Meeting Title: [Eden] Standup and Weekly Sprint Retro-Planning Date: 2025-04-25 Meeting participants: Annie Yu, Demilade Agboola, Robert Tseng, Rob, Awaish Kumar
WEBVTT
1 00:08:42.064 ⇒ 00:08:42.709 Awaish Kumar: Hello!
2 00:08:46.370 ⇒ 00:08:47.889 Awaish Kumar: Hi! How are you doing.
3 00:08:48.740 ⇒ 00:08:50.259 Robert Tseng: I’m good. How are you?
4 00:08:52.120 ⇒ 00:08:53.780 Awaish Kumar: Oh, I’m good as well.
5 00:08:58.073 ⇒ 00:09:03.060 Robert Tseng: Grabbing some tickets and responding to some of the messages here.
6 00:09:10.200 ⇒ 00:09:13.706 Robert Tseng: Okay, I’ll get started and
7 00:09:15.500 ⇒ 00:09:30.670 Robert Tseng: I think overall this week was great. We would move forward on a lot of things so we’ll do just like a kind of quick scan through the outstanding tickets, you know, if Josh or other Eden folks join, I imagine they’ll probably derail the conversation. So
8 00:09:31.234 ⇒ 00:09:35.579 Robert Tseng: want definitely wanna cover anything that’s in flight that we need help on
9 00:09:36.035 ⇒ 00:09:45.439 Robert Tseng: and then, since today is a bit of an extra like a longer session, we’ll talk about some planning and kind of like what the next week is gonna look like as well.
10 00:09:48.640 ⇒ 00:09:51.786 Robert Tseng: Hey? Rob? Say, saw that you just joined
11 00:09:52.840 ⇒ 00:09:55.269 rob: Now I was just coming on to say, Hey, guys.
12 00:09:55.840 ⇒ 00:09:57.739 rob: I don’t really have anything for you. But.
13 00:09:58.164 ⇒ 00:10:02.409 Robert Tseng: Okay, any, any update on the influencer affiliate stuff.
14 00:10:03.105 ⇒ 00:10:08.220 rob: So they actually gave me. Yeah, it changed a little bit
15 00:10:08.450 ⇒ 00:10:15.510 rob: how we’re doing it. You guys probably heard that drip is leaving right.
16 00:10:16.980 ⇒ 00:10:18.099 Robert Tseng: I did not. Yeah.
17 00:10:18.410 ⇒ 00:10:25.870 rob: Yeah, so they’re done at the end of the month. So I have to take over. They were manually filling out some spreadsheets.
18 00:10:26.010 ⇒ 00:10:26.485 Robert Tseng: Right.
19 00:10:27.920 ⇒ 00:10:30.050 Robert Tseng: Don’t be, Doc. Yeah.
20 00:10:30.520 ⇒ 00:10:33.919 rob: Yeah. Oh, is that what they call it? The offline spend.
21 00:10:34.330 ⇒ 00:10:35.160 Robert Tseng: Yep. Yep.
22 00:10:35.670 ⇒ 00:10:41.619 rob: Okay, yeah. So I’ve been working on that. Oh, shit, I didn’t get you the influencer bigquery stuff.
23 00:10:42.520 ⇒ 00:10:43.880 Robert Tseng: All good.
24 00:10:45.030 ⇒ 00:10:47.399 rob: I’ll get that to you today, I promise.
25 00:10:47.570 ⇒ 00:10:50.930 Robert Tseng: Okay, I.
26 00:10:50.930 ⇒ 00:10:55.119 rob: That’s all I got. Yeah, this, probably your sprint retrospective right?
27 00:10:55.860 ⇒ 00:11:03.320 Robert Tseng: Yeah, we’re gonna well, we have a few out outstanding things that we’re gonna probably clear out today. And then we’re also doing like a retro and planning for next week.
28 00:11:04.660 ⇒ 00:11:05.909 rob: Okay. Alright, I’ll.
29 00:11:05.910 ⇒ 00:11:06.290 Robert Tseng: Okay.
30 00:11:06.290 ⇒ 00:11:09.299 rob: Cause I got another call. But yeah, good to see you guys.
31 00:11:09.300 ⇒ 00:11:10.909 Robert Tseng: Hey? Yeah, thanks thanks for joining.
32 00:11:10.910 ⇒ 00:11:12.430 rob: Alright! See ya bye.
33 00:11:12.600 ⇒ 00:11:17.739 Robert Tseng: Bye, hey? That was a big bomb.
34 00:11:18.410 ⇒ 00:11:21.174 Robert Tseng: I guess if you guys don’t know
35 00:11:24.370 ⇒ 00:11:28.479 Robert Tseng: drip is the performance marketing
36 00:11:28.950 ⇒ 00:11:33.989 Robert Tseng: like team, pretty much like they don’t have people in house. They were using an agency for that as well.
37 00:11:34.150 ⇒ 00:11:41.610 Robert Tseng: So huh! I guess it’s no surprise now that the marketing dashboard has kind of been
38 00:11:42.030 ⇒ 00:11:45.710 Robert Tseng: just like silent this past week.
39 00:11:46.581 ⇒ 00:11:56.538 Robert Tseng: I mean, I guess. Fortunately we’re not getting too much push on this. I think we are still moving things over to corral. So I think we’re, we’re okay there. But
40 00:11:57.410 ⇒ 00:12:03.594 Robert Tseng: okay, wow, anyways, I will have to go and figure out what that really means for our priorities.
41 00:12:05.610 ⇒ 00:12:15.409 Robert Tseng: yeah, a couple of updates just for the the team to kind of stay on track. So yeah, wish. And I met with corral yesterday. I think we were clear on like what the ask was. And so
42 00:12:15.940 ⇒ 00:12:22.219 Robert Tseng: pretty much we’re sharing some data models with them so wish I sent them kind of the.
43 00:12:24.040 ⇒ 00:12:27.030 Robert Tseng: Yeah, the the model that you sent me so that they can
44 00:12:27.260 ⇒ 00:12:32.730 Robert Tseng: try to basically replicate what we have for the product level
45 00:12:33.080 ⇒ 00:12:41.499 Robert Tseng: filtering. And then I also sent them the sample data from the current fact. Add model. We’re basically asking them to.
46 00:12:42.010 ⇒ 00:12:47.809 Robert Tseng: you know, give us add data in that format so that you can easily join it into the model.
47 00:12:48.421 ⇒ 00:13:06.120 Robert Tseng: Once we do that. And then we also have the offline spends from the Google Sheet, we should have both of those enough to update fact, add performance. This will probably happen next week, and then we can build out the the mer report, finally, which is marketing efficiency.
48 00:13:06.890 ⇒ 00:13:16.990 Robert Tseng: Report. It’s like taking the full marketing budget. And then, rather than only looking at the paid channels that we have been doing up to this point, so
49 00:13:17.620 ⇒ 00:13:30.040 Robert Tseng: glad that that’s been moving along on the Health Club. Pixel tracking stuff. Seems like corral’s pretty versed in licensing and how to help with that. So actually, I loop them in. So I’m actually taking taking this off my plate.
50 00:13:30.876 ⇒ 00:13:32.110 Robert Tseng: As well.
51 00:13:32.510 ⇒ 00:13:34.156 Robert Tseng: And then.
52 00:13:36.330 ⇒ 00:13:46.499 Robert Tseng: yeah, few other things. Maybe we’ll just start here any product launch. Dash oh, I guess you just sent this draft. I didn’t actually look at it yet. But my, maybe we’ll just
53 00:13:46.650 ⇒ 00:13:49.460 Robert Tseng: review it together here.
54 00:13:55.020 ⇒ 00:14:00.300 Robert Tseng: okay. So great to update. Picker
55 00:14:01.540 ⇒ 00:14:04.870 Robert Tseng: has to be calendar style because it’s a parameter.
56 00:14:05.150 ⇒ 00:14:06.080 Robert Tseng: I see.
57 00:14:12.650 ⇒ 00:14:17.429 Robert Tseng: I mean, I think it’s okay to send to them.
58 00:14:17.430 ⇒ 00:14:27.299 Annie Yu: Yeah. And if it if we want, we can always add like a reference table, say, like, if you want to see the last 6 months. What would the start date be for.
59 00:14:27.810 ⇒ 00:14:28.790 Robert Tseng: I see? I see.
60 00:14:29.330 ⇒ 00:14:33.210 Robert Tseng: Yeah. And that’s just because this model is structured differently from this model.
61 00:14:33.210 ⇒ 00:14:41.650 Annie Yu: Yeah, cause we need that trailing. So we can’t just use like a date filter. So the actual filter is.
62 00:14:41.870 ⇒ 00:14:49.907 Annie Yu: I’m setting like 1 20 days prior to the select start date, but that 1 20, just a random number.
63 00:14:51.780 ⇒ 00:14:52.540 Robert Tseng: Got it.
64 00:14:53.830 ⇒ 00:14:54.680 Robert Tseng: Okay?
65 00:14:55.690 ⇒ 00:15:08.139 Robert Tseng: yeah. I mean, I think these are all the right pieces. Obviously a couple of stylistic things like making sure that the you know, the formatting on the dates is consistent across all the reports. I think this is this would be good just like month day
66 00:15:08.552 ⇒ 00:15:15.280 Robert Tseng: would be would be great. But I think, having some more consistency there and then product names
67 00:15:15.810 ⇒ 00:15:20.119 Robert Tseng: great, I mean. So I know that this.
68 00:15:20.120 ⇒ 00:15:29.529 Annie Yu: I do see there are a a few different fields for product name. So I this one. But I’m not sure if we want to use.
69 00:15:30.540 ⇒ 00:15:30.890 Robert Tseng: Yeah.
70 00:15:30.890 ⇒ 00:15:31.410 Awaish Kumar: It’s right.
71 00:15:31.410 ⇒ 00:15:35.520 Robert Tseng: What I like about this one is, it’s yeah. Okay, anyway. Wait. Go ahead.
72 00:15:36.490 ⇒ 00:15:43.159 Awaish Kumar: Yeah. And I was just saying, order assembly table should also have a standardized product name as well. If you want to bring that in.
73 00:15:43.790 ⇒ 00:15:48.580 Robert Tseng: Yeah. So I would say, Use this one. Whatever is here, Annie.
74 00:15:49.249 ⇒ 00:15:55.639 Annie Yu: So did you mean wish? Did you mean this order? Summary also has standardized product name.
75 00:15:58.165 ⇒ 00:16:03.880 Awaish Kumar: Yes, I I think it should have. If not, I can just add it in, but I’m
76 00:16:04.120 ⇒ 00:16:06.980 Awaish Kumar: I’m sure that it it it has.
77 00:16:06.980 ⇒ 00:16:11.050 Annie Yu: Okay. Okay, then I’ll make that aligned.
78 00:16:11.930 ⇒ 00:16:12.760 Robert Tseng: Great.
79 00:16:15.460 ⇒ 00:16:18.610 Robert Tseng: Yeah. You mentioned something about the run rate here.
80 00:16:18.860 ⇒ 00:16:28.531 Annie Yu: Yeah, I think, okay, if I did my, because I had never really worked with run rate, but based on my my research, that would mean, like,
81 00:16:32.180 ⇒ 00:16:39.230 Annie Yu: the kind of the month revenue over days of months, and then multiply by wait.
82 00:16:39.500 ⇒ 00:16:51.010 Annie Yu: divided by wait. What am I saying? So? The monthly revenue over the days and months, and then multiply by 3, 65. But I don’t think that’s how they calculate it. So I
83 00:16:51.130 ⇒ 00:16:56.289 Annie Yu: because looking at the example screenshots.
84 00:16:56.460 ⇒ 00:17:04.569 Annie Yu: That number is definitely not that. So I I think that’s where I would need clarification on. What’s the calculation of
85 00:17:05.460 ⇒ 00:17:06.329 Annie Yu: what they mean?
86 00:17:06.339 ⇒ 00:17:06.939 Robert Tseng: Okay.
87 00:17:06.940 ⇒ 00:17:08.710 Annie Yu: 12 months, run rate.
88 00:17:09.109 ⇒ 00:17:10.059 Robert Tseng: Let’s see.
89 00:17:10.060 ⇒ 00:17:13.650 Annie Yu: You see that it’s 6, 29, and then.
90 00:17:15.730 ⇒ 00:17:20.079 Annie Yu: which is very close to their revenue of that period.
91 00:17:21.329 ⇒ 00:17:22.029 Robert Tseng: Yeah.
92 00:17:28.319 ⇒ 00:17:31.279 Robert Tseng: Oh, man, I don’t know what report that was
93 00:17:33.089 ⇒ 00:17:35.956 Robert Tseng: still some more than monthly.
94 00:17:56.149 ⇒ 00:18:03.789 Robert Tseng: Oh, I yeah, I.
95 00:18:05.640 ⇒ 00:18:10.259 Annie Yu: So it’s the the revenue multiplied by 12.
96 00:18:10.260 ⇒ 00:18:15.500 Robert Tseng: It looks like, yeah, it’s just like it’s kind of a pacing metric as well.
97 00:18:15.850 ⇒ 00:18:27.790 Annie Yu: Yeah. And that’s what I did. I think I’m confused. Why, it’s saying 3. Okay, it’s saying 3, 12 here. But if you look at their revenue. It’s also 3 million.
98 00:18:28.230 ⇒ 00:18:35.140 Annie Yu: so shouldn’t that number be much bigger, because we’re multiplying it bye.
99 00:18:35.140 ⇒ 00:18:40.460 Robert Tseng: So this is, you know, 2.5 times 12 right like
100 00:18:42.345 ⇒ 00:18:45.070 Annie Yu: Basically. But I’m doing like.
101 00:18:45.360 ⇒ 00:18:51.560 Annie Yu: divided by the days of this month first, st and then multiply by 3, 65 days.
102 00:18:52.120 ⇒ 00:18:52.790 Annie Yu: So that would be.
103 00:18:53.172 ⇒ 00:18:58.270 Robert Tseng: I see. So you’re doing it like a, yeah. So
104 00:18:59.570 ⇒ 00:19:02.499 Robert Tseng: okay, yeah, I mean, you were.
105 00:19:03.050 ⇒ 00:19:07.290 Robert Tseng: wait. So you’re multiplying by 3, 65 days, and which day are you using.
106 00:19:10.110 ⇒ 00:19:13.050 Annie Yu: It’s it’s dynamic. So it’s based on today. So today.
107 00:19:13.050 ⇒ 00:19:14.309 Robert Tseng: Always based on today. Yeah.
108 00:19:14.712 ⇒ 00:19:17.529 Annie Yu: Months must date over 25 days.
109 00:19:18.190 ⇒ 00:19:21.140 Robert Tseng: Yeah, I mean, I I would. I think what you’re.
110 00:19:21.840 ⇒ 00:19:27.959 Robert Tseng: I think day is probably too volatile to be honest, because it’ll probably be like, if
111 00:19:30.800 ⇒ 00:19:41.300 Robert Tseng: if you’re only if you’re using basically today, times 365 and days into the future. Then, you know, given what like Josh said about like.
112 00:19:41.450 ⇒ 00:19:45.920 Robert Tseng: Hey, people are only really buying orders like every 2 weeks or like.
113 00:19:46.240 ⇒ 00:19:52.850 Robert Tseng: Then on Sundays it’s gonna be, look really low, like 37 is is really low. I mean, this is all products, I guess.
114 00:19:53.160 ⇒ 00:19:57.129 Robert Tseng: But even with all products, that’s still pretty. Okay, yeah, that makes more sense.
115 00:19:59.050 ⇒ 00:20:07.020 Robert Tseng: yeah, I mean 400 million is a lot. I don’t think, actually think there are 400 million dollar business. I think they’re closer to like a hundred 50 million dollar business.
116 00:20:07.020 ⇒ 00:20:07.500 Annie Yu: Yeah.
117 00:20:07.500 ⇒ 00:20:15.329 Robert Tseng: But yeah, anyway, I think it would just be. It would probably be better to like, do what?
118 00:20:15.520 ⇒ 00:20:18.089 Robert Tseng: Closer to what is being done here. So
119 00:20:18.320 ⇒ 00:20:21.040 Robert Tseng: taking the past 30 days, and then we’ll spline by 12.
120 00:20:22.700 ⇒ 00:20:24.750 Annie Yu: Past 30 days, and then.
121 00:20:24.750 ⇒ 00:20:31.389 Robert Tseng: Yeah, like, have like a full month worth, and then multiply by 12 rather than picking like a certain right? Because, like.
122 00:20:31.610 ⇒ 00:20:46.659 Robert Tseng: maybe on this day, like the number is super high, because it’s like Friday, and everyone got their paycheck. And now they’re buying a bunch of stuff. But then, if you looked at like a random, like Sunday, or like something. This number would probably be way lower.
123 00:20:47.237 ⇒ 00:20:48.580 Annie Yu: Okay, okay, that’s.
124 00:20:48.580 ⇒ 00:21:00.819 Robert Tseng: Yeah. So I think that’s yeah. We probably get closer to this. I will say, maybe we just when when you do it, make sure you check the revenue orders and stuff. I think it’s the same model, but just check it against like some of the other
125 00:21:01.518 ⇒ 00:21:12.030 Robert Tseng: like reports that we have, so that there’s not any blatant inconsistencies there. So I would expect revenue and orders to match, like what we have in, like the executive dashboard, for example.
126 00:21:12.180 ⇒ 00:21:15.600 Robert Tseng: or assuming you include all products. Yeah.
127 00:21:16.293 ⇒ 00:21:24.189 Robert Tseng: But otherwise, like, yeah, I think this is this is this is the right metrics. So it looks good. I will still send this to them. Yeah, go ahead.
128 00:21:24.190 ⇒ 00:21:30.187 Annie Yu: No, I I just I wouldn’t know how to interpret this pro charts, but I think that’s what they want, right.
129 00:21:30.760 ⇒ 00:21:38.360 Robert Tseng: Yeah. So I mean, I guess I can tell you how I would interpret this. So it’s like, Okay, yeah, revenue is clearly dropping over
130 00:21:38.670 ⇒ 00:21:44.750 Robert Tseng: this this week. I mean, I think this it’s volatility.
131 00:21:45.170 ⇒ 00:21:49.359 Robert Tseng: Well, I mean, this is obviously concerning to see like
132 00:21:50.200 ⇒ 00:21:55.986 Robert Tseng: day to day. You expect things to spike right? I don’t know what April 14th was, but I’m assumed that was like,
133 00:21:56.380 ⇒ 00:22:09.239 Robert Tseng: you know, that’s in the middle of that’s like, after the 1st 2 weeks. Right then there should be another peak 2 weeks later. So 28, I guess 28 hasn’t happened yet. Oh, I see, I see what this is, this is like a daily calc.
134 00:22:09.240 ⇒ 00:22:11.920 Robert Tseng: Yeah, this is the regular.
135 00:22:12.080 ⇒ 00:22:13.620 Annie Yu: Daily Revenue.
136 00:22:13.620 ⇒ 00:22:34.510 Robert Tseng: This regular day. Yeah. So I guess we would be expecting this to shoot up towards the end of the month, because it’s like, okay. 2 weeks after this we’re expecting people to be purchasing stuff again. I’m not. I’m not sure how that touch for that is. But if you look at a 30 day trailing. Yeah, it’s actually not that volatile. It looks pretty consistent to what we saw last last month as well.
137 00:22:35.010 ⇒ 00:22:35.760 Annie Yu: Yeah.
138 00:22:36.100 ⇒ 00:22:36.700 Robert Tseng: Yeah.
139 00:22:36.890 ⇒ 00:22:49.570 Robert Tseng: right? So I think that’s why it’s important to be able to look at the daily volatility, but then also be able to say, hey? Well, what we’re seeing on the Daily like decline. It’s not that different. It’s not really that much different from what we saw 30 days ago.
140 00:22:50.062 ⇒ 00:22:54.160 Annie Yu: There is still a clear dip. So maybe April is actually a down month.
141 00:22:54.575 ⇒ 00:23:05.599 Robert Tseng: It’s like what this is telling me is the second half of April is doing worse than the you know. The second half of March last year, or like last month. So but I don’t know. Maybe that’s just
142 00:23:06.020 ⇒ 00:23:09.259 Robert Tseng: out is, and we’ll have to kind of see. Towards the end of the month.
143 00:23:09.837 ⇒ 00:23:17.360 Robert Tseng: And then orders wise orders. It looks flat, right? The 14 day and the 30 day trailing average like it looks pretty consistent.
144 00:23:18.790 ⇒ 00:23:32.979 Robert Tseng: So yeah, it’s not like, I think the I guess the concern here is like, yeah, the business isn’t really growing. If you look at all across all products. But let’s just pick like compound some glutide and just see what that looks like.
145 00:23:34.610 ⇒ 00:23:37.540 Robert Tseng: That is strange to me.
146 00:23:39.380 ⇒ 00:23:43.910 Robert Tseng: I would expect higher numbers here. Revenue is 0.
147 00:23:46.010 ⇒ 00:23:46.770 Robert Tseng: Huh?
148 00:23:47.180 ⇒ 00:23:49.749 Robert Tseng: Something feels off about this.
149 00:23:54.810 ⇒ 00:24:01.600 Robert Tseng: Yeah. Okay. Anyway, I think maybe it’ll make more sense after we switch to the standardized product name, because maybe I’m just like not selecting the right stuff.
150 00:24:01.940 ⇒ 00:24:07.911 Robert Tseng: There’s a lot of other products here. Yeah. So I won’t comment too much on like whether or not. It’s accurate or not. But
151 00:24:08.750 ⇒ 00:24:10.659 Robert Tseng: yeah, I’ll just use the all for now
152 00:24:11.690 ⇒ 00:24:18.599 Robert Tseng: new resorting order about new versions returning, yeah. Great new patients returning patients. Great?
153 00:24:19.342 ⇒ 00:24:21.939 Robert Tseng: Yeah. The gender breakdown. Also. Great
154 00:24:22.320 ⇒ 00:24:26.139 Robert Tseng: cap, profit, ratios, cap, profit ratios great.
155 00:24:26.390 ⇒ 00:24:39.459 Robert Tseng: And then the monthly breakdown. Yeah, I mean, I think so. Obviously, there’s a couple of cosmetic things to kind of fix up. And you’re gonna change the filters. I will still send this over to the team later today. So I think that would be great.
156 00:24:40.228 ⇒ 00:24:49.999 Robert Tseng: One last thing I will comment on this is, if you could like add an annotation like we did with the executive dashboard on like what’s excluded here.
157 00:24:50.775 ⇒ 00:24:55.030 Robert Tseng: I think that could be helpful, or just like any.
158 00:24:56.140 ⇒ 00:25:01.030 Robert Tseng: I think it’s assumed that you’re always excluding abandoned orders. So you can always just put that there.
159 00:25:01.200 ⇒ 00:25:03.819 Robert Tseng: But yeah, I think.
160 00:25:03.820 ⇒ 00:25:05.759 Annie Yu: 7, so I will do that.
161 00:25:06.000 ⇒ 00:25:06.730 Robert Tseng: Okay.
162 00:25:09.510 ⇒ 00:25:20.230 Robert Tseng: okay, cool. But yeah, I would expect the order and revenue counts for the totals. When I’m filtering by all products, it should match what I’m seeing here. So just wanna make sure that that’s actually the case.
163 00:25:21.590 ⇒ 00:25:23.879 Annie Yu: Okay, so that, but but just
164 00:25:24.340 ⇒ 00:25:29.080 Annie Yu: to note they are using different models. But yeah.
165 00:25:29.490 ⇒ 00:25:31.469 Annie Yu: it would still make sense if they match.
166 00:25:32.020 ⇒ 00:25:44.220 Robert Tseng: Yes, they’re using different models, and if there is a slight discrepancy, then we should probably try to. I mean, I think you could probably ask a wish like to look into like why, why, the models are different. You’ll be able to tell you how we built one the other.
167 00:25:44.560 ⇒ 00:25:45.150 Annie Yu: Okay.
168 00:25:46.980 ⇒ 00:25:53.870 Robert Tseng: Okay, cool overall looks good. Yeah.
169 00:25:57.350 ⇒ 00:26:03.250 Robert Tseng: So I think this is in little feedback right now.
170 00:26:03.510 ⇒ 00:26:06.430 Robert Tseng: Alright, let’s go through other stuff here. So
171 00:26:06.960 ⇒ 00:26:14.020 Robert Tseng: yeah, I didn’t let it. We had this conclusion. You sent me some information about the
172 00:26:15.050 ⇒ 00:26:17.759 Robert Tseng: order. This is kind of like
173 00:26:20.250 ⇒ 00:26:27.430 Robert Tseng: I forgot. If we needed to say anything else about this order like the delay and the
174 00:26:27.620 ⇒ 00:26:29.710 Robert Tseng: order transaction thing.
175 00:26:33.120 ⇒ 00:26:53.620 Demilade Agboola: To be honest. Yeah, I have. I like, I just fully built out the model which is what is in staging, and I just tagged and into it. So that like at least, we have something that we can look at. But then I haven’t done like fully gone into the investigation. I just was building up the model. So to try and do that today.
176 00:26:54.260 ⇒ 00:26:55.240 Demilade Agboola: Okay, just.
177 00:26:57.320 ⇒ 00:27:10.369 Robert Tseng: Okay, yeah. I mean, I think it would just be good to give Josh a final answer and be like, Hey, look, it’s fine. We looked at these orphan transactions. They do end up turning into orders, but there’s probably like a 1 month delay or something.
178 00:27:10.520 ⇒ 00:27:14.240 Robert Tseng: and I can just move on from that. That’s kind of all I want to see. There.
179 00:27:14.950 ⇒ 00:27:15.680 Demilade Agboola: Gotcha.
180 00:27:16.080 ⇒ 00:27:16.690 Robert Tseng: Yeah.
181 00:27:17.561 ⇒ 00:27:27.490 Robert Tseng: Yeah. So I think these 2 are related customers. Table talked about location parameter still on me. I wish for to go and add that.
182 00:27:27.770 ⇒ 00:27:32.549 Robert Tseng: Yeah. So between that and then this mixed panel stuff. I need to do these 2 things today.
183 00:27:33.210 ⇒ 00:27:38.380 Robert Tseng: Customer journey dashboard. I gave some feedback to Sahana. So
184 00:27:38.830 ⇒ 00:27:47.819 Robert Tseng: she’s not on this call. But actually, Annie, we’re probably gonna pause, Sahana, after today, probably your last day.
185 00:27:47.960 ⇒ 00:28:01.240 Robert Tseng: And then I may actually just end up transferring this over to you, starting next week. So Tbd. On like, whether or not she wants to kind of take the weekend to finish it, but otherwise
186 00:28:01.360 ⇒ 00:28:04.649 Robert Tseng: I think you’ll probably end up owning this moving forward.
187 00:28:07.390 ⇒ 00:28:08.100 Annie Yu: Okay.
188 00:28:08.970 ⇒ 00:28:14.296 Robert Tseng: Yeah, so just a heads up, not assigning it to you yet, because I don’t really feel like you need to do anything yet. But
189 00:28:14.850 ⇒ 00:28:16.769 Robert Tseng: yeah, I’m gonna just let her.
190 00:28:17.700 ⇒ 00:28:23.499 Robert Tseng: So I had to close things out. She has like a round of changes that I wanted her to make, but don’t know if she’ll get to it.
191 00:28:23.640 ⇒ 00:28:24.255 Annie Yu: Yeah.
192 00:28:25.540 ⇒ 00:28:31.750 Robert Tseng: Yeah, maybe we’ll talk about intake stuff. So yeah, Ryan called me. I don’t. I don’t know, I guess.
193 00:28:31.990 ⇒ 00:28:41.709 Robert Tseng: what else we need. Do we need to pull and test data, your segments of bigquery. You have some questions around that I can look into that for you after this call. I think
194 00:28:42.670 ⇒ 00:28:44.050 Robert Tseng: right? Is that where we’re at.
195 00:28:44.630 ⇒ 00:28:51.036 Demilade Agboola: Yes, so I I basically asked that there’s supposedly a
196 00:28:52.520 ⇒ 00:28:58.439 Demilade Agboola: blink from like the web hook into segment and into the destination of bigquery.
197 00:28:58.620 ⇒ 00:29:00.750 Demilade Agboola: I can’t seem to see that.
198 00:29:00.890 ⇒ 00:29:09.100 Demilade Agboola: And obviously, I don’t want to duplicate effort. So I just want to be sure, if if that link does exist, what it it is like.
199 00:29:09.640 ⇒ 00:29:14.900 Demilade Agboola: And so I can just use, use it and test it, and be sure that, like, we’re getting the data that we need.
200 00:29:16.780 ⇒ 00:29:18.210 Robert Tseng: Okay, got it?
201 00:29:22.390 ⇒ 00:29:26.970 Robert Tseng: you know. I might even just send him the link for our stand up right now. See if he wants to join.
202 00:29:28.470 ⇒ 00:29:29.780 Robert Tseng: Let’s see.
203 00:29:34.270 ⇒ 00:29:36.380 Robert Tseng: Okay, whatever it’s fine. I’ll just
204 00:29:36.850 ⇒ 00:29:39.052 Robert Tseng: guess I’ll call him after this.
205 00:29:40.230 ⇒ 00:29:50.350 Robert Tseng: yeah. So I think that’s pretty much it for this like today, this cycle. I mean, I know some of you guys are still working on some things we want to clear out in here.
206 00:29:50.970 ⇒ 00:29:58.190 Robert Tseng: All right, I guess this data bottle? Have you thought? Put much more thought into this demo? A.
207 00:29:59.570 ⇒ 00:30:03.260 Demilade Agboola: A little, but not as much as I’d like.
208 00:30:04.180 ⇒ 00:30:08.049 Demilade Agboola: I still have like the configurations I like
209 00:30:09.570 ⇒ 00:30:16.290 Demilade Agboola: how custom is custom. I think that I think I had a couple of questions about that. And I think I actually did ask that.
210 00:30:16.540 ⇒ 00:30:28.581 Demilade Agboola: But basically my question was, how custom are we talking in these kids? So if it’s very custom, then it does mean, okay, I did actually send this on Tuesday.
211 00:30:30.680 ⇒ 00:30:31.900 Demilade Agboola: let me tag you.
212 00:30:32.080 ⇒ 00:30:33.130 Demilade Agboola: Okay? And for, like.
213 00:30:33.130 ⇒ 00:30:33.710 Robert Tseng: Yeah.
214 00:30:34.010 ⇒ 00:30:36.369 Demilade Agboola: Basically, if it’s custom.
215 00:30:38.190 ⇒ 00:30:41.030 Demilade Agboola: Then it’s not in the product sheet that we get from Zach.
216 00:30:41.746 ⇒ 00:30:46.459 Demilade Agboola: But if it’s fixed, that means there is a like a pattern to it.
217 00:30:47.520 ⇒ 00:31:03.319 Robert Tseng: Yeah. So whatever product field that Annie is using in this dashboard, I think this comes straight out of bast like this is like the best product names. Right? So we end up taking this, and we turn it into different categories. But at its raw form, like these are all the different types of products that we see
218 00:31:03.803 ⇒ 00:31:10.150 Robert Tseng: and so right before we were doing just like product name. And then maybe like plan, type.
219 00:31:11.203 ⇒ 00:31:15.690 Robert Tseng: and then, like, there were some bundles in here.
220 00:31:17.690 ⇒ 00:31:22.050 Robert Tseng: Bundle, bundle bundle trying to find a bundle.
221 00:31:22.960 ⇒ 00:31:28.073 Robert Tseng: That’s like, multi products, yeah, or like, B, 12 is not really?
222 00:31:29.320 ⇒ 00:31:33.907 Robert Tseng: okay, yeah, like, this compound is semaglutide and 50 12 bundle. Right?
223 00:31:34.750 ⇒ 00:31:37.250 Robert Tseng: so yeah, I mean, I think, like.
224 00:31:37.660 ⇒ 00:31:41.040 Robert Tseng: the question is like, Okay, well, we if we’re.
225 00:31:43.190 ⇒ 00:31:46.910 Demilade Agboola: So here’s what I here’s what I’ve seen from.
226 00:31:47.510 ⇒ 00:32:00.920 Demilade Agboola: It appears that they are custom med kits, but like cause when we look at the product name in med kits. So we have med kits, one Medicaid, 2 Medicaid, 3 and 4, and Medicaid 5. Those are the ones I can see so far
227 00:32:01.180 ⇒ 00:32:03.530 Demilade Agboola: that there is a
228 00:32:03.730 ⇒ 00:32:12.969 Demilade Agboola: they. They come as a well, not custom, like a fixed on med kits like. So question is like, what makes up those med kits. And how do we get that information.
229 00:32:14.970 ⇒ 00:32:15.650 Robert Tseng: Okay.
230 00:32:15.900 ⇒ 00:32:25.999 Robert Tseng: yeah. I mean, you’re in that. We could. We could probably ask like Christiana to for that. She’s kind of like the product, admin, admin or whatever. So
231 00:32:26.508 ⇒ 00:32:31.670 Robert Tseng: yeah, any questions that you have about like, how do we actually break this
232 00:32:31.920 ⇒ 00:32:39.558 Robert Tseng: product out into its, you know, individual products? We should, we should, you know, we should tag her and
233 00:32:41.390 ⇒ 00:32:42.260 Robert Tseng: yeah.
234 00:32:45.230 ⇒ 00:32:46.670 Robert Tseng: So even like
235 00:32:46.960 ⇒ 00:33:15.870 Robert Tseng: stock quantity might even be a different category. So like, I think what we need to do is be like, look, we have all of these different product types. Or, like, yeah, like all these product names right now, our hierarchy is just product, name, member, plan, or like membership plan. But in order to like adapt to the evolving like multi level product, hierarchy, here we think that we need to have product name when you have membership plan, bundle like med kit, maybe even like drug quantity
236 00:33:16.220 ⇒ 00:33:23.430 Robert Tseng: and like try. I mean, I don’t know out of 5. Seems like it’s really like a lot. So I don’t know. Maybe we consolidate, and maybe it’s not even.
237 00:33:23.730 ⇒ 00:33:38.540 Robert Tseng: Maybe we don’t even maybe bundle and bed kit aren’t even like separate things. Because, like conceptually, it’s the same thing. It’s just like multiple products in the same order. So like, maybe we just have to come up with our like a Consolidated category for that.
238 00:33:38.760 ⇒ 00:33:48.920 Robert Tseng: But then we can go back to the team and be like this is what we propose, and you know, like, help them help build that mental model for them. Because right now, like, they’re just like
239 00:33:49.300 ⇒ 00:34:06.160 Robert Tseng: creating whatever product they want. I mean from Cutter’s perspective on the growth side. He doesn’t care. He’s just gonna test whatever he wants, and he just wants to find the win winning combination. He’s not thinking about Fan out. He’s not thinking about like how complicated this gets like later on.
240 00:34:06.554 ⇒ 00:34:09.989 Robert Tseng: But we kind of are like kind of working at it from the
241 00:34:10.480 ⇒ 00:34:20.740 Robert Tseng: work from the other, from the other side of the equation. So hopefully, that helps me to understand, like where, like, how this is positioned.
242 00:34:21.380 ⇒ 00:34:30.489 Demilade Agboola: Yeah, I think what we’ll wind up doing. And this, this is just like, once we get the med kits, we can split it out into the individual like products
243 00:34:30.699 ⇒ 00:34:32.549 Demilade Agboola: and attribute it to them.
244 00:34:34.360 ⇒ 00:35:02.630 Robert Tseng: Yeah. So there’s like one thing here that I don’t know if we we don’t have like line item, level, like breakouts, right? Like for other clients, whether pool parts or Javi. We have like an order and order lines model. And like, we can always break out an individual order to it’s multiple products like we haven’t been able to do that here. Which is why we’ve just been making assumptions like, based on the selected product name. It probably includes, like XY, and Z products, right.
245 00:35:04.540 ⇒ 00:35:08.300 Demilade Agboola: I mean, no, so ideally like the orders are just directly tied
246 00:35:08.640 ⇒ 00:35:11.254 Demilade Agboola: to like if someone is buying
247 00:35:13.202 ⇒ 00:35:19.960 Demilade Agboola: what’s it called like Meta trim right like at like a on a monthly subscription.
248 00:35:20.740 ⇒ 00:35:26.440 Demilade Agboola: But like med kits, makes it harder now, because now, ideally, we should have either like an order line
249 00:35:26.720 ⇒ 00:35:27.370 Demilade Agboola: or.
250 00:35:27.370 ⇒ 00:35:27.900 Robert Tseng: Yeah.
251 00:35:28.090 ⇒ 00:35:29.149 Demilade Agboola: Operation of what makes.
252 00:35:29.150 ⇒ 00:35:30.170 Robert Tseng: But we don’t.
253 00:35:30.170 ⇒ 00:35:30.950 Demilade Agboola: Events.
254 00:35:33.580 ⇒ 00:35:38.290 Robert Tseng: Right? Like, I’m I’m saying we don’t have order line. And so we.
255 00:35:39.190 ⇒ 00:36:04.160 Robert Tseng: I mean, yeah, we we kind of have to make the assumption that med kit one, I guess maybe it’s not like the same products every time. So like, that’s maybe some question there. But like, for example, semaglutide. Nick B, 12 like this bundle is always just those 2 products. And so we know that when someone orders that like, we assume that it’s actually one for semi glutide, one for Mcb. 12. Right.
256 00:36:07.850 ⇒ 00:36:09.029 Demilade Agboola: I might need to look at that.
257 00:36:10.480 ⇒ 00:36:12.049 Demilade Agboola: Yeah, I believe so.
258 00:36:12.270 ⇒ 00:36:20.159 Robert Tseng: Yeah, like, it’s not as straightforward. Yeah, I’m totally, I have no idea, like Med Kit to like. I don’t even know what what’s in there. So I I hear you on that like there are certain.
259 00:36:20.580 ⇒ 00:36:21.420 Robert Tseng: You know.
260 00:36:21.770 ⇒ 00:36:32.170 Robert Tseng: I consider kits and bundles the same thing. A bundle to me was just like a 2 product kit, whereas a kit is a n number of products in a
261 00:36:32.740 ⇒ 00:36:34.979 Robert Tseng: like, yeah, like, sold together.
262 00:36:35.720 ⇒ 00:36:43.650 Robert Tseng: And they just the yeah, because we don’t have line item data like.
263 00:36:43.990 ⇒ 00:36:58.300 Robert Tseng: or maybe we do question mark, like, maybe we need to go and create a web hook and and like, get that. But for now we’re just making assumptions at the order level by looking at the product name which
264 00:36:58.880 ⇒ 00:37:06.030 Robert Tseng: which line items, I guess if you want to call it that are actually included in that order.
265 00:37:09.250 ⇒ 00:37:10.590 Demilade Agboola: Yeah, yeah.
266 00:37:11.970 ⇒ 00:37:27.940 Robert Tseng: So I don’t know if that means that we. In the meanwhile we generate our own order line model. That’s basically like we come up with the line item like ids or whatever like. I don’t know. It seems like there’s a lot of work to do that. But I’m just trying to like, think through like, how do we?
267 00:37:29.530 ⇒ 00:37:41.644 Robert Tseng: I don’t even know if we are accurately reporting at the product level. I do think that we do. At which correct me if I’m wrong, but like product sales summary like. If it saw this bundle, it would put it would it would,
268 00:37:42.190 ⇒ 00:37:48.239 Robert Tseng: it would give credit to both, or it would. Yeah, it would count both Sema and make me 12 right.
269 00:37:56.300 ⇒ 00:38:01.679 Awaish Kumar: I I think that like it, it is going to
270 00:38:01.990 ⇒ 00:38:05.280 Awaish Kumar: the revenue is going to be assigned to one basically.
271 00:38:05.280 ⇒ 00:38:07.869 Robert Tseng: Yeah, okay, it’s only assigned to one.
272 00:38:08.390 ⇒ 00:38:09.090 Awaish Kumar: You know.
273 00:38:12.250 ⇒ 00:38:18.149 Demilade Agboola: Yeah, she’s gonna assign to the 1st one. Actually, because it’s like a case. It’s like a conditional like case when statement.
274 00:38:18.660 ⇒ 00:38:19.000 Robert Tseng: Yeah.
275 00:38:19.420 ⇒ 00:38:20.780 Demilade Agboola: They’ll keep like
276 00:38:21.050 ⇒ 00:38:27.619 Demilade Agboola: going through, and if the condition matches, so if the 1st condition, which may be summer, for instance, it would assign it to summer.
277 00:38:27.800 ⇒ 00:38:34.740 Demilade Agboola: so by time, so it won’t be available to be counted for like a make. Make. Be 12 bundle.
278 00:38:37.120 ⇒ 00:38:37.820 Robert Tseng: Okay.
279 00:38:40.480 ⇒ 00:38:56.520 Robert Tseng: okay. Well, I mean, clearly, we’re doing it wrong. And then I mean, it’s fine, like, no one’s caught it yet at this point. But like, yeah, this isn’t gonna work as they scale up, get more bundles and kits like. Obviously, that’s probably not the majority of their sales right now, which is why nobody has looked into it too closely. But
280 00:38:56.981 ⇒ 00:39:12.260 Robert Tseng: okay, so let’s just kind of bring it back. So I think, yeah, you should probably follow up with Christiana. Ask her a couple of questions like How do you really assign, like different med kits? But then, also, like, maybe we should be asking the question like, Okay, well, why don’t we have order line data like, Where do, where should be getting this from?
281 00:39:12.672 ⇒ 00:39:41.250 Robert Tseng: But yeah, I feel like we need to push. We need to drive that conversation. Like we we own the product data model. We need to make sure that like we’re handling like the fan out of more products, like, properly. So, yeah, even if like, there isn’t like an action for you to take right now. And you’re just asking more questions, I think, thinking through the question, the model figuring out like, what state is it in? Currently, what needs to be in the future like that’s that’s kind of what I’m I’m hoping that you’ll you’ll take from from this.
282 00:39:42.620 ⇒ 00:39:44.110 Demilade Agboola: Yeah. Sounds good. Sounds good.
283 00:39:44.840 ⇒ 00:39:45.500 Robert Tseng: Okay.
284 00:39:47.660 ⇒ 00:39:57.809 Robert Tseng: cool if you need help. Like drafting message, you know. Send me ideas like, always help kind of edit or like, refine the messaging, but I I do want to not be the bottleneck for the for the message.
285 00:39:58.620 ⇒ 00:40:04.440 Demilade Agboola: No, no, it’s fine. I usually just sends a message to Christina, so we’ll see what she says, and then we can kick off from there.
286 00:40:04.830 ⇒ 00:40:05.496 Robert Tseng: Okay, cool.
287 00:40:08.020 ⇒ 00:40:14.410 Robert Tseng: Okay. So I’ll make a note here. So they want to follow up with this Yada.
288 00:40:15.320 ⇒ 00:40:19.820 Robert Tseng: I mean, we’ll probably have to tag other people, which is fine, but starting with her is fine
289 00:40:20.450 ⇒ 00:40:23.760 Robert Tseng: on the handle that kits.
290 00:40:24.080 ⇒ 00:40:27.230 Robert Tseng: We have over 4 line items.
291 00:40:28.540 ⇒ 00:40:38.970 Robert Tseng: Yeah, how do we make sure that order and revenue accounts are properly attributed. And multi product orders.
292 00:40:41.060 ⇒ 00:40:41.955 Robert Tseng: cool.
293 00:40:45.000 ⇒ 00:40:59.610 Robert Tseng: yeah. Last thing here is, I guess we time and doing a lot of I know we took on this call. But I know you guys have like some documentation work. I haven’t clicked into these tickets and really pushed on this. But like, I do want to have, I do want to review the data platform documentation today, because I’m
294 00:40:59.790 ⇒ 00:41:10.109 Robert Tseng: trying to have a conversation with with the Eden team and and getting getting us yeah, like, increasing our contract pretty much. So I need to be able to show like, hey.
295 00:41:10.140 ⇒ 00:41:38.289 Robert Tseng: we are saving you money by like, you know, and that’s more on, like the tooling side. The procurement decisions we made like, I have the case for that. But then also, like, and you guys are saying, like, these are the data sources we’re responsible for. So we’ve maintained here in the model like whatever that looked like like I like. I would like something to be able to have that conversation with them. So I think that’s what you know. I’m the. I know these tickets have kind of been sitting here for a couple of weeks, so if
296 00:41:38.330 ⇒ 00:41:42.060 Robert Tseng: if we have time today like would be great to to push that along.
297 00:41:44.548 ⇒ 00:41:56.590 Demilade Agboola: Yeah. So for that, I I know I wish worked on like a bunch of the models already, and I think that’s it’s in a good spot like that ticket is in a good spot. Add some of the
298 00:41:57.162 ⇒ 00:42:04.149 Demilade Agboola: maybe some of the newer models, but like I don’t think it’s too many of them, maybe one or 2. But like that right now, it’s in a good spot.
299 00:42:05.080 ⇒ 00:42:06.370 Robert Tseng: Okay, cool?
300 00:42:06.887 ⇒ 00:42:14.120 Robert Tseng: Yeah. But I want to do a quick retro here. So like on the past week. Kind of how how did things go. Yeah. Well, well.
301 00:42:14.500 ⇒ 00:42:22.889 Robert Tseng: I guess we don’t have a full time to do like the whole figma with like kind of stickies and everything. But anybody wants to share.
302 00:42:23.170 ⇒ 00:42:27.941 Robert Tseng: Yeah, I guess maybe 3. You could answer one of 3 questions like, it’s like,
303 00:42:29.440 ⇒ 00:42:46.669 Robert Tseng: yeah, like, how are the expectations of like, you know, did did like, did we? Did we plan capacity? Well for you this week? Like, did you feel like you had enough like? And it wasn’t. It wasn’t too much like. Did we like accurately like plan the right amount of work for for this week
304 00:42:47.202 ⇒ 00:42:52.550 Robert Tseng: and then 2 like kind of what like, what? What could have been better?
305 00:42:53.221 ⇒ 00:43:01.549 Robert Tseng: Like. Maybe you got stuck on something that you wanted faster feedback on, or like. You didn’t know where to go like if there were any mistakes that we made that we can can learn from.
306 00:43:02.770 ⇒ 00:43:13.969 Robert Tseng: yeah. The 3rd one was really more like what went. Well. But honestly, I think that’s fine. We can skip that question. I really just want to know what we could be doing better. So yeah, maybe just if if you guys could.
307 00:43:14.290 ⇒ 00:43:17.519 Robert Tseng: we’ll think through and answer one of those 2 questions.
308 00:43:22.488 ⇒ 00:43:24.881 Demilade Agboola: I think from my perspective.
309 00:43:26.110 ⇒ 00:43:32.510 Demilade Agboola: things scoping was fine. I like was anything of like being too overwhelmed with work.
310 00:43:33.152 ⇒ 00:43:38.260 Demilade Agboola: On Eden and being able to.
311 00:43:39.490 ⇒ 00:43:48.320 Demilade Agboola: I think I think the only thing that like really happened. If I was like kind of like a spanner, and work was like what happened today with the like, the tableau dashboards.
312 00:43:49.057 ⇒ 00:43:53.352 Demilade Agboola: where like things just did not work so for that, I’m.
313 00:43:53.710 ⇒ 00:43:57.979 Robert Tseng: What was that? Was that just like bad day? I came in like, I mean, yeah.
314 00:43:58.380 ⇒ 00:44:06.180 Demilade Agboola: It’s a different model that we’re we’re not using for anything in particular, and
315 00:44:07.300 ⇒ 00:44:11.669 Demilade Agboola: it just broke like the the time. So it’s a it was a date issue
316 00:44:12.203 ⇒ 00:44:15.936 Demilade Agboola: we’ll still need to fix it. But effectively it was
317 00:44:16.680 ⇒ 00:44:30.220 Demilade Agboola: was expecting data in the dates, month, year format. And the data came in in the month, day, year, format, and that broke that model and broke the entire Dbt run. So even like models that one like
318 00:44:30.420 ⇒ 00:44:34.070 Demilade Agboola: it related to what is the one related to it at all.
319 00:44:34.557 ⇒ 00:44:35.539 Demilade Agboola: Do not run
320 00:44:36.064 ⇒ 00:44:44.280 Demilade Agboola: and so that affected models that we were like using for our reports and everything. So I had to send a fix to take out that model
321 00:44:45.069 ⇒ 00:44:58.719 Demilade Agboola: and then force the rerun, and then started refreshing. Our tableau extracts, and obviously the dashboards followed, and then I had to also send the subscriptions again. So it will appear in the channels
322 00:44:58.870 ⇒ 00:45:00.179 Demilade Agboola: for ease of use?
323 00:45:00.731 ⇒ 00:45:05.698 Demilade Agboola: But yeah, it will. Just basically. So I’m trying to set up tests so that
324 00:45:06.260 ⇒ 00:45:13.040 Demilade Agboola: number one freshness test. So if there are no counts for the if there are no there’s no desire for the previous day, it will know.
325 00:45:13.230 ⇒ 00:45:19.769 Demilade Agboola: And also just like figuring out like the Dbt run, so that like they’re persistent. So even if
326 00:45:19.920 ⇒ 00:45:24.600 Demilade Agboola: a model breaks. But there’s another line of models that can run, they can still run as well.
327 00:45:25.105 ⇒ 00:45:29.809 Demilade Agboola: So that’s what I’m working on today, so that there is some robustness in our dB, 2 runs as well.
328 00:45:33.930 ⇒ 00:45:34.600 Robert Tseng: Okay.
329 00:45:35.176 ⇒ 00:45:46.919 Robert Tseng: Yeah. I know that stuff is not like easy and a fast fix. So I mean, thanks for handling it very smoothly. That like it was not really much of a disruption. We caught it within a couple of hours, so like that was
330 00:45:47.030 ⇒ 00:45:47.960 Robert Tseng: like, I don’t.
331 00:45:48.280 ⇒ 00:45:58.630 Robert Tseng: I think that stuff always goes underappreciated from the clients. But like, yeah, no, I think that. That was it was great that we were able to respond so quickly and and deal with it so.
332 00:45:59.620 ⇒ 00:46:05.799 Demilade Agboola: Yeah, but yeah, I think all things, all things being all things.
333 00:46:05.920 ⇒ 00:46:21.950 Demilade Agboola: all, all in all, I think was a fine week. Like, I said. I’ve also got the fact order, life, lifecycle staging model out. So it’s it’s exactly like what the prod will look like. So allows us to test and see whether vector metrics were missing.
334 00:46:22.509 ⇒ 00:46:26.209 Demilade Agboola: And just the general quality of our data as well.
335 00:46:27.830 ⇒ 00:46:32.689 Demilade Agboola: yeah, like again, I I don’t think there’s necessarily anything
336 00:46:32.850 ⇒ 00:46:45.730 Demilade Agboola: that we’ve gotten negative feedback on. I think also, just being able to, maybe proactively, which is kind of like your point about the med kids is proactively make suggestions on like.
337 00:46:46.210 ⇒ 00:47:03.779 Demilade Agboola: Hey, how do we handle this? And like, these are like cleared like data issues or data quality issues that you’re having, but you might not necessarily be aware of them, so that, like that puts us in a light where we are like the people who are like making the.
338 00:47:05.250 ⇒ 00:47:15.960 Demilade Agboola: But by showing them that there are solutions that can be provided. Basically, we’re showing them what the issues are and what solutions we can provide to those issues, and just like allowing them also be feel confident in our like expertise.
339 00:47:16.890 ⇒ 00:47:32.759 Robert Tseng: Yeah, yeah, I mean, I think that’s that’s what will help build trust, not when we’re reactive. But when we’re able to be like, hey? We noticed this, we think this is coming like, this is what we think we should do providing those recommendations. Like, yeah, I think those go a lot. Yeah, that that’s
340 00:47:33.200 ⇒ 00:47:57.470 Robert Tseng: that’s hopefully the the direction we’re heading in, like, we’re doing less like reactive building a bunch of reports like, you know, I I know that. You know, we got we spent a lot of time building up a lot of these models. Now, now, it’s just like making a small adjustments here and there. Maybe sometimes we’re changing sources, adding new fields, but otherwise, like we get to think a bit longer term in the direction of the business like
341 00:47:57.962 ⇒ 00:48:02.639 Robert Tseng: and pay pay attention to like what’s what’s coming. So we can anticipate some of this.
342 00:48:07.130 ⇒ 00:48:08.540 Demilade Agboola: But yeah, that’s it from me.
343 00:48:08.920 ⇒ 00:48:16.184 Robert Tseng: Okay, a wish. And Annie, any any thoughts? If you forgot the question just let me know. But
344 00:48:17.950 ⇒ 00:48:18.490 Robert Tseng: yeah,
345 00:48:19.240 ⇒ 00:48:28.171 Awaish Kumar: Yeah, for me, like I thought, I think like, this week has been very little work on this client. Mo. Most of the task has been like,
346 00:48:28.600 ⇒ 00:48:32.630 Awaish Kumar: blocked, or we’ve been stuck on something. But yeah.
347 00:48:33.210 ⇒ 00:48:41.140 Robert Tseng: Yeah, for you. Specifically, I guess the marketing stuff. I guess we know why it was blocked. They like, we’re going through some.
348 00:48:41.630 ⇒ 00:48:50.470 Robert Tseng: Yeah, they lost a performance marketing team. I guess so. And then corral. We’re just like kind of meeting with them. And like, kind of rearranging some things.
349 00:48:52.750 ⇒ 00:48:57.410 Robert Tseng: Yeah. So I I hear you that you didn’t really have that much on your plate this week.
350 00:48:58.940 ⇒ 00:49:05.150 Awaish Kumar: Yeah, but other otherwise, like, yeah, things have been, go to the on the
351 00:49:05.500 ⇒ 00:49:08.279 Awaish Kumar: I don’t like. I think the
352 00:49:08.420 ⇒ 00:49:12.709 Awaish Kumar: the the overall with the client. Things have been going very smooth, right.
353 00:49:13.550 ⇒ 00:49:25.720 Robert Tseng: Yeah, definitely smoother. So, yeah, I do. You know, I kind of split away and did a lot of your guys domain. So a wish. I’ve kind of kept you on marketing stuff just because you built those initial models. And like.
354 00:49:26.070 ⇒ 00:49:31.010 Robert Tseng: you know, I feel like that’s just like a dedicated domain. And then David Lotte is really kind of
355 00:49:31.310 ⇒ 00:49:39.050 Robert Tseng: doing everything else. Which maybe those are less frequent, and maybe they’re more like ad hoc, or whatever. But like
356 00:49:39.640 ⇒ 00:49:41.180 Robert Tseng: I don’t know. Hopefully.
357 00:49:42.310 ⇒ 00:49:55.610 Robert Tseng: I think it made sense to kind of have both of you on it. But you could. You guys can let me know, like later on. Like, if you feel like, Hey, like this is really, this should really just be one person like, I’m open to that feedback. But I feel like this cadence has been good. So like
358 00:49:56.370 ⇒ 00:49:58.520 Robert Tseng: but anyway, that’s yeah.
359 00:50:01.860 ⇒ 00:50:04.509 Annie Yu: Yeah. And for me, I think.
360 00:50:04.770 ⇒ 00:50:09.540 Annie Yu: And this is probably not like only this week. But I think
361 00:50:10.110 ⇒ 00:50:34.300 Annie Yu: after joining Eden, I’m still like learning, spending a lot of time learning metric calculation. Just because I was like in my past life, I was never really working with like finance or marketing related data. So having to figure out like metric calculation, or just the definition is, one thing that’s taking some time for me. And I think.
362 00:50:35.280 ⇒ 00:50:57.559 Annie Yu: while we’re on like documentation, I think that’s something that I would be happy to cover in the future as well. And also like what dashboards using what model and also, I feel like Eden does have a lot of tables compared to the other clients. So also for me, it’s like figuring out
363 00:50:57.820 ⇒ 00:51:06.310 Annie Yu: the relationships between each table, and then kind of what granularity each is on is also like one thing that’s taking me
364 00:51:07.515 ⇒ 00:51:08.490 Annie Yu: sometime.
365 00:51:08.860 ⇒ 00:51:14.960 Annie Yu: But I do think that those things will become easier as as I go.
366 00:51:15.570 ⇒ 00:51:16.220 Robert Tseng: Okay.
367 00:51:16.340 ⇒ 00:51:23.160 Robert Tseng: yeah, I guess my concern any with you kind of being like the primary analyst on this client is that like
368 00:51:23.760 ⇒ 00:51:30.629 Robert Tseng: the we do kind of work with every function here. So like the breadth of the domain expertise is
369 00:51:30.750 ⇒ 00:51:46.529 Robert Tseng: probably more than what you’ve seen before. So like, I know that we kind of talk talk through like trailing averages, and some of these like finance metrics. You can always ask me if you need to like. If you want to learn. And if you feel like this is too much, and you would rather like, Yeah, I guess
370 00:51:46.700 ⇒ 00:52:06.340 Robert Tseng: you let me know. I guess. Because before, like, yeah, like, I’ve, I’ve worked across every single team. So like, I know the data for, like all these different functions. But I don’t expect everyone to to operate that way. So I’m kind of being cautious about, like, yeah, I want to give you opportunity to go and like.
371 00:52:06.410 ⇒ 00:52:19.835 Robert Tseng: really take some of these like kind of learn. Learn some new things here. But then I also want you to be able to go deep and and like in like a couple of projects. I know we kind of pushed one of them back, which was the
372 00:52:20.854 ⇒ 00:52:24.529 Robert Tseng: like the how to handle. And like this this more like
373 00:52:24.760 ⇒ 00:52:31.710 Robert Tseng: forecasting ml, kind of work here, which I want to bring back into cycle. So I’m I’m trying to find the right balance of like
374 00:52:31.830 ⇒ 00:52:41.439 Robert Tseng: not spreading you across a bunch of like random tasks on functions, while also giving you opportunity to like, go deeper in the stuff that you’re more interested in.
375 00:52:41.920 ⇒ 00:52:46.619 Annie Yu: Yeah, sounds good, and I will. I will ping you
376 00:52:47.730 ⇒ 00:52:56.210 Annie Yu: when when I do have more questions. But I also think like oat and damalade are like super knowledgeable. I feel like whenever I ask a question.
377 00:52:56.320 ⇒ 00:53:01.880 Annie Yu: always just always has the answer about like data. So that’s 1 good thing.
378 00:53:02.910 ⇒ 00:53:03.570 Robert Tseng: Cool.
379 00:53:03.820 ⇒ 00:53:19.429 Robert Tseng: Yeah, let me wrap this up with one more thought. So I feel like this seems getting more settled on this client. I feel like I’m kind of the source of like ideas for most of these tasks and stuff. But now that you’re spending more time in the data like I noticed that.
380 00:53:19.790 ⇒ 00:53:35.259 Robert Tseng: you know, especially like Dame a lot of you’ve you’ve brought up some things that like, Hey, you’re seeing this while you’re doing some other investigation like, maybe we should look at that like, I want to get that kind of feedback from from you guys because you’re spending time in the data.
381 00:53:35.590 ⇒ 00:53:57.930 Robert Tseng: probably more than me, or just as much as me, and nobody else on the in on the client side is so like anything in it. I think data work is just like that when you’re doing one task, maybe like you just along the way you figure out like, Hey, there’s like some stuff that’s interesting or something that’s off along the way. And and I want you to be able to contribute to this roadmap as well. So
382 00:53:58.256 ⇒ 00:54:11.313 Robert Tseng: yeah, like, I I just wanna call that out that like, I think that’s that will help, especially once we get to a place where everybody’s kind of pitching in ideas and like kinda being involved in the planning together.
383 00:54:11.800 ⇒ 00:54:21.049 Robert Tseng: obviously, that is my job, and I will keep doing that. But I you know, I I want you to feel a power to go in and tell me, like the the opportunities you you see.
384 00:54:25.990 ⇒ 00:54:26.770 Demilade Agboola: Sounds good.
385 00:54:26.770 ⇒ 00:54:34.849 Robert Tseng: Okay, okay. Yeah. Alright, I think we’re that’s it for me. Yeah. Thanks. Thanks to work on the good work this week. Team.
386 00:54:35.990 ⇒ 00:54:50.679 Demilade Agboola: Just something quick to add. I’ll be in the Us. From tomorrow till the 20th of May, so like the early EU hours, that I, you know, hop on, and kind of do. The tableau work might be like early us hours instead, but like
387 00:54:50.840 ⇒ 00:54:55.729 Demilade Agboola: it won’t be as early as like 5 Am. Us in the like New York time.
388 00:54:55.870 ⇒ 00:54:56.490 Demilade Agboola: It’ll probably.
389 00:54:56.490 ⇒ 00:54:58.959 Robert Tseng: Yeah, okay, you’re gonna be in Florida.
390 00:54:59.730 ⇒ 00:55:02.539 Demilade Agboola: I’m not in Minnesota. Largely.
391 00:55:03.360 ⇒ 00:55:05.060 Robert Tseng: Oh, cool. Okay.
392 00:55:05.450 ⇒ 00:55:06.185 Annie Yu: Fun.
393 00:55:08.006 ⇒ 00:55:15.409 Demilade Agboola: I mean, the weather is much better now. I was there during winter. That was terrible, but like now it’s it’s it’s warm out so.
394 00:55:16.410 ⇒ 00:55:17.140 Robert Tseng: Nice.
395 00:55:18.150 ⇒ 00:55:22.969 Robert Tseng: Okay? Well, I’ll see you all in a bit at the retro, I guess, though.
396 00:55:23.170 ⇒ 00:55:24.670 Annie Yu: Yeah. Alright. Thank you. Bye.
397 00:55:25.290 ⇒ 00:55:26.170 Demilade Agboola: Alright, then bye.