 00:40:43.810 Sahana Asokan: it’s I. I think the next step is also thinking about how we can increase usage of these dashboards. It’s not useful to just have a bunch. It’s like, I actually want people to use them. So that I would say, the next phase after data quality is really making these usable, you know, like people actually go in and use these. So I’m sure we’re gonna have to change the design of some of them.
329 00:40:43.810 ⇒ 00:40:45.290 josh: Yep, exactly.
330 00:40:46.870 ⇒ 00:40:47.610 Sahana Asokan: Yeah.
331 00:40:49.720 ⇒ 00:41:02.450 Robert Tseng: Alright. I think we’re good. We can hop off. Yeah. Fridays are usually gonna be retros. We can- we can do Demos as well. It’s a bit of a longer meeting, a process not able to run it today. But starting next week, we’ll probably use the full 45.
332 00:41:02.660 ⇒ 00:41:12.629 Robert Tseng: But yeah, I think we’re gonna we’re gonna sync on roadmap. We’ll send you the list of the priorities that we have, you know, for the next sprint. And then, Josh, you just you just kind of sign off on
333 00:41:12.750 ⇒ 00:41:18.320 Robert Tseng: what? What? On the order that we should tackle them? I think that’s kind of what we’re gonna aim to do. By the end of the day
334 00:41:18.570 ⇒ 00:41:21.419 josh: Cool awesome sounds, good guys, thanks.
335 00:41:21.800 ⇒ 00:41:22.940 Robert Tseng: Alright! Thanks everyone.