Meeting Title: Eden Project Data Integration Sync Date: 2025-11-18 Meeting participants: Awaish Kumar, Henry Zhao
WEBVTT
1 00:01:46.280 ⇒ 00:01:47.789 Henry Zhao: Hi, Awash, how are you doing?
2 00:01:48.940 ⇒ 00:01:51.060 Awaish Kumar: Hello, I’m good, how about you?
3 00:01:51.460 ⇒ 00:01:54.449 Henry Zhao: Good, thank you. Thanks for helping us run that, interview.
4 00:01:55.880 ⇒ 00:01:56.840 Awaish Kumar: By no worries.
5 00:01:57.670 ⇒ 00:01:59.620 Henry Zhao: Alright, let’s see, where’s the stuff?
6 00:02:00.100 ⇒ 00:02:12.580 Henry Zhao: Hey, so I added you to this call because I need your help for some things with Eden, and feel free to send these to Demilade if you don’t want to do it, or if you think he’s better for it. I just don’t know who to assign it to, and I think Demolade’s out today, right?
7 00:02:15.740 ⇒ 00:02:32.490 Henry Zhao: So the first thing is, as you know, the product ROASDash right now is getting ad spends from different platforms, right? So we have the Klaviyo spend… not Klaviyo, MetaSpend, Catalyst Spend, blah blah blah, all that stuff, but a lot of these come from Northbeam, right? So, Zuran is about to cancel Northbeam.
8 00:02:33.130 ⇒ 00:02:44.459 Henry Zhao: And we need to know if we can get these ad spends outside of Northbeam. So, like, probably from the platforms as APIs, and how long this would take, so he knows when we are able to cancel Northbeam and start setting up Wicked Reports.
9 00:02:45.120 ⇒ 00:02:48.550 Awaish Kumar: Yeah, we have to do in parallel, we can’t cancel it.
10 00:02:50.720 ⇒ 00:02:52.360 Henry Zhao: Yeah, when you do it parallel.
11 00:02:52.360 ⇒ 00:03:04.850 Awaish Kumar: You need to know, like, until and unless, Wikidreport is set up, like, we can’t, like, turn it off, because the reason is, it will take me
12 00:03:04.960 ⇒ 00:03:21.110 Awaish Kumar: a few weeks to build these things from scratch on somewhere else. So, in those few weeks, 4 weeks, you can actually have Wicked Report already on, so there’s no point in basically creating all those connections separately.
13 00:03:21.110 ⇒ 00:03:31.320 Awaish Kumar: It’s not just creating connection. Creating connection will just take a few, maybe an hour, or two, or whatever, but then it needs to be modeled, it needs to come out to the…
14 00:03:31.810 ⇒ 00:03:47.309 Awaish Kumar: from the raw, it goes to modeling. Modeling, how it works, we need to verify the numbers, validate it, and all of these, blah, blah, blah. And we have to do it again when we have fixed reports. It’s just, like, we’ll just increase more…
15 00:03:47.600 ⇒ 00:03:48.450 Awaish Kumar: Product.
16 00:03:49.370 ⇒ 00:03:58.809 Henry Zhao: Okay, if they don’t want to add another month of Northbeam, because that’ll be, like, $5,000, could we turn it off and just get the data manually during the month while you’re working?
17 00:03:59.370 ⇒ 00:04:08.260 Awaish Kumar: But it will be the same for us, like, I… I’m… I… like, we don’t have any connectors, right? We are going to use polyatomic then. Polyatomic will also
18 00:04:08.590 ⇒ 00:04:14.960 Awaish Kumar: tag them, right? For each factor. But not for Facebook, for Google, for TikTok, for each of them.
19 00:04:15.070 ⇒ 00:04:29.660 Awaish Kumar: the polyatomic will charge them, and then the extra hours we are going to put into modeling it all, and then… then we know that it needs to be turned off. I’m not sure why we want to put so much effort in…
20 00:04:29.780 ⇒ 00:04:32.470 Awaish Kumar: Something which we know we are… we are just going to…
21 00:04:33.540 ⇒ 00:04:37.009 Awaish Kumar: Like, just going to get a bit, yeah.
22 00:04:37.520 ⇒ 00:04:42.590 Henry Zhao: So you’re saying we just implement this in Wicked Reports, if that’s possible, and then turn it off after it’s implemented in Wicked Reports?
23 00:04:42.590 ⇒ 00:04:49.470 Awaish Kumar: Yeah, yeah, that’s what I’m, like, saying. Like, at the… at the end, we wanna get this, all of this data from WickedReport, right?
24 00:04:49.890 ⇒ 00:04:50.530 Henry Zhao: Yeah.
25 00:04:50.780 ⇒ 00:04:52.089 Awaish Kumar: That’s what I’m saying.
26 00:04:52.090 ⇒ 00:04:52.630 Henry Zhao: Yes.
27 00:04:52.740 ⇒ 00:04:54.559 Awaish Kumar: And we asked Geron about that, yeah.
28 00:04:55.020 ⇒ 00:05:11.380 Awaish Kumar: So when we, know that in 4 weeks, we have a tool ready, from when we are going to get this data, so I’m not sure why we can put, like, 40 or 50, 60 hours of efforts in something which is going to get
29 00:05:11.790 ⇒ 00:05:13.540 Awaish Kumar: Turned off in 4 weeks.
30 00:05:15.090 ⇒ 00:05:17.829 Henry Zhao: Okay, so let me ask him, will we be ready at Sven from Wicked Reports?
31 00:05:17.970 ⇒ 00:05:21.189 Henry Zhao: Same way we do for North Beam.
32 00:05:22.040 ⇒ 00:05:23.859 Henry Zhao: from… from North Beam.
33 00:05:24.960 ⇒ 00:05:26.000 Henry Zhao: Indeed.
34 00:05:26.110 ⇒ 00:05:34.280 Henry Zhao: our dashboards… If so… Yeah, let’s… let me just start asking that. Okay.
35 00:05:34.920 ⇒ 00:05:37.330 Henry Zhao: So, hold off on this, let’s,
36 00:05:40.560 ⇒ 00:05:42.099 Henry Zhao: BigQuery data modeling.
37 00:05:43.650 ⇒ 00:05:46.889 Awaish Kumar: Yeah, I can work on this this week.
38 00:05:47.360 ⇒ 00:05:48.419 Awaish Kumar: Alright. Okay.
39 00:05:49.920 ⇒ 00:05:50.740 Awaish Kumar: Hmm…
40 00:05:51.520 ⇒ 00:06:01.180 Henry Zhao: The next thing is, if you can reach out to Stuart, he wants to split the spend by product for Samoralin a different way. So, right now, in Product ROAS NL2B…
41 00:06:01.180 ⇒ 00:06:04.509 Awaish Kumar: We have a product mapping sheet.
42 00:06:04.760 ⇒ 00:06:05.390 Awaish Kumar: Right.
43 00:06:05.390 ⇒ 00:06:07.439 Henry Zhao: He wants to do it a different way, so…
44 00:06:07.440 ⇒ 00:06:08.820 Awaish Kumar: That’s what I’m saying.
45 00:06:09.230 ⇒ 00:06:18.060 Awaish Kumar: We have a sheet, like, ask him for that mapping that he wants to set up, and we can put that… put that in this sheet.
46 00:06:18.260 ⇒ 00:06:22.569 Awaish Kumar: And then, we can adopt it. So that’s the process.
47 00:06:22.910 ⇒ 00:06:26.459 Henry Zhao: Can I have you talk to him about this? Then you can just deal with him directly?
48 00:06:28.080 ⇒ 00:06:33.220 Henry Zhao: that I don’t really know… Yeah, he just wants this spend broken out by ad group.
49 00:06:33.580 ⇒ 00:06:35.500 Henry Zhao: Not ad group… right now, it’s ad group.
50 00:06:35.600 ⇒ 00:06:41.589 Henry Zhao: But he wants to break it down a different way. Right now, I think it’s broken down in the DBT by, order volume, right?
51 00:06:42.260 ⇒ 00:06:43.380 Henry Zhao: proportionally.
52 00:06:43.380 ⇒ 00:06:50.010 Awaish Kumar: No, no, we have… like, right now, for Sermaline, like, for each product, we get the…
53 00:06:50.240 ⇒ 00:07:08.689 Awaish Kumar: ad spend, right? We have the… the… each of these have either campaign name or ad name, where it says the product name, basically. Okay. So you identify if it is thermaline ODT or Cermoline injection. So, based on that name, we classify into one of those.
54 00:07:10.190 ⇒ 00:07:13.070 Henry Zhao: Let me check with him, and then if it’s so good, I can just close this, right?
55 00:07:13.420 ⇒ 00:07:14.400 Awaish Kumar: Yeah.
56 00:07:14.400 ⇒ 00:07:15.689 Henry Zhao: Okay, got it. Thank you.
57 00:07:18.150 ⇒ 00:07:20.209 Henry Zhao: Was that it? Let me check something. Alright.
58 00:07:20.660 ⇒ 00:07:21.690 Henry Zhao: That might have been it.
59 00:07:25.790 ⇒ 00:07:28.439 Henry Zhao: I think that’s it. On this one, he asked us…
60 00:07:28.710 ⇒ 00:07:31.889 Henry Zhao: Can we check if this conversion is legitimate? Alright, let’s take a…
61 00:07:31.890 ⇒ 00:07:37.940 Awaish Kumar: I think that’s very easy, yeah, you can just… Login into the…
62 00:07:41.700 ⇒ 00:07:42.180 Henry Zhao: back here, right?
63 00:07:43.960 ⇒ 00:07:47.749 Awaish Kumar: Yeah, you can check it here, but you can also check in PASC.
64 00:07:48.970 ⇒ 00:07:50.200 Henry Zhao: Oh, in Basque? Okay.
65 00:07:50.410 ⇒ 00:07:54.269 Awaish Kumar: We can search for this transaction ID, and maybe it will pop up.
66 00:07:54.980 ⇒ 00:08:01.500 Awaish Kumar: the order. Otherwise, you can obviously look at fact transaction. If it shows up there, that means it’s valid.
67 00:08:02.340 ⇒ 00:08:03.220 Awaish Kumar: Sounds like…
68 00:08:03.490 ⇒ 00:08:04.669 Henry Zhao: It’s not showing up there.
69 00:08:05.140 ⇒ 00:08:06.239 Henry Zhao: So it’s not valid?
70 00:08:06.650 ⇒ 00:08:07.310 Henry Zhao: Oh.
71 00:08:07.600 ⇒ 00:08:08.610 Awaish Kumar: Oh, yeah.
72 00:08:16.430 ⇒ 00:08:17.719 Henry Zhao: Nope, not showing up.
73 00:08:17.970 ⇒ 00:08:24.469 Awaish Kumar: Yeah, that could be the reason. I already shared one of this scenario. Yeah, can you go back to Victory?
74 00:08:25.360 ⇒ 00:08:29.769 Awaish Kumar: And, say, fact transaction with all orders.
75 00:08:30.170 ⇒ 00:08:30.880 Henry Zhao: Yeah.
76 00:08:35.409 ⇒ 00:08:41.369 Awaish Kumar: Yeah, so this is… the status was maybe moved, canceled, or something. That’s why.
77 00:08:41.370 ⇒ 00:08:42.840 Henry Zhao: Okay, canceled. Alright.
78 00:08:43.429 ⇒ 00:08:43.999 Awaish Kumar: Okay.
79 00:08:45.170 ⇒ 00:08:46.720 Henry Zhao: Let’s say this was a canceled order, right?
80 00:08:46.990 ⇒ 00:08:47.660 Awaish Kumar: Yeah.
81 00:08:48.270 ⇒ 00:08:58.460 Henry Zhao: Yeah, but we do want these orders in the attribution table, because we want to later analyze, like, is there a higher percentage of canceled orders from a specific UTM? So that’s why I use the raw tables.
82 00:09:00.680 ⇒ 00:09:05.580 Awaish Kumar: Yeah, but we, like, we don’t need, raw tables even for that, for…
83 00:09:05.710 ⇒ 00:09:11.370 Awaish Kumar: affect transactions, you have the UTM, parameters.
84 00:09:11.590 ⇒ 00:09:12.879 Awaish Kumar: In the factology.
85 00:09:15.010 ⇒ 00:09:16.509 Henry Zhao: Yeah, yeah, but this is from Basque.
86 00:09:16.770 ⇒ 00:09:17.780 Henry Zhao: So…
87 00:09:18.340 ⇒ 00:09:18.860 Awaish Kumar: So what?
88 00:09:18.860 ⇒ 00:09:20.089 Henry Zhao: What I’m doing is the edge layer data.
89 00:09:20.090 ⇒ 00:09:32.119 Awaish Kumar: Yeah, that’s what I wanted to say. You can join your Agile data with these fact transaction tables. You don’t need to go into the order completed or order updated tables.
90 00:09:32.350 ⇒ 00:09:39.750 Awaish Kumar: Because, like this, like, cases like this, the order is canceled, like, in the fact transaction, there’s no entry for this data.
91 00:09:39.990 ⇒ 00:09:43.309 Awaish Kumar: And you don’t have to take care of these edge cases
92 00:09:44.230 ⇒ 00:09:47.770 Awaish Kumar: If you just use fake transaction. Otherwise, you have to…
93 00:09:47.770 ⇒ 00:09:49.820 Henry Zhao: I need those edge cases, though.
94 00:09:50.100 ⇒ 00:10:08.549 Awaish Kumar: Yeah, yeah, but it, like, depends what you need, but, like, we have fact knowledge with all orders, if you need all the orders. So we have worked on, like, there are duplicate things, in fact, order updated, in order completed also. There are duplicate entries of the same order, so… and it just,
95 00:10:08.750 ⇒ 00:10:12.280 Awaish Kumar: Like, you are now… you are going to access the full table.
96 00:10:12.450 ⇒ 00:10:18.669 Awaish Kumar: In the order updated, which is raw table, and that’s, like, costly to access in BigQuery as well. So, yeah.
97 00:10:18.670 ⇒ 00:10:26.529 Henry Zhao: But the downside of that is that if I use the dbt tables, if one of those breaks, or one of those is wrong, it’s gonna have bad downstream effects.
98 00:10:27.710 ⇒ 00:10:28.619 Awaish Kumar: Sorry?
99 00:10:30.280 ⇒ 00:10:36.380 Henry Zhao: If I use one of the, like, model tables, if something breaks or something ends up being wrong, then it’s gonna break these tables also.
100 00:10:36.480 ⇒ 00:10:39.569 Henry Zhao: I feel it’s just safer to use the raw tables.
101 00:10:39.570 ⇒ 00:10:46.020 Awaish Kumar: Yeah, like, in my experience, that’s not the best case scenario, but…
102 00:10:46.150 ⇒ 00:10:50.610 Awaish Kumar: If you say so, because I have been using that, like,
103 00:10:50.820 ⇒ 00:10:55.989 Awaish Kumar: like, there are a lot of things. If you read the code, like, if you read
104 00:10:56.100 ⇒ 00:11:01.149 Awaish Kumar: affect transaction, if you read Production Summary, there are a lot of things going
105 00:11:01.380 ⇒ 00:11:14.730 Awaish Kumar: into these SQL we wrote that I can’t even remember all these edge cases if I have to do it again. So, that’s what I’m saying, that if you’re going directly into RAW, you miss a few things.
106 00:11:19.450 ⇒ 00:11:25.050 Henry Zhao: I don’t think so, though, because what we’re doing is completely separate.
107 00:11:26.980 ⇒ 00:11:29.779 Awaish Kumar: Yeah, okay, yeah, that’s okay.
108 00:11:30.180 ⇒ 00:11:45.179 Henry Zhao: We’re literally just connecting Zoran’s Webflow data just based on the transaction ID. Like, that’s basically it. So, I don’t want to filter by anything or change anything. So I’m not… I don’t want any of the fact transactions logic. If I do want it, I do a join with this table to fact transactions, and that actually will be coming up next.
109 00:11:46.460 ⇒ 00:11:50.139 Henry Zhao: Because what I need to do is verify transactions UTMs to the edge layer UTMs.
110 00:11:51.630 ⇒ 00:11:55.969 Awaish Kumar: But that’s… that’s the point. You can correctly draw an edge clear data.
111 00:11:55.970 ⇒ 00:11:59.349 Henry Zhao: No, but then I can’t… then I’m comparing Apple… then I’m comparing…
112 00:11:59.550 ⇒ 00:12:07.540 Henry Zhao: like, something that’s already done to something that’s already done. Like, I need to compare something that’s newly created to something that’s already done to look at the differences.
113 00:12:10.550 ⇒ 00:12:14.380 Awaish Kumar: Like, the newly created data is edge layer data, right?
114 00:12:14.380 ⇒ 00:12:33.579 Henry Zhao: Exactly, right. So I’m comparing it just to… connecting it just to the BASC order completed. I’m looking at the whole history based on anonymous ID. Fact transaction is different. It uses BASC data, it has all these filters and all these manipulations. Later, we need to compare these two tables and say, out of the transaction IDs for November, now that we’ve implemented both, what are the differences?
115 00:12:34.650 ⇒ 00:12:45.380 Henry Zhao: And then either something might be wrong with fan transactions, or we might be getting additional data from EdgeLayer. That’s what Eden wants to know. It’s like, did we improve our UTM tracking by implementing EdgeLayer versus relying on the BASC data?
116 00:12:45.640 ⇒ 00:13:04.009 Awaish Kumar: Because right now, that… the bath data is not good enough for them. The UTMs don’t look right. They’re not able to… Yeah, that you can even get it, yeah. You… that’s not the… like, order completed and the fact000, basically the same data. It’s just more normalized, you can say, but it’s the same source, basically.
117 00:13:04.740 ⇒ 00:13:05.320 Henry Zhao: Yeah.
118 00:13:10.170 ⇒ 00:13:16.049 Henry Zhao: Okay, so… so that’s my explanation. Those are the only things I need help with. I will get back to you on the Stuart thing,
119 00:13:16.440 ⇒ 00:13:19.480 Henry Zhao: But, let me know if you have any… anything else you want to talk about.
120 00:13:20.820 ⇒ 00:13:23.759 Awaish Kumar: Yeah, no, no, I don’t have anything else right now.
121 00:13:23.760 ⇒ 00:13:25.470 Henry Zhao: I’ll be out Thursday and Friday, so…
122 00:13:25.940 ⇒ 00:13:26.600 Awaish Kumar: Okay.
123 00:13:26.790 ⇒ 00:13:28.369 Henry Zhao: Alright, we’ll be more things coming your way.
124 00:13:29.830 ⇒ 00:13:38.279 Awaish Kumar: I may be working on, yeah, I don’t know, these new requests coming from Rayon. He wants us to now, again, update the logic for Catalyst.
125 00:13:41.780 ⇒ 00:13:45.330 Awaish Kumar: Then this ticket, which you showed for…
126 00:13:45.870 ⇒ 00:13:51.389 Awaish Kumar: Gabby, so yeah, I will try to… Prioritize those this week.
127 00:13:51.390 ⇒ 00:13:51.950 Henry Zhao: Yup.
128 00:13:52.730 ⇒ 00:13:55.259 Henry Zhao: Okay, sounds good. Alright, thanks, Alish.