Meeting Title: Eden Daily Standup Date: 2026-02-11 Meeting participants: Zoran Selinger, Demilade Agboola, Awaish Kumar, Ashwini Sharma, Amber Lin, Robert Tseng
WEBVTT
1 00:04:49.030 ⇒ 00:04:51.970 Robert Tseng: Hey, everyone, sorry, I closed late.
2 00:04:59.100 ⇒ 00:04:59.910 Robert Tseng: Okay.
3 00:05:01.930 ⇒ 00:05:08.320 Robert Tseng: jump into it. Yeah, so Ron, do you want to go first, since, you weren’t here yesterday?
4 00:05:08.320 ⇒ 00:05:09.600 Zoran Selinger: Yeah, sure, sure.
5 00:05:09.730 ⇒ 00:05:15.169 Zoran Selinger: Yeah, so the focus for this week is definitely activating TikTok.
6 00:05:15.510 ⇒ 00:05:18.740 Zoran Selinger: So I’ve done GTM part already.
7 00:05:18.870 ⇒ 00:05:26.409 Zoran Selinger: That’s ready. We have two more, two more tasks there.
8 00:05:26.650 ⇒ 00:05:29.969 Zoran Selinger: modeling, which I’m going to attempt it
9 00:05:30.150 ⇒ 00:05:36.360 Zoran Selinger: myself, and I’m gonna get… I’m gonna have Avaesh look over it once I’m finished.
10 00:05:36.540 ⇒ 00:05:41.580 Zoran Selinger: Great. Why I’m doing it myself is simple. It’s essentially a copy of…
11 00:05:41.750 ⇒ 00:05:55.249 Zoran Selinger: of the meta model, so I’m just changing a few lines. So that’s the reason. In most cases, I just have to see, I see the hashing for the reverse ETL is very similar.
12 00:05:55.250 ⇒ 00:06:07.130 Zoran Selinger: So I think it’s gonna be basically almost, almost identical. So, I’m just gonna take that off of HSS’ hands and do it myself today, because we wanna be ready, so we kind of set up
13 00:06:07.130 ⇒ 00:06:16.209 Zoran Selinger: 10 a.m. Eastern Time, Friday, as a deadline, so I really want to have that done, and now I’m gonna have to do the reverse ETL piece as well.
14 00:06:16.260 ⇒ 00:06:24.620 Zoran Selinger: I have access to the account and everything I need there, so I think, yeah, we’ll hit the deadline there.
15 00:06:24.810 ⇒ 00:06:26.770 Zoran Selinger: Today, I’m also investigating
16 00:06:27.890 ⇒ 00:06:36.880 Zoran Selinger: some partners on Catalyst, so most partners, it looks normal, but we have some… some that were performing really well before.
17 00:06:36.980 ⇒ 00:06:42.200 Zoran Selinger: And now, even after we fixed it, it looks really bad.
18 00:06:43.600 ⇒ 00:06:50.070 Zoran Selinger: Round found out, that, And that we have…
19 00:06:50.110 ⇒ 00:07:03.229 Zoran Selinger: Like, they have multiple clicks, even more than 10 in some days, from the same URL, and it does not show up for any other partner. So it just looks like a lot of
20 00:07:03.230 ⇒ 00:07:10.659 Zoran Selinger: a lot of bot traffic. And it wasn’t the case, like, a month ago, that that traffic was good… good quality traffic.
21 00:07:10.660 ⇒ 00:07:23.570 Zoran Selinger: Now it looks really bad. So, what I’m doing today, I’m going to just figure out if we see, any pattern in the transactions, in the treatments themselves.
22 00:07:23.650 ⇒ 00:07:32.390 Zoran Selinger: Or the… basically, the treatments and transactions that we got are legitimate. I want to confirm… confirm that.
23 00:07:32.630 ⇒ 00:07:39.720 Zoran Selinger: I suspect the transactions are real, because those are coming from
24 00:07:39.830 ⇒ 00:07:43.070 Zoran Selinger: From real traffic, but there’s a lot of…
25 00:07:43.730 ⇒ 00:07:53.499 Zoran Selinger: bot clicks, seems to me. I mean, it’s… it’s pretty obvious, so we’ll see, we’ll see. GA shows us, instead of
26 00:07:53.700 ⇒ 00:07:57.629 Zoran Selinger: 2,000 clicks, GA shows us only 300.
27 00:07:58.480 ⇒ 00:08:09.050 Zoran Selinger: Mixpanel logs everything, so Mixpanel kind of agrees with Catalyst’s reporting, but the GA shows us, like, 350 legitimate sessions.
28 00:08:09.520 ⇒ 00:08:11.449 Zoran Selinger: from SpotAware. So…
29 00:08:12.660 ⇒ 00:08:24.669 Zoran Selinger: that’s… those are the two… two things, for today. We are making very little progress because we just don’t have time on… on the KPI dash, though.
30 00:08:25.080 ⇒ 00:08:27.660 Zoran Selinger: I don’t know how it’s going on your side.
31 00:08:27.820 ⇒ 00:08:30.119 Zoran Selinger: The channel performance bit.
32 00:08:31.000 ⇒ 00:08:40.899 Robert Tseng: Yeah, I guess I’ll talk about that when I get to my part, but I already… I already kind of built out, like, a good chunk of it, so… I just… we’re just doing some data validation.
33 00:08:40.909 ⇒ 00:08:51.579 Zoran Selinger: Sure. So, actually, yeah, that’s, that’s it, I’m gonna talk… we need to talk a little bit about, like, we’re gonna have a call with Normim next week, and we have our scheduled
34 00:08:51.839 ⇒ 00:09:09.189 Zoran Selinger: drift report anyway for this month, so I’m gonna do it early next week, probably Monday. I’m gonna do an analysis on how much we’re missing on NordBeam compared to the Edge, and yeah, we’ll, I think we’re gonna be fine anyway. Yeah.
35 00:09:10.380 ⇒ 00:09:12.099 Robert Tseng: Okay, did you have the slides?
36 00:09:12.270 ⇒ 00:09:13.130 Zoran Selinger: Sorry?
37 00:09:13.380 ⇒ 00:09:17.230 Robert Tseng: Did you add your slides for this morning? I’m gonna basically do it after this. Okay, great.
38 00:09:17.230 ⇒ 00:09:25.909 Zoran Selinger: Yeah, I did. I even done most of the slides for tomorrow’s weekly check-in, so that’s already ready as well. Okay.
39 00:09:26.070 ⇒ 00:09:33.289 Zoran Selinger: Yeah, the… the other most really important thing for… for,
40 00:09:33.510 ⇒ 00:09:40.619 Zoran Selinger: for Eden on Martech’s side right now, is the… the upfluence automation
41 00:09:40.960 ⇒ 00:09:44.079 Zoran Selinger: We have some data in because I’ve added it manually.
42 00:09:44.830 ⇒ 00:09:50.129 Zoran Selinger: I know Ashwini is, is working, is working on it.
43 00:09:51.020 ⇒ 00:09:55.369 Zoran Selinger: So we currently don’t have anything, basically, for, for…
44 00:09:55.610 ⇒ 00:10:00.220 Zoran Selinger: There’s probably, Ashwini, there’s a ticket for it, right? Already?
45 00:10:00.440 ⇒ 00:10:03.700 Zoran Selinger: I think there’s a ticket for it. There is a ticket for it, there’s a ticket.
46 00:10:04.110 ⇒ 00:10:21.829 Zoran Selinger: So that’s… that’s the only other thing that I’m… I’m waiting, that we’re currently missing. Data from October to end of January, that’s loaded, because I’ve done it manually, and that spreadsheet, looks fine. It was… it was pretty easy to do.
47 00:10:21.830 ⇒ 00:10:23.200 Robert Tseng: When is this going to be done.
48 00:10:25.300 ⇒ 00:10:25.930 Ashwini Sharma: This one?
49 00:10:25.930 ⇒ 00:10:27.129 Robert Tseng: for the cycle? Yeah.
50 00:10:27.130 ⇒ 00:10:28.500 Ashwini Sharma: It’s this week? Yeah, this week.
51 00:10:28.500 ⇒ 00:10:29.599 Robert Tseng: Okay, alright.
52 00:10:29.600 ⇒ 00:10:32.720 Zoran Selinger: Okay. That’s… I think that’s…
53 00:10:36.160 ⇒ 00:10:39.259 Robert Tseng: This one is… have you done this? Have you already done this?
54 00:10:40.420 ⇒ 00:10:43.589 Zoran Selinger: No, nobody, sounds fantasy.
55 00:10:48.720 ⇒ 00:11:01.650 Zoran Selinger: Yeah, sorry, I never looked into this ticket before. This is not the case. Tracking wasn’t failing because of duplicate orders. They did receive, like.
56 00:11:02.540 ⇒ 00:11:18.870 Zoran Selinger: for some orders, they received 500 different pings, like, identical pings, but that did not disrupt the conversion rates at all. The issue was fully, fully on the Google Tag Manager side.
57 00:11:20.280 ⇒ 00:11:20.630 Robert Tseng: Great.
58 00:11:20.630 ⇒ 00:11:37.300 Zoran Selinger: It’s nothing to do with the model. Guys did a good job. That table of pushed orders stopped updating, and that’s the reason why they were sending duplicates, but duplicates did not change how, like, the conversion rates or anything like that, so…
59 00:11:38.630 ⇒ 00:11:39.350 Robert Tseng: Okay.
60 00:11:39.350 ⇒ 00:11:39.910 Zoran Selinger: Yeah.
61 00:11:42.020 ⇒ 00:11:53.489 Robert Tseng: Yeah, other than that, I’m just getting the deck ready, away as we’d already discussed, so I’m, like, we’re just verifying some of the data. I haven’t checked the thread since, any updates there.
62 00:11:57.020 ⇒ 00:12:01.980 Awaish Kumar: No, I… I think, I guess… QA’d that…
63 00:12:04.300 ⇒ 00:12:10.730 Awaish Kumar: like, they spend… like, new customers come for later, and it’s… like, I couldn’t find anything, in…
64 00:12:11.330 ⇒ 00:12:12.960 Awaish Kumar: fake transactions.
65 00:12:13.270 ⇒ 00:12:17.429 Awaish Kumar: For Meta, like, up until March, right?
66 00:12:18.230 ⇒ 00:12:32.719 Robert Tseng: Yeah, I mean, this is basically saying they’ve been… they were spending 2 million and got no one. Like, I don’t know if that’s true, so…
67 00:12:34.290 ⇒ 00:12:39.909 Robert Tseng: I mean, like, we can… If this… if… Like, I…
68 00:12:41.120 ⇒ 00:12:46.060 Robert Tseng: We’re always having to check whether our numbers match up against
69 00:12:46.590 ⇒ 00:12:55.170 Robert Tseng: what their expectations are. So I’m, like, hesitant to push this to ELT, because I feel like they’re gonna see the same thing. They’re being like, I don’t think that’s true, so…
70 00:12:56.610 ⇒ 00:13:00.419 Robert Tseng: Like, if we need to pull in somebody from Nitesh’s team to help.
71 00:13:00.520 ⇒ 00:13:08.369 Robert Tseng: like, QA, like, I mean, I don’t know, I just… I just feel like this is just part of, like, what we have to do when building these models, yeah.
72 00:13:08.870 ⇒ 00:13:17.739 Awaish Kumar: Yeah, I would like to add one thing, like, this data where it is missing, new customer count for Meta is start of the, 2025.
73 00:13:18.030 ⇒ 00:13:28.200 Awaish Kumar: Right? Initial 3 months of 2025, right? So, that is when we were completely dependent on Bask’s data. We didn’t have our own Agile or anything.
74 00:13:29.950 ⇒ 00:13:33.530 Robert Tseng: Okay, yeah, that’s true, that’s before we really took over the modeling, so…
75 00:13:33.530 ⇒ 00:13:33.950 Awaish Kumar: You know what I mean.
76 00:13:33.950 ⇒ 00:13:36.140 Robert Tseng: yeah, I’m gonna share this with them and be like.
77 00:13:36.330 ⇒ 00:13:43.450 Robert Tseng: this is not reliable, but they’re gonna wanna be… I mean, they’re gonna wanna back… backfill, so, like, I don’t know if that’s possible.
78 00:13:43.840 ⇒ 00:13:45.540 Robert Tseng: For, for us, so…
79 00:13:50.090 ⇒ 00:13:51.930 Awaish Kumar: Well, like, we…
80 00:13:52.800 ⇒ 00:14:02.140 Awaish Kumar: didn’t have… we don’t have those, like, touchpoints, right? We have the data, like, we have historical data from Basque, and we can only access what Basque provided us.
81 00:14:02.140 ⇒ 00:14:04.690 Robert Tseng: So this is already historical data from BASC?
82 00:14:05.660 ⇒ 00:14:08.170 Awaish Kumar: Yeah, like, yeah, so this is, like,
83 00:14:09.800 ⇒ 00:14:18.659 Awaish Kumar: Yeah, we are looking at the CAC in 2025, so that means it’s… these are the customers at that moment of time.
84 00:14:25.830 ⇒ 00:14:34.950 Robert Tseng: Okay, I mean… I’m gonna present this today, and I may get just… I mean, I don’t…
85 00:14:35.790 ⇒ 00:14:37.970 Robert Tseng: I know, I don’t feel good about it.
86 00:14:38.420 ⇒ 00:14:40.300 Robert Tseng: I’m gonna get destroyed.
87 00:14:41.100 ⇒ 00:14:49.720 Awaish Kumar: Okay, like, what’s your feedback on the recent data? If that is looking off, then we can actually go in and debug it with our edge layer.
88 00:14:49.920 ⇒ 00:14:51.220 Awaish Kumar: attribution.
89 00:14:52.110 ⇒ 00:14:59.390 Robert Tseng: Yeah, I mean, I was just doing… I was just doing the QA of checking it against Tableau, so I feel like, yeah, you should…
90 00:14:59.510 ⇒ 00:15:02.919 Robert Tseng: I feel like you should, you should, you should do that. When… but when you,
91 00:15:03.280 ⇒ 00:15:08.390 Robert Tseng: I know you’re just… you just dump the raw data in here, and then, like, have me build it out, but…
92 00:15:08.630 ⇒ 00:15:18.670 Robert Tseng: And I will do the checks, but I mean, I feel like everybody that’s touching data needs to have some degree of, like, QAing your own work, so…
93 00:15:19.190 ⇒ 00:15:20.150 Awaish Kumar: No.
94 00:15:20.480 ⇒ 00:15:20.990 Awaish Kumar: So, like.
95 00:15:20.990 ⇒ 00:15:23.690 Robert Tseng: Yeah, Mike, if you want to just test, like, this… yeah.
96 00:15:24.400 ⇒ 00:15:30.350 Awaish Kumar: Yeah, I have QA’d it with, like, the base models. Like, we have, we have, like, order, mask order completed.
97 00:15:30.590 ⇒ 00:15:37.079 Awaish Kumar: So, using that, I have… IQA’d and, like, the numbers… Like, do match.
98 00:15:37.340 ⇒ 00:15:50.139 Awaish Kumar: this… yeah, it is missing some offline spend data, but apart from that, like, the revenue and stuff like that exactly match what we are getting from Basque.
99 00:15:52.330 ⇒ 00:16:09.769 Robert Tseng: Okay. Well, I mean, like, the meta numbers make sense. These are pretty much, like, they didn’t spend meta in the past 3 months, like, I’m not, like… this doesn’t surprise me that it’s, like, close to zero towards the end. They were spending a lot of meta for these 6 months, that makes sense, but yeah, I mean, obviously, not having anything January, February is kind of weird.
100 00:16:10.190 ⇒ 00:16:18.310 Robert Tseng: Okay, it’s fine. Yeah, so, I mean, the next part of this is, like, can we do… can we do this? I mean…
101 00:16:18.770 ⇒ 00:16:26.909 Robert Tseng: We have… I guess this is just, like, the same numbers of new customer revenue accounts that I can pull into here. Spend is…
102 00:16:27.100 ⇒ 00:16:27.639 Awaish Kumar: in the same.
103 00:16:27.640 ⇒ 00:16:28.020 Robert Tseng: And then…
104 00:16:28.020 ⇒ 00:16:30.650 Awaish Kumar: Same model, we also have a product name.
105 00:16:30.650 ⇒ 00:16:31.330 Robert Tseng: Good Friday.
106 00:16:31.330 ⇒ 00:16:38.629 Awaish Kumar: Basically, you can do that on the product name level, like, you can filter on Google and product, but yeah, that’s the only…
107 00:16:38.810 ⇒ 00:16:40.770 Awaish Kumar: Granularity we can do right now.
108 00:16:41.030 ⇒ 00:16:49.909 Robert Tseng: Okay, all right, well then, I think this is… it is… that’s ready. So, yeah, that’s kind of where, Amber, I’d probably have you kind of jump in and finish this out, so…
109 00:16:50.160 ⇒ 00:16:52.529 Amber Lin: You pretty much just need to…
110 00:16:52.780 ⇒ 00:16:57.380 Robert Tseng: like, kind of get the spend. Anyway, you know, it’s… it’s the same… same formulas here.
111 00:16:58.080 ⇒ 00:16:59.019 Amber Lin: Oh, sounds good.
112 00:16:59.020 ⇒ 00:17:03.509 Robert Tseng: treatment, we’ll use… we’ll use product, so you can… we can just… you can just swap that out.
113 00:17:04.819 ⇒ 00:17:12.240 Robert Tseng: I’ve been, obviously, I think on affiliates, I don’t think you can do that, and for lifecycle, there isn’t really something here, so…
114 00:17:13.740 ⇒ 00:17:24.520 Robert Tseng: We’ll probably touch that in this next… I’m… I guess… we’ll… we… I can… can…
115 00:17:24.760 ⇒ 00:17:29.700 Robert Tseng: We basically just said we can’t do these three sections in the first
116 00:17:29.920 ⇒ 00:17:40.960 Robert Tseng: pass, but, like, I guess, which the next thing’s gonna… they want these, so can we do anything by affiliate? Can we do anything by… at the life… for lifecycle? Like, I need you to kind of…
117 00:17:40.960 ⇒ 00:17:41.320 Awaish Kumar: Okay.
118 00:17:41.320 ⇒ 00:17:41.820 Robert Tseng: to this snap.
119 00:17:41.820 ⇒ 00:17:42.320 Awaish Kumar: Tim…
120 00:17:42.320 ⇒ 00:17:42.910 Robert Tseng: Yeah.
121 00:17:43.630 ⇒ 00:17:50.460 Awaish Kumar: Yeah, I can easily add the offline spend data, which includes the affiliate data, like, from
122 00:17:50.690 ⇒ 00:18:00.179 Awaish Kumar: the offers of… and the influencer uploads. So, basically, using that, we can create this affiliate section. But, if I get…
123 00:18:00.320 ⇒ 00:18:04.770 Awaish Kumar: definitions for these lifecycle metrics, then, like, I can…
124 00:18:06.890 ⇒ 00:18:10.320 Awaish Kumar: See, like, when you say incremental.
125 00:18:10.600 ⇒ 00:18:13.370 Awaish Kumar: revenue? Like, you… what do you exactly want to…
126 00:18:14.120 ⇒ 00:18:18.220 Awaish Kumar: See, like, just the cumulative run of every week, or, like…
127 00:18:19.800 ⇒ 00:18:27.180 Robert Tseng: Yeah, it’s cumulative in the sense that… or it’s incremental, In that,
128 00:18:29.330 ⇒ 00:18:34.570 Robert Tseng: Well, this is tied to win-backs, and, like, for lifecycle campaigns.
129 00:18:34.890 ⇒ 00:18:45.919 Robert Tseng: when you work… if somebody is on a… like, they’re not an existing customer, they’re, like, net new customers, net new revenue that’s coming from, like, Flyco campaigns. That’s what this is.
130 00:18:50.400 ⇒ 00:18:54.469 Awaish Kumar: Okay, so it’s new customer count for lifecycle campaigns.
131 00:18:55.050 ⇒ 00:18:55.380 Robert Tseng: Yeah.
132 00:18:55.380 ⇒ 00:19:00.790 Awaish Kumar: Or no, new, new customer… Revenue from new customers for lifecycle cameras, okay.
133 00:19:01.210 ⇒ 00:19:04.540 Robert Tseng: Yeah, and then, like, 30-day retention, like…
134 00:19:04.780 ⇒ 00:19:08.079 Robert Tseng: whatever we have on Tableau with retention.
135 00:19:08.300 ⇒ 00:19:14.549 Robert Tseng: it… I think they’re just wanting to understand, like, what percentage of…
136 00:19:15.490 ⇒ 00:19:19.440 Robert Tseng: This is a little bit more complicated, I’m gonna have to break this out more.
137 00:19:21.140 ⇒ 00:19:22.600 Robert Tseng: I guess it’s like…
138 00:19:23.850 ⇒ 00:19:30.779 Robert Tseng: Okay, I’ll probably add a couple more lines here to make that a bit clearer, so I’ll take that after this. I’ll do that first after this call.
139 00:19:31.510 ⇒ 00:19:42.130 Robert Tseng: But yeah, it’s gonna be some, like, split of retention cohorting, so this might be a separate model, and that skews… okay. Yeah, I just… I’ll report another, like, Zoom, like.
140 00:19:42.470 ⇒ 00:19:47.989 Robert Tseng: clip, and I need to just… I might… I might just update this section real quick afterwards.
141 00:19:48.820 ⇒ 00:19:49.550 Awaish Kumar: Okay.
142 00:19:50.260 ⇒ 00:19:50.900 Robert Tseng: Okay.
143 00:19:53.200 ⇒ 00:19:58.679 Robert Tseng: Yeah, and then these, I will probably just tell them that they can give these to their agencies now.
144 00:19:59.410 ⇒ 00:20:03.970 Robert Tseng: Yeah, okay, so this is pretty much the main thing I’m gonna present to them, and then the slides.
145 00:20:05.560 ⇒ 00:20:10.660 Robert Tseng: Cool. Okay, I think that’s all I got.
146 00:20:12.550 ⇒ 00:20:13.730 Robert Tseng: Anything else?
147 00:20:21.080 ⇒ 00:20:24.420 Robert Tseng: Okay, if not, then I think we should be good.
148 00:20:24.620 ⇒ 00:20:25.320 Zoran Selinger: Yep.
149 00:20:25.440 ⇒ 00:20:26.460 Robert Tseng: Thanks, everyone.