Meeting Title: [Eden] Daily Standup Date: 2025-08-20 Meeting participants: Annie Yu, Andrew O’Neil, Henry Zhao, Amber Lin, Awaish Kumar, Vashdev Heerani, Demilade Agboola
WEBVTT
1 00:00:07.520 ⇒ 00:00:11.300 Amber Lin: Hi, team. Sorry, another meeting ran over.
2 00:00:11.890 ⇒ 00:00:13.610 Amber Lin: I don’t Hi.
3 00:00:15.110 ⇒ 00:00:20.330 Amber Lin: Share screen… …
4 00:00:21.140 ⇒ 00:00:22.300 Amber Lin: Alright.
5 00:00:22.980 ⇒ 00:00:24.210 Amber Lin: Actually…
6 00:00:30.770 ⇒ 00:00:31.930 Amber Lin: …
7 00:00:40.220 ⇒ 00:00:41.960 Amber Lin: Alright, let’s start here.
8 00:00:42.350 ⇒ 00:00:46.910 Amber Lin: Andrew, is this something we can close now, or…?
9 00:00:49.160 ⇒ 00:01:01.849 Andrew O’Neil: Yeah, I think… I think so. There was an update today that Cutter put in our external Eden channel, but in another Eden channel, it looks like Ryan was able to fix it.
10 00:01:01.880 ⇒ 00:01:02.840 Amber Lin: ….
11 00:01:02.930 ⇒ 00:01:13.829 Andrew O’Neil: So, so, for example, like, this… the… it seems like the Eden team had tasked Ryan with setting this up, from a pixel standpoint, but, …
12 00:01:13.870 ⇒ 00:01:19.099 Andrew O’Neil: you know, they then turn to us, I think, when things don’t work. …
13 00:01:19.100 ⇒ 00:01:34.670 Andrew O’Neil: So I’ll… I’ll, like, reach out to make sure it’s… it is working, because it sounds like it is firing from what they’re QAing today, but the number of purchases seems inaccurate from,
14 00:01:34.670 ⇒ 00:01:45.079 Andrew O’Neil: the… the marketing team’s perspective, so… I think there’s more to do on this one, but it’s… it is firing, that’s the update, but it’s… I think the count is incorrect.
15 00:01:45.410 ⇒ 00:01:49.759 Henry Zhao: Do you know why the count was incorrect, Andrew? Or is that something they’re looking into?
16 00:01:50.510 ⇒ 00:01:57.839 Andrew O’Neil: So I think, yeah, this is… I don’t… do you… does anyone know what Ryan’s role is with Eden? Is he kind of helping with tagging as well?
17 00:01:57.840 ⇒ 00:02:01.580 Henry Zhao: Robert’s… yeah, Robert said… let me read word for word what he just said.
18 00:02:01.700 ⇒ 00:02:08.580 Henry Zhao: … Robert said, Ryan should be on the hook for this, he is the conversion rate optimization lead.
19 00:02:09.320 ⇒ 00:02:11.759 Henry Zhao: But he just has not been able to solve his own problems.
20 00:02:11.900 ⇒ 00:02:15.319 Henry Zhao: So, we need to kind of help diagnose problems.
21 00:02:15.510 ⇒ 00:02:18.329 Henry Zhao: You and me, Andrew. That’s what, Robert….
22 00:02:19.500 ⇒ 00:02:20.010 Andrew O’Neil: Ours, okay.
23 00:02:20.010 ⇒ 00:02:26.040 Henry Zhao: Twitter, like, called me earlier today, like, super upset, along with Mitesh, so this is something we need to manage with our stakeholders.
24 00:02:27.160 ⇒ 00:02:32.100 Andrew O’Neil: Okay. Yeah, then I’ll take a look at this. So, I guess, Amber, this ticket is…
25 00:02:32.690 ⇒ 00:02:42.799 Andrew O’Neil: I guess, complete, but if you want to open up another one, or if you want to modify this one to say, like, help with the purchase event,
26 00:02:43.410 ⇒ 00:02:47.889 Andrew O’Neil: Because this… it… the ticket is related to issues that the team is flagging, so….
27 00:02:47.890 ⇒ 00:02:48.290 Amber Lin: Hmm.
28 00:02:48.290 ⇒ 00:02:59.800 Andrew O’Neil: They at first thought it wasn’t firing at all, but it looks like it is. It’s just… there probably still needs to be some data QA, and if Ryan’s unable to do that, then obviously we can help.
29 00:03:00.510 ⇒ 00:03:02.730 Amber Lin: Gotcha, okay.
30 00:03:03.000 ⇒ 00:03:05.470 Amber Lin: Put it here….
31 00:03:07.510 ⇒ 00:03:08.929 Andrew O’Neil: And you can assign it to me.
32 00:03:08.930 ⇒ 00:03:09.590 Amber Lin: Okay.
33 00:03:14.790 ⇒ 00:03:15.730 Amber Lin: Alright.
34 00:03:16.330 ⇒ 00:03:20.750 Amber Lin: I’ll say that this is urgent, because they seem pretty upset.
35 00:03:20.960 ⇒ 00:03:26.629 Henry Zhao: And then… It might be… become unurgent in 53 minutes, but yeah, for now, let’s put it as urgent.
36 00:03:26.630 ⇒ 00:03:28.740 Andrew O’Neil: Okay, we’ll see.
37 00:03:28.740 ⇒ 00:03:32.020 Amber Lin: … Alright.
38 00:03:35.260 ⇒ 00:03:43.170 Amber Lin: … Checking here… Okay, awesome.
39 00:03:44.160 ⇒ 00:03:50.190 Amber Lin: And on the… Okay.
40 00:04:01.610 ⇒ 00:04:02.630 Amber Lin: Okay.
41 00:04:03.390 ⇒ 00:04:06.239 Amber Lin: So I’ll change this to to-do.
42 00:04:11.230 ⇒ 00:04:12.450 Amber Lin: …
43 00:04:15.730 ⇒ 00:04:19.969 Amber Lin: Annie, I’ll just edit the previous ticket to this.
44 00:04:26.580 ⇒ 00:04:34.900 Annie Yu: No. That one… I still have to do something with Tableau, and that… this funds for model.
45 00:04:35.170 ⇒ 00:04:36.090 Amber Lin: I see.
46 00:04:37.000 ⇒ 00:04:37.720 Amber Lin: Let’s see…
47 00:04:46.000 ⇒ 00:04:46.740 Amber Lin: Bye.
48 00:04:48.250 ⇒ 00:04:48.810 Amber Lin: 2…
49 00:04:53.250 ⇒ 00:04:57.740 Amber Lin: Marcus… Okay.
50 00:05:02.490 ⇒ 00:05:03.180 Annie Yu: Ember.
51 00:05:03.180 ⇒ 00:05:03.950 Awaish Kumar: Hmm.
52 00:05:03.950 ⇒ 00:05:04.810 Annie Yu: modeling work….
53 00:05:04.810 ⇒ 00:05:05.619 Awaish Kumar: What’s the name?
54 00:05:05.620 ⇒ 00:05:06.470 Annie Yu: to us.
55 00:05:06.470 ⇒ 00:05:07.250 Amber Lin: Okay.
56 00:05:12.960 ⇒ 00:05:13.710 Amber Lin: Great.
57 00:05:16.340 ⇒ 00:05:19.100 Amber Lin: Does Vaschev have capacity today?
58 00:05:20.400 ⇒ 00:05:22.070 Amber Lin: Because there’s a Utah.
59 00:05:22.070 ⇒ 00:05:26.500 Awaish Kumar: All depends on… Urgency.
60 00:05:26.500 ⇒ 00:05:27.290 Amber Lin: Okay.
61 00:05:29.150 ⇒ 00:05:30.750 Amber Lin: Okay, sounds good.
62 00:05:31.130 ⇒ 00:05:34.860 Amber Lin: … Alright.
63 00:05:35.250 ⇒ 00:05:43.520 Annie Yu: Amber, 55, I think I’d… I have to….
64 00:05:43.970 ⇒ 00:05:45.679 Amber Lin: Yeah, I think there was another….
65 00:05:45.680 ⇒ 00:05:54.910 Annie Yu: rate, yeah, separately. So I guess my question is which one to do first? Like, this one, or the, dashboard for Jonah?
66 00:05:54.910 ⇒ 00:05:57.229 Amber Lin: How long does a churn rate take?
67 00:05:57.230 ⇒ 00:06:02.520 Annie Yu: For this one, I will have to, write another query in order to show the same charts.
68 00:06:02.720 ⇒ 00:06:07.739 Amber Lin: Or, like, a sub… Not gonna take… how many hours?
69 00:06:08.210 ⇒ 00:06:11.540 Annie Yu: I would say… 2 points.
70 00:06:13.750 ⇒ 00:06:14.900 Amber Lin: 4 hours?
71 00:06:15.320 ⇒ 00:06:17.209 Annie Yu: 2 to 3.
72 00:06:17.600 ⇒ 00:06:18.280 Amber Lin: Okay.
73 00:06:18.710 ⇒ 00:06:24.680 Amber Lin: So that would be… Around 1 to 2 points.
74 00:06:25.410 ⇒ 00:06:26.190 Amber Lin: Okay.
75 00:06:33.070 ⇒ 00:06:34.090 Amber Lin: Oh.
76 00:06:38.340 ⇒ 00:06:39.230 Amber Lin: Okay
77 00:06:55.210 ⇒ 00:07:03.280 Amber Lin: I think we Well, we still need to give Jonah something, …
78 00:07:04.380 ⇒ 00:07:10.680 Amber Lin: We can ask later when we meet with Mitesh how… How important that is.
79 00:07:10.860 ⇒ 00:07:15.640 Amber Lin: I would say… Huh.
80 00:07:15.840 ⇒ 00:07:21.670 Amber Lin: I think they need to make an immediate decision on this, so let’s do this one first, and then let’s do…
81 00:07:21.990 ⇒ 00:07:27.649 Amber Lin: … the finance… And then let’s do the finance one.
82 00:07:29.940 ⇒ 00:07:32.870 Awaish Kumar: But finance needs, modeling, right?
83 00:07:33.150 ⇒ 00:07:34.739 Awaish Kumar: Yeah. So this is blocked.
84 00:07:35.560 ⇒ 00:07:36.060 Amber Lin: Yeah.
85 00:07:36.450 ⇒ 00:07:36.920 Awaish Kumar: Friday.
86 00:07:36.920 ⇒ 00:07:42.020 Amber Lin: Some stuff we can do, I think, but mostly we still need modeling to make it easier.
87 00:07:43.280 ⇒ 00:07:43.960 Awaish Kumar: Goodbye.
88 00:07:44.290 ⇒ 00:07:44.890 Amber Lin: Yeah.
89 00:07:45.370 ⇒ 00:07:46.390 Amber Lin: Awesome.
90 00:07:46.830 ⇒ 00:07:47.840 Amber Lin: Okay.
91 00:07:48.390 ⇒ 00:07:52.910 Amber Lin: … Architecture diagram….
92 00:07:53.380 ⇒ 00:08:04.430 Awaish Kumar: Yeah, so that’s the only… That’s my focus, is for today, like, 669 and 654.
93 00:08:06.360 ⇒ 00:08:06.860 Amber Lin: Okay.
94 00:08:06.860 ⇒ 00:08:14.639 Awaish Kumar: So I will be looking at these two today, and … just for an update for any, like, return on ad spend model.
95 00:08:14.740 ⇒ 00:08:16.960 Awaish Kumar: I have pushed it.
96 00:08:17.280 ⇒ 00:08:20.960 Awaish Kumar: In production, it is, like.
97 00:08:21.230 ⇒ 00:08:27.519 Awaish Kumar: It seems to be correct. I tried everything. All the test tickets were there. I…
98 00:08:27.670 ⇒ 00:08:30.970 Awaish Kumar: Tried to test it out, and it looked fine to me.
99 00:08:31.230 ⇒ 00:08:32.299 Awaish Kumar: Sure, yeah.
100 00:08:32.610 ⇒ 00:08:36.609 Awaish Kumar: If you find something, let me know, but yeah, I think it’s good to go.
101 00:08:37.409 ⇒ 00:08:38.119 Amber Lin: Okay.
102 00:08:39.269 ⇒ 00:08:40.259 Amber Lin: Awesome.
103 00:08:40.499 ⇒ 00:08:41.679 Amber Lin: ….
104 00:08:42.150 ⇒ 00:08:47.190 Henry Zhao: Wait, one second for Awash. Awish, I know you said that’s your priority for today, but can we also make sure we focus on
105 00:08:47.720 ⇒ 00:08:51.410 Henry Zhao: Responding to Polytomic and making sure we finalize the server tracking.
106 00:08:51.780 ⇒ 00:09:05.509 Awaish Kumar: Yeah, but I’m… I… I’m sure… I’m working on that, but for… from our GetResponse from PolyTomic, the requests we send are successfully being sent from API.
107 00:09:05.780 ⇒ 00:09:09.579 Henry Zhao: Yeah, that’s fine. I’m just saying, like, when they respond, like, let’s just prioritize that, because….
108 00:09:10.230 ⇒ 00:09:12.719 Awaish Kumar: Yeah, yeah, that’s what I’m saying, they already replied.
109 00:09:12.940 ⇒ 00:09:16.130 Awaish Kumar: And the errors are from, like.
110 00:09:16.340 ⇒ 00:09:19.659 Awaish Kumar: Two days ago, and right now, we…
111 00:09:19.890 ⇒ 00:09:32.810 Awaish Kumar: don’t… I don’t see any errors. I can push more data, but I need you to figure out, like, if that data looks in… is in North Beam, and it looks good, so I can, like.
112 00:09:33.000 ⇒ 00:09:34.760 Awaish Kumar: Push everything, right?
113 00:09:34.970 ⇒ 00:09:37.510 Awaish Kumar: Right now, I’m just pushing some test orders.
114 00:09:37.750 ⇒ 00:09:41.509 Awaish Kumar: And we need to… I need an answer from you or Andrew.
115 00:09:41.700 ⇒ 00:09:45.689 Awaish Kumar: Confirming that the data is in Laos Beam, and it is correct.
116 00:09:46.400 ⇒ 00:09:52.000 Awaish Kumar: Or if you need more, like, fields or something, so I can update my model.
117 00:09:52.670 ⇒ 00:09:57.500 Henry Zhao: Okay, what do you think, Andrew? Do you think we’re okay to push production, or are we not seeing data in Northbeam yet?
118 00:09:58.110 ⇒ 00:10:05.329 Andrew O’Neil: So, Awash, have you been sending the data to Northbeam’s test API endpoint?
119 00:10:07.140 ⇒ 00:10:10.769 Awaish Kumar: So I tried to send it to both, …
120 00:10:11.190 ⇒ 00:10:20.830 Awaish Kumar: first, I sent it to test, test, API, and I could not see anything, then I sent it to production one as well, and that’s just one order.
121 00:10:21.780 ⇒ 00:10:22.540 Andrew O’Neil: Okay.
122 00:10:22.540 ⇒ 00:10:24.050 Henry Zhao: You only said one.
123 00:10:25.430 ⇒ 00:10:42.389 Andrew O’Neil: to make sure, because, like, the chances are that that order’s also getting captured from other places, can we essentially send a test record that, for an order that doesn’t actually exist, that way we know definitively it came from
124 00:10:42.420 ⇒ 00:10:47.430 Andrew O’Neil: our tests into sending the state into production, that way we can.
125 00:10:47.430 ⇒ 00:10:48.180 Awaish Kumar: What?
126 00:10:48.180 ⇒ 00:10:49.610 Andrew O’Neil: It actually came from us.
127 00:10:50.500 ⇒ 00:10:52.800 Awaish Kumar: Okay, I will do that, I will change…
128 00:10:54.010 ⇒ 00:10:59.909 Awaish Kumar: some order ID, to some, like, artificial ID, and I can catch… Yes. Yeah, okay.
129 00:11:01.160 ⇒ 00:11:05.360 Henry Zhao: Or just some very, like, obviously fake, hoarder ID, so we can… Look for it.
130 00:11:07.170 ⇒ 00:11:07.900 Awaish Kumar: Yeah.
131 00:11:08.880 ⇒ 00:11:09.630 Amber Lin: Okay.
132 00:11:09.790 ⇒ 00:11:19.640 Amber Lin: … This is in PR review. Who would be reviewing and closing the PR?
133 00:11:21.530 ⇒ 00:11:25.610 Demilade Agboola: Okay, I’ll review it. Yeah, I’ll review.
134 00:11:25.610 ⇒ 00:11:26.660 Amber Lin: That’s good.
135 00:11:29.130 ⇒ 00:11:30.309 Awaish Kumar: I’m just doing nothing.
136 00:11:30.900 ⇒ 00:11:31.580 Amber Lin: Alrighty.
137 00:11:32.040 ⇒ 00:11:32.580 Amber Lin: And….
138 00:11:32.580 ⇒ 00:11:33.470 Demilade Agboola: also…
139 00:11:33.640 ⇒ 00:11:44.130 Demilade Agboola: This, yeah, the… the reason why the numbers look different and so different, is that there is a filter where we’re counting the…
140 00:11:44.250 ⇒ 00:11:52.670 Demilade Agboola: filtering… like, counting new customers only from the offer or no. So there’s an extra filter, basically.
141 00:11:52.960 ⇒ 00:11:53.760 Demilade Agboola: that….
142 00:11:53.760 ⇒ 00:11:59.839 Awaish Kumar: Yes. I think Demlade, maybe we can remove it now.
143 00:12:00.330 ⇒ 00:12:07.550 Awaish Kumar: But, just, just an update. So, this was added… when…
144 00:12:08.050 ⇒ 00:12:12.969 Awaish Kumar: They didn’t want it to see any order which is coming from the offer.
145 00:12:13.280 ⇒ 00:12:19.809 Awaish Kumar: So they didn’t want you to count those customers, because they were not paying for the offer, or something like that, the marketing team.
146 00:12:19.910 ⇒ 00:12:26.199 Awaish Kumar: But now, they are paying for the offer, and we can count the customers which are coming from
147 00:12:26.370 ⇒ 00:12:32.199 Awaish Kumar: They offer… like, So, the offer source.
148 00:12:32.440 ⇒ 00:12:37.749 Awaish Kumar: And, the only thing I… if we want… if we want to remove this filter.
149 00:12:38.060 ⇒ 00:12:51.529 Awaish Kumar: We just may have to make sure that it gets removed from all the other models, because the numbers won’t match, again, if we just remove it from one model and don’t remove it from others.
150 00:12:51.930 ⇒ 00:12:55.830 Awaish Kumar: So, because to match the numbers, I added it to multiple
151 00:12:56.000 ⇒ 00:12:59.849 Awaish Kumar: Different models as well, so we need to remove from all of those places.
152 00:13:03.690 ⇒ 00:13:10.440 Demilade Agboola: Okay, alright, so I will remove this from all the other places as well.
153 00:13:11.320 ⇒ 00:13:14.830 Awaish Kumar: We can, like, maybe have a separate ticket for that.
154 00:13:14.830 ⇒ 00:13:15.520 Amber Lin: Okay.
155 00:13:17.010 ⇒ 00:13:17.640 Demilade Agboola: Sure.
156 00:13:18.780 ⇒ 00:13:25.390 Amber Lin: Move… Close this… Create a ticket….
157 00:13:29.270 ⇒ 00:13:34.980 Awaish Kumar: To remove the offer filter from… all the models.
158 00:13:45.060 ⇒ 00:13:45.680 Amber Lin: Okay.
159 00:13:49.710 ⇒ 00:13:51.500 Amber Lin: Alright.
160 00:13:53.120 ⇒ 00:13:54.460 Amber Lin: Cure length…
161 00:13:57.680 ⇒ 00:14:03.379 Amber Lin: Okay, I think we should… I think we should think about the priorities.
162 00:14:03.630 ⇒ 00:14:04.950 Amber Lin: of these.
163 00:14:05.420 ⇒ 00:14:11.820 Amber Lin: So we have 1, 2, 3, 4… Tickets… ….
164 00:14:16.100 ⇒ 00:14:16.850 Demilade Agboola: Hmm.
165 00:14:17.090 ⇒ 00:14:18.470 Amber Lin: How should we….
166 00:14:19.860 ⇒ 00:14:21.020 Awaish Kumar: work from.
167 00:14:21.690 ⇒ 00:14:26.309 Awaish Kumar: If… if there is no… Urgency from client.
168 00:14:26.500 ⇒ 00:14:32.639 Awaish Kumar: Then we should work on those tickets, which can unblock any.
169 00:14:33.150 ⇒ 00:14:41.949 Amber Lin: Yeah, I… I agree. I think… This… Josh’s ticket can be medium.
170 00:14:42.160 ⇒ 00:14:47.139 Amber Lin: We don’t have to do that one first, he’s not asking for it yet. ….
171 00:14:48.130 ⇒ 00:14:55.660 Awaish Kumar: 10, 5, 6… Yeah. That’s… yeah. Yeah, I think… First can be 759, and then 756.
172 00:14:56.270 ⇒ 00:14:57.050 Amber Lin: Okay.
173 00:14:58.500 ⇒ 00:15:02.200 Amber Lin: Sure. Yeah, I’ll change these to high.
174 00:15:02.580 ⇒ 00:15:06.770 Amber Lin: And then… We can… okay, that’s better.
175 00:15:07.590 ⇒ 00:15:11.859 Amber Lin: Alright, Harry, on your side, can I close any of these?
176 00:15:12.540 ⇒ 00:15:19.950 Henry Zhao: … No, I think it’s the same update as yesterday. Trying to finish the fixed treatment check-in today.
177 00:15:21.290 ⇒ 00:15:25.639 Amber Lin: I see, okay. This one, did you check the numbers?
178 00:15:25.640 ⇒ 00:15:28.810 Henry Zhao: Not yet, I’ve just been trying to focus on some….
179 00:15:29.030 ⇒ 00:15:30.279 Amber Lin: I see, okay.
180 00:15:30.760 ⇒ 00:15:35.900 Amber Lin: … I guess….
181 00:15:36.500 ⇒ 00:15:52.100 Henry Zhao: Are these on track to be done this Friday? Like, all of these, or are there any items that will get pushed? Oh, definitely not all of these. I don’t think all of these should be due Friday. I think Friday should just be 731-676-666 and 57.
182 00:15:53.500 ⇒ 00:15:57.000 Amber Lin: 16, 6… 57….
183 00:16:01.560 ⇒ 00:16:03.500 Henry Zhao: Oh, and 622 also, I guessed.
184 00:16:04.020 ⇒ 00:16:08.040 Amber Lin: 6… 2… this week.
185 00:16:08.690 ⇒ 00:16:11.969 Amber Lin: Then, in that case…
186 00:16:12.130 ⇒ 00:16:25.980 Amber Lin: For these tickets, should we… who should take them, or how should we make sure that they’re done? Because I… are they crucial to our process for the.
187 00:16:25.980 ⇒ 00:16:27.990 Henry Zhao: I’ll never think for this week.
188 00:16:27.990 ⇒ 00:16:29.170 Awaish Kumar: What is 713?
189 00:16:31.400 ⇒ 00:16:32.250 Henry Zhao: Scalable.
190 00:16:32.250 ⇒ 00:16:38.780 Amber Lin: It’s the table you wish… the task you asked to be added to North Beam.
191 00:16:39.400 ⇒ 00:16:43.109 Awaish Kumar: Yeah, like, I already have this, so it’s done.
192 00:16:43.110 ⇒ 00:16:44.150 Amber Lin: Oh, okay.
193 00:16:48.400 ⇒ 00:16:51.729 Awaish Kumar: So this is, like, I created this model, so you can assign it to me.
194 00:16:54.390 ⇒ 00:16:57.020 Amber Lin: Sorry, … This one?
195 00:16:59.180 ⇒ 00:17:00.010 Awaish Kumar: Oh, no.
196 00:17:00.260 ⇒ 00:17:04.899 Awaish Kumar: Yeah, like, so identification of fields in the model, and …
197 00:17:05.480 ⇒ 00:17:08.760 Awaish Kumar: So, this is all done, right, basically.
198 00:17:08.780 ⇒ 00:17:11.820 Amber Lin: Now we are only waiting on confirmation.
199 00:17:11.940 ⇒ 00:17:18.490 Awaish Kumar: from that model, the data is in the North Maine, and that’s what I’m also looking at.
200 00:17:18.720 ⇒ 00:17:19.810 Amber Lin: Gotcha.
201 00:17:20.079 ⇒ 00:17:21.929 Amber Lin: Okay, that’s awesome.
202 00:17:22.279 ⇒ 00:17:24.649 Amber Lin: Alright.
203 00:17:25.079 ⇒ 00:17:27.809 Amber Lin: Alright, that looks a lot nicer, and then….
204 00:17:27.969 ⇒ 00:17:30.680 Henry Zhao: That one’s a long term, that should probably be next cycle.
205 00:17:30.680 ⇒ 00:17:31.879 Amber Lin: Yeah, I’ll move it.
206 00:17:32.720 ⇒ 00:17:33.660 Amber Lin: Okay.
207 00:17:34.680 ⇒ 00:17:41.709 Amber Lin: Great. And then, for Vaslov’s tickets, let’s help, prioritize, because there’s quite a few.
208 00:17:45.340 ⇒ 00:17:46.530 Amber Lin: …
209 00:17:48.070 ⇒ 00:17:57.350 Amber Lin: Annie will need some modeling here, but I know, I wish I think you assigned one to the melody and one to….
210 00:17:57.350 ⇒ 00:17:58.300 Awaish Kumar: Dad.
211 00:17:58.460 ⇒ 00:17:59.140 Amber Lin: Not that one.
212 00:17:59.140 ⇒ 00:17:59.930 Awaish Kumar: seems….
213 00:18:01.610 ⇒ 00:18:05.339 Awaish Kumar: Yeah, it seemed like a different modeling request. Yeah.
214 00:18:05.750 ⇒ 00:18:11.200 Awaish Kumar: Yeah, like, I’m, I mean… I think, like, …
215 00:18:12.300 ⇒ 00:18:17.319 Awaish Kumar: these are separate modeling requests and need separate models, that’s why I.
216 00:18:17.330 ⇒ 00:18:19.029 Amber Lin: Oh, that makes sense.
217 00:18:19.450 ⇒ 00:18:27.639 Amber Lin: Okay, … I would say, like, the one for Josh is medium. Catalyst seems pretty…
218 00:18:27.760 ⇒ 00:18:32.010 Amber Lin: important. I know they said it’s a different request, but I do remember….
219 00:18:32.480 ⇒ 00:18:34.429 Awaish Kumar: Say, they have nothing.
220 00:18:34.540 ⇒ 00:18:36.690 Awaish Kumar: in Catalyst yet. There’s no.
221 00:18:36.690 ⇒ 00:18:37.469 Amber Lin: I don’t.
222 00:18:37.850 ⇒ 00:18:39.360 Amber Lin: Oh, I see.
223 00:18:39.360 ⇒ 00:18:40.509 Awaish Kumar: Connect, like, right now.
224 00:18:40.510 ⇒ 00:18:41.030 Amber Lin: Okay.
225 00:18:41.030 ⇒ 00:18:41.900 Awaish Kumar: There’s nothing.
226 00:18:41.900 ⇒ 00:18:44.749 Amber Lin: I, I hear you, okay. So this can be…
227 00:18:45.170 ⇒ 00:18:51.140 Amber Lin: pushed, I’ll say, I’ll push this to… Friday.
228 00:18:52.870 ⇒ 00:18:57.829 Amber Lin: … Is that the same for, like, these?
229 00:18:57.830 ⇒ 00:18:59.320 Awaish Kumar: No, a flirt has the data.
230 00:18:59.530 ⇒ 00:19:02.050 Awaish Kumar: 7… like, all of the tickets are…
231 00:19:02.850 ⇒ 00:19:06.280 Awaish Kumar: Only the catalyst thing is, can we integrate everything else?
232 00:19:06.890 ⇒ 00:19:09.449 Amber Lin: Okay, so I’ll say…
233 00:19:12.950 ⇒ 00:19:19.299 Amber Lin: So between these, I guess we’ll do the finance modeling for Annie first.
234 00:19:19.650 ⇒ 00:19:25.389 Amber Lin: And then we can do… Affluence, and then MN Mountain.
235 00:19:30.310 ⇒ 00:19:32.610 Amber Lin: Wish, do you think this is…
236 00:19:32.840 ⇒ 00:19:38.240 Amber Lin: These are reasonable as for today, or should I say something, one thing.
237 00:19:38.240 ⇒ 00:19:44.149 Awaish Kumar: 747 is… is, … It’s, like, 3-4 hours, and it’s….
238 00:19:44.550 ⇒ 00:19:51.769 Amber Lin: In itself, right? Yeah, if it’s 4 hours, I will say… sorry, how many hours would that be?
239 00:19:52.380 ⇒ 00:19:55.659 Awaish Kumar: Yeah, I’m saying, like, it’s between 3 to 5, because it’s a
240 00:19:56.550 ⇒ 00:19:58.570 Awaish Kumar: This is a big change in a….
241 00:19:59.070 ⇒ 00:20:00.560 Amber Lin: Hmm.
242 00:20:00.560 ⇒ 00:20:02.349 Awaish Kumar: If you can open it, maybe I….
243 00:20:02.350 ⇒ 00:20:02.980 Amber Lin: Yeah.
244 00:20:05.460 ⇒ 00:20:16.030 Awaish Kumar: Yeah, so it’s a… it’s not a big change, but it’s… it’s a… like, a change in a model, which is really important. I want to…
245 00:20:16.530 ⇒ 00:20:24.460 Awaish Kumar: want anyone who is touching this to, like, be extra careful, and validate everything, and then push it. Okay.
246 00:20:24.460 ⇒ 00:20:31.860 Amber Lin: Okay, so Vasla, let’s focus on this one today, and then, let’s try and have…
247 00:20:31.990 ⇒ 00:20:34.460 Amber Lin: work with OH to help validate it.
248 00:20:34.600 ⇒ 00:20:40.659 Amber Lin: And then, I think for Ups Fluence and Mountain, we’ll hold out for tomorrow.
249 00:20:41.340 ⇒ 00:20:49.150 Amber Lin: Thursday… So then we have Thursday, and then we have Catalyst for Friday, if nothing comes up.
250 00:20:49.530 ⇒ 00:20:50.350 Amber Lin: Okay.
251 00:20:52.230 ⇒ 00:20:53.360 Amber Lin: Sounds good.
252 00:20:54.370 ⇒ 00:21:00.979 Annie Yu: I have one question for Awash or Vashdev. Is that return on Aspen fixed?
253 00:21:02.170 ⇒ 00:21:04.870 Awaish Kumar: Yeah, yeah, that’s what I updated in my….
254 00:21:05.790 ⇒ 00:21:06.590 Annie Yu: Oh, okay.
255 00:21:06.590 ⇒ 00:21:12.320 Awaish Kumar: Yeah, when I was talking. So, the model is upgraded. I have tested all the…
256 00:21:12.640 ⇒ 00:21:18.409 Awaish Kumar: Like, the edge cases you provided, and it looked… okay to me, …
257 00:21:18.660 ⇒ 00:21:23.140 Awaish Kumar: But if anything you find out, like, just let me know.
258 00:21:23.270 ⇒ 00:21:26.020 Awaish Kumar: And I will give it a priority today.
259 00:21:27.660 ⇒ 00:21:32.209 Amber Lin: Okay, okay, nice. That one’s excluding, the offer.
260 00:21:32.330 ⇒ 00:21:33.319 Annie Yu: Is that it?
261 00:21:35.320 ⇒ 00:21:45.000 Awaish Kumar: Yeah, right now, it does. Right now, we are excluding the offer from everywhere. But, yeah, after Demolariq.
262 00:21:45.180 ⇒ 00:21:49.409 Awaish Kumar: Removes that filter from all the models, then it will be removed as well.
263 00:21:49.410 ⇒ 00:21:50.320 Amber Lin: No problem.
264 00:21:52.000 ⇒ 00:21:53.680 Awaish Kumar: But do you want it with the.
265 00:21:53.680 ⇒ 00:21:54.120 Demilade Agboola: So….
266 00:21:54.120 ⇒ 00:21:55.850 Awaish Kumar: The offer filter, or no?
267 00:21:56.300 ⇒ 00:22:04.809 Demilade Agboola: So what I’m thinking is, you know, Josh also wants us the ability to add and include the offer. I could instead just make a flag for it.
268 00:22:06.370 ⇒ 00:22:14.599 Demilade Agboola: So, it’s offered true-false, basically. And so that way, you can kind of see the numbers with or without, while still removing it.
269 00:22:15.310 ⇒ 00:22:19.320 Awaish Kumar: Yeah, I… I… yeah, that… That’s a good, …
270 00:22:20.130 ⇒ 00:22:29.569 Awaish Kumar: like, flag to have, but the reason why it was in a… such a way in all those previous models is that they wanted all the data
271 00:22:29.820 ⇒ 00:22:36.530 Awaish Kumar: For all other metrics, but they only wanted new customer count to be filtered out for the offer.
272 00:22:38.650 ⇒ 00:22:41.819 Awaish Kumar: For the customers which are coming from the office, so that’s why…
273 00:22:42.130 ⇒ 00:22:45.730 Awaish Kumar: It was added like that, and we didn’t add any kind of filter thing.
274 00:22:45.850 ⇒ 00:22:50.600 Awaish Kumar: But right now, I’m, like, in product sales summary by transaction.
275 00:22:51.170 ⇒ 00:22:53.720 Awaish Kumar: I don’t think we need a flag in that table.
276 00:23:00.610 ⇒ 00:23:03.499 Awaish Kumar: Is there any requirement for that? Any WRA?
277 00:23:04.280 ⇒ 00:23:18.069 Demilade Agboola: There is a requirement for removing the offer from… well, like, the ability to add or include the offer in certain dashboards, but I’ll need to be sure what data, like, what models that dashboard is using.
278 00:23:20.120 ⇒ 00:23:27.220 Awaish Kumar: Yeah, but if we added in product sales summary, we need to have An extra, like, layer of…
279 00:23:27.480 ⇒ 00:23:32.640 Awaish Kumar: a new dimension there, right? Just for this, so I’m not…
280 00:23:33.080 ⇒ 00:23:36.709 Awaish Kumar: I’m not really sure if it’s needed in that table, but
281 00:23:36.900 ⇒ 00:23:40.950 Awaish Kumar: Yeah, we can add in other… Tables where you need it.
282 00:23:41.060 ⇒ 00:23:47.089 Awaish Kumar: We can have this flag, for example, in channel sales summary, or whatever, like, we can add this flag.
283 00:23:48.160 ⇒ 00:23:48.970 Demilade Agboola: Sure.
284 00:23:53.010 ⇒ 00:23:56.509 Awaish Kumar: So, like, I wanted to understand why Annie wanted
285 00:23:57.540 ⇒ 00:24:01.019 Awaish Kumar: Did you want it to remove it in this?
286 00:24:01.020 ⇒ 00:24:17.149 Annie Yu: I’m just confirming, because I need to do another, like, churn analysis for Mitesh, and I’m thinking if I can just copy… I can just reuse the query for this model, but then remove the… the offer filter.
287 00:24:17.880 ⇒ 00:24:21.380 Awaish Kumar: So right now, it already removed the offer ordered.
288 00:24:21.380 ⇒ 00:24:24.070 Annie Yu: Oh, yeah, yeah, I mean, like, if I can remove that…
289 00:24:24.340 ⇒ 00:24:26.380 Annie Yu: I can, I can include that.
290 00:24:27.410 ⇒ 00:24:34.300 Annie Yu: And just do the query. But I’ll see if I have everything I need within that query.
291 00:24:34.300 ⇒ 00:24:36.580 Amber Lin: Okay, thank you.
292 00:24:37.180 ⇒ 00:24:37.850 Annie Yu: Thanks.
293 00:24:39.380 ⇒ 00:24:40.200 Amber Lin: Alright.
294 00:24:40.650 ⇒ 00:24:41.920 Amber Lin: Thank you, team.
295 00:24:43.840 ⇒ 00:24:44.679 Henry Zhao: Thank you, guys.
296 00:24:44.890 ⇒ 00:24:46.250 Amber Lin: Alright, bye.
297 00:24:46.250 ⇒ 00:24:47.090 Andrew O’Neil: Good, thanks.
298 00:24:47.510 ⇒ 00:24:48.190 Amber Lin: Hello.