Meeting Title: Magic Spoon — Brainforge sync Date: 2026-01-30 Meeting participants: Demilade Agboola, Justin Tabarini, Michael Thorson, Uttam Kumaran, Ashwini Sharma, Mary Burke
WEBVTT
1 00:00:14.560 ⇒ 00:00:15.719 Demilade Agboola: Hey, how you doing?
2 00:00:17.310 ⇒ 00:00:18.300 Justin Tabarini: Good, good.
3 00:00:18.930 ⇒ 00:00:19.630 Justin Tabarini: How about you?
4 00:00:19.630 ⇒ 00:00:20.400 Demilade Agboola: Nice.
5 00:00:20.690 ⇒ 00:00:22.400 Demilade Agboola: I’m doing very well, pretty good.
6 00:00:24.150 ⇒ 00:00:25.600 Michael Thorson: Mornin’.
7 00:00:26.870 ⇒ 00:00:28.040 Demilade Agboola: Morning.
8 00:00:28.320 ⇒ 00:00:29.290 Uttam Kumaran: Hey, good morning.
9 00:00:31.710 ⇒ 00:00:32.350 Justin Tabarini: Morning, Mon.
10 00:00:32.350 ⇒ 00:00:32.990 Mary Burke: Hey guys!
11 00:00:37.620 ⇒ 00:00:39.779 Demilade Agboola: How’s everyone doing? Hope we’re well.
12 00:00:44.350 ⇒ 00:00:45.900 Michael Thorson: Yeah, pretty solid.
13 00:00:46.620 ⇒ 00:00:47.670 Demilade Agboola: Let’s good, though.
14 00:00:48.060 ⇒ 00:00:49.510 Michael Thorson: Rigid in New York still.
15 00:00:51.220 ⇒ 00:00:56.690 Uttam Kumaran: Yeah, it’s… it’s, it’s, like, 30s here in Austin, which is, like, brutal.
16 00:00:56.690 ⇒ 00:00:57.440 Mary Burke: Excuse me.
17 00:00:57.440 ⇒ 00:00:57.980 Michael Thorson: Wow.
18 00:00:58.710 ⇒ 00:01:00.820 Uttam Kumaran: Yeah, sounds balmy, yeah.
19 00:01:05.209 ⇒ 00:01:06.329 Demilade Agboola: Okay.
20 00:01:07.379 ⇒ 00:01:12.449 Demilade Agboola: Alright, so let me share the screen, and we can kind of go through today’s,
21 00:01:13.819 ⇒ 00:01:16.749 Demilade Agboola: Presentation and review of the week so far.
22 00:01:19.529 ⇒ 00:01:24.579 Demilade Agboola: Again, just, like, the final… Project deliverables are these.
23 00:01:25.269 ⇒ 00:01:34.149 Demilade Agboola: And I think the two things to talk about today are the Spins API, how far we’ve come, as well as the MMM Mart, and how far we’ve come.
24 00:01:35.659 ⇒ 00:01:37.149 Demilade Agboola: So, in terms of…
25 00:01:37.329 ⇒ 00:01:45.119 Demilade Agboola: the Spins API update, I’ll just give a high-level overview, and then Otan will go deeper into, like, what’s… what the details of that as well.
26 00:01:45.229 ⇒ 00:01:55.799 Demilade Agboola: But overall, we’re just, like, in the final stages of QA, and I know that we’re trying to, get the data across so that you can verify on your end, and just be sure that everything matches as it should.
27 00:01:55.999 ⇒ 00:02:00.659 Demilade Agboola: So with that being said, I think I’ll just hand over to Utam, and then he will kind of just walk through…
28 00:02:00.819 ⇒ 00:02:04.009 Demilade Agboola: Some of the documentation on the work we’ve done on that.
29 00:02:05.280 ⇒ 00:02:09.560 Uttam Kumaran: Yeah, so a couple of pieces, we… Talked about…
30 00:02:09.729 ⇒ 00:02:17.800 Uttam Kumaran: on Wednesday is… we were able to put together those, like, 3 aggregates based on the filters that we got.
31 00:02:18.020 ⇒ 00:02:27.590 Uttam Kumaran: And we’re still dealing with some API-related issues, kind of on the format of which we’re bringing that in. So I can… first thing is.
32 00:02:27.590 ⇒ 00:02:40.939 Uttam Kumaran: I think one… I think, Michael, I believe we’re still waiting on the data set to do, sort of, the comparison QA, but I also remember we mentioned, like, I think it was Heather on the Magic Spoon team was able to maybe do some QA as well, so I feel like
33 00:02:41.670 ⇒ 00:02:53.560 Uttam Kumaran: those are potentially possible, but we also did a little bit of a write-up that I’m gonna send over to the Spins team on some of the issues we’re seeing, and kind of, like, actually really specifically,
34 00:02:53.880 ⇒ 00:02:59.550 Uttam Kumaran: Like, in the shape of which we’re making calls and, like, where we need help.
35 00:02:59.680 ⇒ 00:03:08.379 Uttam Kumaran: So, maybe I’ll just walk through that first, and then we can sort of have a chat. So yeah, I mean, we’re… basically, we’re… we’re able to
36 00:03:08.660 ⇒ 00:03:22.679 Uttam Kumaran: pull at all these different time grains, but we’re seeing that, there is, differences in the amount of records that we’re getting at the, weekly level, then,
37 00:03:22.680 ⇒ 00:03:31.089 Uttam Kumaran: Across the 4-week level, there’s some, like, aggregating some of these extracts from the weekly to the 4-week do not match.
38 00:03:31.420 ⇒ 00:03:35.800 Uttam Kumaran: And, like, some certain calculated fields, because
39 00:03:36.550 ⇒ 00:03:44.189 Uttam Kumaran: we can’t, like, sum TDP or ACV, we can’t, like, roll up from the individual weeks all the way up here.
40 00:03:44.190 ⇒ 00:03:57.500 Uttam Kumaran: And so, we’re kind of… additionally, as… in order to sort of supply some of the reporting requirements, we’re exhausting sort of the rate limits on the SpinApe Spins API in order to pull that, and so that’s just, like.
41 00:03:57.800 ⇒ 00:04:09.390 Uttam Kumaran: we’re having to continuously, like, restart the pipeline to sort of, like, figure this out. So I feel like in this doc, we’ve… we’ve summarized, hopefully, like, enough for them to be like, okay, you guys are…
42 00:04:09.540 ⇒ 00:04:11.350 Uttam Kumaran: You guys can either solve this.
43 00:04:11.600 ⇒ 00:04:20.029 Uttam Kumaran: This way, or maybe give us a nuance of, like, what pulling at a week versus pulling at the 4, 12, 24, 52 week.
44 00:04:20.160 ⇒ 00:04:28.399 Uttam Kumaran: differences are, and I know we’re on a thread with the Spins team, so this is sort of, like, what I was gonna send over to them to…
45 00:04:28.910 ⇒ 00:04:30.489 Uttam Kumaran: Give some commentary.
46 00:04:34.420 ⇒ 00:04:43.190 Michael Thorson: Yeah, just… Supporting… just thinking through this, too, like, are you… are you hitting, like,
47 00:04:43.450 ⇒ 00:04:48.230 Michael Thorson: Because as we’re stepping through all the weeks, are you hitting, like,
48 00:04:48.350 ⇒ 00:04:52.470 Michael Thorson: The rate limit as, like, af… you know, like, further back than, like.
49 00:04:52.620 ⇒ 00:05:02.929 Michael Thorson: I don’t know, like, 24 weeks? Like, is it, like, towards the one-year mark? Because I think, like, long-term, like, steady stay, we’d probably be building an incremental model, you know? So…
50 00:05:03.080 ⇒ 00:05:03.680 Uttam Kumaran: Yeah.
51 00:05:03.680 ⇒ 00:05:06.719 Michael Thorson: This might just be kind of like a… I’m wondering…
52 00:05:06.720 ⇒ 00:05:08.059 Uttam Kumaran: pulling everything right now?
53 00:05:08.400 ⇒ 00:05:14.970 Michael Thorson: Exactly, like, for the full year, whereas, like, moving forward, we wouldn’t necessarily need to do that, we just need a backfill one time, and then we can, like.
54 00:05:14.970 ⇒ 00:05:20.619 Uttam Kumaran: So that’s, like, that’s basically right, because we’re going so far back for the QA exercise. So another
55 00:05:21.030 ⇒ 00:05:34.429 Uttam Kumaran: thing I’m gonna ask them, kindly, is, like, can they just pull in there and give us a flat file, and then we just, like, can increment on top? That is another option that we’ve done with a lot of vendors, where they just give us, in a batch.
56 00:05:34.600 ⇒ 00:05:36.309 Uttam Kumaran: All of the backfill data.
57 00:05:36.430 ⇒ 00:05:40.819 Uttam Kumaran: and then we can just call the API incrementally on top of that.
58 00:05:41.610 ⇒ 00:05:44.199 Uttam Kumaran: Which would be amazing.
59 00:05:44.340 ⇒ 00:05:51.019 Uttam Kumaran: It’s gonna be something I’m gonna ask, them if they could do. Otherwise, we should either
60 00:05:51.400 ⇒ 00:05:57.450 Uttam Kumaran: like, for the QA, I think we should just… we could just pick a smaller subset, maybe, and go after it.
61 00:06:01.330 ⇒ 00:06:03.730 Justin Tabarini: Yeah, I do think it’s smaller, that’s a good call.
62 00:06:04.000 ⇒ 00:06:09.260 Michael Thorson: Yeah, it’s just… it’s just taking quite a bit of time to look through that far back, so…
63 00:06:09.760 ⇒ 00:06:27.249 Uttam Kumaran: I mean, I think part of this is, like, I think I’m gonna start the thread with Spins. I think also, we do have a data set produced there, but maybe we can just agree to kind of go after maybe the last 2 months, or pick 2 months that are already closed, because additionally, Spins has, like, a 26-week
64 00:06:27.650 ⇒ 00:06:30.060 Uttam Kumaran: Like, revision period?
65 00:06:30.300 ⇒ 00:06:31.719 Uttam Kumaran: Which is a bit annoying.
66 00:06:31.840 ⇒ 00:06:33.010 Uttam Kumaran: So…
67 00:06:33.140 ⇒ 00:06:41.370 Uttam Kumaran: We’re also like, okay, if we don’t have any… we don’t have any, information on, like, what was revised and, like, what to pull…
68 00:06:41.950 ⇒ 00:06:48.500 Uttam Kumaran: we kind of are a little bit jammed here, in terms of, like, doing anything incrementally in case there’s revisions, right? So…
69 00:06:49.520 ⇒ 00:06:50.980 Demilade Agboola: Yeah. So would March…
70 00:06:50.980 ⇒ 00:06:51.670 Justin Tabarini: April.
71 00:06:51.840 ⇒ 00:06:54.600 Justin Tabarini: Of 2025 be a good window.
72 00:06:54.600 ⇒ 00:06:54.970 Uttam Kumaran: Yes.
73 00:06:54.970 ⇒ 00:06:55.480 Justin Tabarini: That’s it.
74 00:06:55.870 ⇒ 00:06:57.290 Justin Tabarini: Validate against?
75 00:06:57.290 ⇒ 00:06:57.960 Uttam Kumaran: Yeah.
76 00:06:58.280 ⇒ 00:06:59.269 Justin Tabarini: Those are locked.
77 00:07:00.000 ⇒ 00:07:09.749 Uttam Kumaran: Okay, so then let’s do that, and then what does it look like on your side? Is, like, going… you mentioned, a woman named Heather last time who could pull this data. Is that still, like…
78 00:07:10.190 ⇒ 00:07:14.229 Uttam Kumaran: a good way to kind of pass to her for QA. Michael, do we still want to do the…
79 00:07:14.370 ⇒ 00:07:17.640 Uttam Kumaran: Comparing to the export on your side, is that the kind of the same thing?
80 00:07:17.930 ⇒ 00:07:42.819 Michael Thorson: So, I have, like, I talked to Heather, and I’ve synced with her, like, I have access… I have her access credentials for the Spins Portal, so if y’all just kind of tee up, like, let’s just make sure we communicate, like, exactly what we’re pulling, so we can do the apples to apples, let me know the… I think the key characteristics there would be the specific, end date and the timeframes that we’re going to compare against, so I’ll… I’ll wait for your direction.
81 00:07:42.820 ⇒ 00:07:43.989 Michael Thorson: I just want to make sure I’m…
82 00:07:43.990 ⇒ 00:07:48.189 Michael Thorson: Not over-polling information, so we get, like, a confusing row count.
83 00:07:48.580 ⇒ 00:07:51.759 Uttam Kumaran: Okay, then we’ll just focus on Feb, March.
84 00:07:52.090 ⇒ 00:07:53.660 Uttam Kumaran: And pull those.
85 00:07:55.680 ⇒ 00:08:01.590 Demilade Agboola: To be clear, we want to pull the aggregated, like, region, geography, geography level data.
86 00:08:02.290 ⇒ 00:08:07.659 Uttam Kumaran: Yeah, so the same… I think the same filters that were produced, we’re just gonna limit the timeframe to…
87 00:08:08.440 ⇒ 00:08:11.669 Uttam Kumaran: The weeks within those 8… those two… those two months.
88 00:08:12.480 ⇒ 00:08:21.140 Michael Thorson: And when we’re talking… okay, yeah, that makes sense. So, I’m pulling up the calendar right now, maybe it’s best we just, like, talk through that as well,
89 00:08:21.660 ⇒ 00:08:24.039 Michael Thorson: So you said March, April 2025, right?
90 00:08:24.040 ⇒ 00:08:24.600 Uttam Kumaran: Okay.
91 00:08:25.660 ⇒ 00:08:28.539 Uttam Kumaran: Yeah, sorry, Justin, I don’t know if you said Feb March or March-April.
92 00:08:28.750 ⇒ 00:08:30.070 Justin Tabarini: Doesn’t matter, it’s…
93 00:08:30.070 ⇒ 00:08:30.880 Uttam Kumaran: Okay.
94 00:08:30.880 ⇒ 00:08:32.659 Justin Tabarini: Two locked ones.
95 00:08:32.669 ⇒ 00:08:34.839 Uttam Kumaran: Then either of those.
96 00:08:36.100 ⇒ 00:08:38.530 Michael Thorson: Okay, so that would be period ending…
97 00:08:39.030 ⇒ 00:08:43.429 Michael Thorson: for 2025, I think it would be? That would be the end date?
98 00:08:44.820 ⇒ 00:08:47.709 Michael Thorson: And then the timeframe would be, like, 2 months, right?
99 00:08:48.050 ⇒ 00:08:48.740 Uttam Kumaran: Yes.
100 00:08:48.740 ⇒ 00:08:49.460 Demilade Agboola: Yes.
101 00:08:50.040 ⇒ 00:08:52.900 Michael Thorson: Cool. Yeah, just pulled that from Calendar. That sounds good.
102 00:08:53.540 ⇒ 00:09:00.430 Uttam Kumaran: And then I think also, again, it’s taking us, like, an hour or so to pull each time, so I think producing the QA dataset should be quicker.
103 00:09:00.690 ⇒ 00:09:04.450 Uttam Kumaran: And so let’s… let’s try to hand that to you. We do have…
104 00:09:06.200 ⇒ 00:09:12.559 Uttam Kumaran: like, we did produce one yesterday for the geos for, sort of, all data.
105 00:09:14.710 ⇒ 00:09:16.529 Uttam Kumaran: If you guys still want to check that out.
106 00:09:20.520 ⇒ 00:09:21.190 Justin Tabarini: Like a…
107 00:09:21.190 ⇒ 00:09:24.730 Uttam Kumaran: So yeah, I mean, I think, kind of, like, on this note, one… yeah, go ahead.
108 00:09:25.780 ⇒ 00:09:41.179 Justin Tabarini: Yeah, I guess, like, the key thing I’m thinking about is just, like, let’s look at those two months, and let’s look at the key levels of aggregation we want to get to. Like, we want to get to understand Target, Kroger, Walmart sales overall, and then we want to split by those regions.
109 00:09:41.350 ⇒ 00:09:44.910 Justin Tabarini: And then sum those up over each, like, split that by each month.
110 00:09:45.980 ⇒ 00:09:52.970 Justin Tabarini: And we want to have, like, the other metrics. We know ACV is, like, market ACV is how you balance that, but it’s not going to average week over week, so it’s like…
111 00:09:53.120 ⇒ 00:09:57.859 Justin Tabarini: Fine. Like, that’s… like, we don’t need those metrics to be there, like, we should do them.
112 00:09:58.110 ⇒ 00:10:03.089 Justin Tabarini: But don’t worry about them. Like, if you can produce those, then Michael can produce those, and we can…
113 00:10:03.250 ⇒ 00:10:04.980 Justin Tabarini: Come to an agreement.
114 00:10:05.500 ⇒ 00:10:08.430 Justin Tabarini: on those numbers, I think that’s the key thing. Cool.
115 00:10:08.640 ⇒ 00:10:13.400 Justin Tabarini: And then, like, maybe, like, when we find a disagreement, I feel like that’s when you have to go granular.
116 00:10:13.680 ⇒ 00:10:15.640 Justin Tabarini: But I meant more like, yeah.
117 00:10:16.110 ⇒ 00:10:16.680 Uttam Kumaran: Yeah.
118 00:10:18.830 ⇒ 00:10:19.560 Uttam Kumaran: Okay.
119 00:10:19.690 ⇒ 00:10:24.239 Uttam Kumaran: And then I’m still gonna… I’m still gonna push on the Spins API folks, and be like.
120 00:10:24.370 ⇒ 00:10:30.499 Uttam Kumaran: have you guys seen this before? What do you recommend? And yeah, can you just hand us, like, an export, so we can just move forward, so…
121 00:10:34.260 ⇒ 00:10:35.859 Uttam Kumaran: Okay, great.
122 00:10:36.010 ⇒ 00:10:41.750 Uttam Kumaran: So I feel… I feel like we’re… we’re pushing, at least this week and…
123 00:10:41.900 ⇒ 00:10:50.599 Uttam Kumaran: you know, into the weekend, I think we’re… we’re pretty aligned. And so, I think the rest of the day today, we’ll try to get the,
124 00:10:51.070 ⇒ 00:10:57.130 Uttam Kumaran: 2-month aggregate, the two-month weekly report out, and with the same filters to kind of go through QA.
125 00:10:57.450 ⇒ 00:10:58.060 Uttam Kumaran: Sweet.
126 00:10:58.060 ⇒ 00:11:03.860 Michael Thorson: And… Down here, do we… do you… can you copy in the exact geo filter? Is that in here?
127 00:11:04.270 ⇒ 00:11:07.900 Uttam Kumaran: I can… yeah, I can actually just… I’ll just…
128 00:11:08.820 ⇒ 00:11:14.040 Uttam Kumaran: I’ll put exact… I’ll just actually link them probably to just this… to a spreadsheet where we’re doing this.
129 00:11:14.260 ⇒ 00:11:15.550 Michael Thorson: Cool, yeah, that would be awesome.
130 00:11:15.550 ⇒ 00:11:16.150 Uttam Kumaran: There’s…
131 00:11:16.650 ⇒ 00:11:24.450 Michael Thorson: I just want to take another look at it. And did that… did that communication make sense? Like, I know I sent over, like, a new filter list with, like, a bit of an expanded list.
132 00:11:24.450 ⇒ 00:11:32.049 Uttam Kumaran: No, that’s great. Yeah, I just… I think that’s fine, yeah. I just wanted to confirm that, like, hey, this was different than the before, and yeah, that’s perfect.
133 00:11:32.500 ⇒ 00:11:48.969 Michael Thorson: Yeah, and I know this is… I don’t know how you wrote the code, but, like, wanted to call out that, like, the list of geos I was able to, like, pull was, like, what Magic Spoon is sold in, but again, like, we… there’s, like, a larger, probably, list of geos that other brands are selling in, so I just want to make sure that, like, are you…
134 00:11:49.250 ⇒ 00:11:56.690 Michael Thorson: Are you just, like, hard-coding in those geos, or are you actually, like, querying spins for, like, all available, like, RMA geographies?
135 00:11:56.690 ⇒ 00:12:01.749 Uttam Kumaran: We are… we are filtering because of how large the dataset can be.
136 00:12:01.860 ⇒ 00:12:05.040 Uttam Kumaran: Okay. We are… we are hard-coding the filters.
137 00:12:05.400 ⇒ 00:12:06.240 Michael Thorson: Okay.
138 00:12:07.800 ⇒ 00:12:19.340 Uttam Kumaran: So this is also where, like, if Magic Spoon expands, if I kind of want to… having a relationship with the vendor team from Spins will allow us to, like, just understand, like, what they’re… what they can do as things change.
139 00:12:19.460 ⇒ 00:12:20.749 Uttam Kumaran: And then…
140 00:12:21.000 ⇒ 00:12:26.590 Uttam Kumaran: Primarily, my goal is, like, whoever we’re talking to, or whoever they can connect us with, can put us on with someone from
141 00:12:26.700 ⇒ 00:12:29.689 Uttam Kumaran: They’re a team, and basically, we walk through the request together.
142 00:12:29.870 ⇒ 00:12:37.990 Uttam Kumaran: And they give us the, like, this is… you’re doing it right. So… Okay, so add,
143 00:12:45.340 ⇒ 00:12:46.020 Uttam Kumaran: Okay.
144 00:12:47.690 ⇒ 00:12:48.510 Uttam Kumaran: Cool.
145 00:12:48.720 ⇒ 00:12:50.520 Uttam Kumaran: So, Demi, I can hand it to you.
146 00:12:53.470 ⇒ 00:12:59.480 Demilade Agboola: Okay, sounds good. Hmm, so I couldn’t…
147 00:13:05.830 ⇒ 00:13:10.029 Demilade Agboola: So, the next thing to note is the MMM art.
148 00:13:10.230 ⇒ 00:13:16.210 Demilade Agboola: So the shell of the MMAT has been built, the PRR has been sent in as a draft PR,
149 00:13:16.530 ⇒ 00:13:27.369 Demilade Agboola: So I think the next edge is just basically, like, reviewing it with JT, just to be sure that numbers align and the logic used is exactly what…
150 00:13:27.430 ⇒ 00:13:37.899 Demilade Agboola: is expected. I do have some questions that I would like to, like, ask JT, just so we can clarify and be sure we’re on the same page. So, most of the metrics that I have put in, I’m, like.
151 00:13:37.920 ⇒ 00:13:48.709 Demilade Agboola: fairly confident in them, but there are some that, like, based off the wording in the doc, I’m just not sure if I represented it properly, and those are the metrics I’ll just like to clarify and be sure that,
152 00:13:48.830 ⇒ 00:13:51.860 Demilade Agboola: what I… whatever I did was a good representation.
153 00:13:51.980 ⇒ 00:13:53.810 Demilade Agboola: But the PR…
154 00:13:55.450 ⇒ 00:14:11.450 Demilade Agboola: has been done. First phase was basically non-CSVs, so that has been done. So everything that was a, yes to be modeled, and also was not a CSV, but already in the warehouse, that’s the PR right now.
155 00:14:11.630 ⇒ 00:14:13.550 Demilade Agboola: So you can see it’s right here.
156 00:14:13.980 ⇒ 00:14:18.109 Demilade Agboola: And basically… that’s been done.
157 00:14:18.310 ⇒ 00:14:22.860 Demilade Agboola: And then, I’m currently just finishing up the CSV version.
158 00:14:23.050 ⇒ 00:14:25.599 Demilade Agboola: Most of it is done.
159 00:14:26.630 ⇒ 00:14:34.130 Demilade Agboola: So, like, the butter rebate offer spend weekly, that’s been done, so I’ve made the union based off all of them.
160 00:14:34.290 ⇒ 00:14:35.900 Demilade Agboola: And then summed up.
161 00:14:36.390 ⇒ 00:14:42.620 Demilade Agboola: Where it’s the property’s daily spend, and all of that. So the linear spend has also been done.
162 00:14:42.970 ⇒ 00:14:48.309 Demilade Agboola: Where the linear is TV. So things like what you put in Doc, I have represented it.
163 00:14:48.420 ⇒ 00:14:56.320 Demilade Agboola: But I do have some, like I said, that I’m just not fairly confident with, and so we can always just have, like, a session where we can quickly just run through it.
164 00:14:56.640 ⇒ 00:15:00.299 Demilade Agboola: I don’t know when you’re available, sometime today.
165 00:15:00.500 ⇒ 00:15:07.830 Demilade Agboola: But we just run through it to just be sure that, like, what I’m doing is exactly what you want to see for those ones that I’m just, like, I have question marks about.
166 00:15:10.160 ⇒ 00:15:18.519 Justin Tabarini: Yeah, I think it’d be, so, agree, yeah, let’s go through a session on the question marks. It would be helpful if you, like, also…
167 00:15:18.890 ⇒ 00:15:22.159 Justin Tabarini: I don’t know if you have an updated version that you could show me the…
168 00:15:22.450 ⇒ 00:15:25.330 Justin Tabarini: CSV stuff as well.
169 00:15:25.930 ⇒ 00:15:32.309 Justin Tabarini: I did, like, half a QA, I’m still working through every field, but so far, so good on the…
170 00:15:32.580 ⇒ 00:15:38.640 Justin Tabarini: The, like… the actual data warehouse data. I think the more… Okay.
171 00:15:40.000 ⇒ 00:15:41.930 Justin Tabarini: complicated ones are gonna be there.
172 00:15:42.840 ⇒ 00:15:50.049 Demilade Agboola: Okay, so the… you’re referring to the… because I ran the… I did a dbt run, so there’s data in there. I know you’ve been looking… is that what you’re referring to?
173 00:15:51.490 ⇒ 00:15:54.640 Justin Tabarini: Yeah, I guess right now I don’t really have the…
174 00:15:54.790 ⇒ 00:15:57.359 Justin Tabarini: The link that you sent me…
175 00:15:57.630 ⇒ 00:16:02.410 Justin Tabarini: Or the table you sent me only includes the stuff that already exists in the warehouse.
176 00:16:03.110 ⇒ 00:16:06.200 Demilade Agboola: Yeah, yeah, so that’s… that’s that. So this is what you’re referring to.
177 00:16:06.200 ⇒ 00:16:07.520 Justin Tabarini: Yep. Yes.
178 00:16:07.520 ⇒ 00:16:08.520 Demilade Agboola: Yeah, alright.
179 00:16:08.790 ⇒ 00:16:14.430 Demilade Agboola: So… That’s, yeah, that’s the warehouse stuff, yeah, so the,
180 00:16:15.100 ⇒ 00:16:28.440 Demilade Agboola: the CSV work will come in later today, and then we can just QA it together, and I’ll tell you, like, the assumptions I made versus, like, what’s in there. Plus, there’s some I do have, like, genuine questions about, so I don’t know if you’ll… Yeah, that’s…
181 00:16:28.510 ⇒ 00:16:29.960 Justin Tabarini: We can sit right now and talk.
182 00:16:30.620 ⇒ 00:16:34.059 Demilade Agboola: Okay, in that case, yeah, let’s just quickly, like, walk through some of them.
183 00:16:34.340 ⇒ 00:16:38.120 Demilade Agboola: Give me one second…
184 00:16:38.880 ⇒ 00:16:39.240 Justin Tabarini: Yeah.
185 00:16:39.350 ⇒ 00:16:41.290 Demilade Agboola: Romantic load… no…
186 00:16:44.370 ⇒ 00:16:46.970 Demilade Agboola: the CSV1s… alright.
187 00:16:48.470 ⇒ 00:16:49.770 Demilade Agboola: so for…
188 00:16:52.860 ⇒ 00:17:00.550 Demilade Agboola: Alright, so one thing I noticed was, for, like, all metrics, Here, the top four.
189 00:17:00.950 ⇒ 00:17:05.579 Demilade Agboola: Yeah. When it comes to the Walmart reporting January 2026 sheet.
190 00:17:05.730 ⇒ 00:17:11.299 Demilade Agboola: It appears that for all of them, we need all CTV. Would that be… would that be accurate?
191 00:17:12.069 ⇒ 00:17:19.519 Justin Tabarini: So it’s… I was trying to, like, categorize that a lot of these use campaign names.
192 00:17:19.989 ⇒ 00:17:30.129 Justin Tabarini: And so, like, whenever, like, this one, it says, like, this is all CTV, which means it actually shouldn’t go in programmatic on-site display, it should actually only go in the CTV column.
193 00:17:32.110 ⇒ 00:17:34.909 Demilade Agboola: Gotcha, so it’s only good for programmatic CTV.
194 00:17:35.380 ⇒ 00:17:39.670 Justin Tabarini: Yeah, it should only fall in that column, and then other ones are using campaign name.
195 00:17:40.400 ⇒ 00:17:52.980 Justin Tabarini: So I was trying to, like, kind of describe, like, these all fall in these buckets, and sometimes it only exists here, sometimes it only exists there. And so I was trying to say, like, well, this one, only put it in CTV. This one…
196 00:17:52.980 ⇒ 00:17:53.640 Demilade Agboola: Okay.
197 00:17:53.800 ⇒ 00:17:56.090 Justin Tabarini: The next one, it has the differences.
198 00:17:57.610 ⇒ 00:17:59.330 Demilade Agboola: Alright, so this, this one…
199 00:18:00.730 ⇒ 00:18:05.599 Demilade Agboola: basically, look for campaign name for display, so that means for programmatic display, look for DSP.
200 00:18:05.740 ⇒ 00:18:09.939 Demilade Agboola: For OLV, look for OLV, and for CTV, look for CTV, right?
201 00:18:09.940 ⇒ 00:18:10.470 Justin Tabarini: Yep.
202 00:18:11.270 ⇒ 00:18:14.149 Demilade Agboola: Okay, and then this same thing here as well.
203 00:18:14.730 ⇒ 00:18:16.719 Demilade Agboola: And then, for this…
204 00:18:18.950 ⇒ 00:18:26.630 Justin Tabarini: So this example, there’s a media type, and there’s 4 media types. And I’m saying OTT and CTV group into CTV.
205 00:18:26.890 ⇒ 00:18:27.670 Demilade Agboola: Gotcha.
206 00:18:28.620 ⇒ 00:18:33.380 Demilade Agboola: and then OTT and… Display.
207 00:18:34.090 ⇒ 00:18:39.699 Demilade Agboola: would be display, and then OTV and OLV would be OLV, right?
208 00:18:41.080 ⇒ 00:18:44.839 Justin Tabarini: Or, actually, no, they’re actually different again, because this was confusing, because.
209 00:18:44.840 ⇒ 00:18:51.090 Demilade Agboola: For each of the media types, there’s OTT, there’s CTV, there’s OLV, and there’s display, so they’re four media types. Cool, yeah.
210 00:18:51.750 ⇒ 00:18:56.059 Demilade Agboola: So I was… so my… For this, it has to be…
211 00:18:56.500 ⇒ 00:18:59.970 Demilade Agboola: I’m just trying to understand how… what makes,
212 00:19:00.830 ⇒ 00:19:03.940 Demilade Agboola: So it has to be OTT and CTV for it to be CTV, right?
213 00:19:03.940 ⇒ 00:19:05.410 Justin Tabarini: No, I’m saying OR.
214 00:19:06.000 ⇒ 00:19:06.740 Justin Tabarini: Like.
215 00:19:06.740 ⇒ 00:19:07.990 Demilade Agboola: Warm, okay.
216 00:19:07.990 ⇒ 00:19:11.690 Justin Tabarini: or it’s those two equal CTV.
217 00:19:11.930 ⇒ 00:19:12.800 Justin Tabarini: Yeah.
218 00:19:13.270 ⇒ 00:19:15.360 Demilade Agboola: You can see how that was confusing.
219 00:19:15.360 ⇒ 00:19:17.350 Justin Tabarini: Yeah, yeah, yeah, there’s 4 different media types, yeah.
220 00:19:17.760 ⇒ 00:19:20.020 Demilade Agboola: Okay, alright, so this play…
221 00:19:20.380 ⇒ 00:19:24.520 Demilade Agboola: When it’s displayed, would that be on-site or off-site, or is that just for both of them?
222 00:19:25.380 ⇒ 00:19:28.580 Justin Tabarini: Okay, that’s off-site. Great question.
223 00:19:28.900 ⇒ 00:19:29.590 Justin Tabarini: So…
224 00:19:29.590 ⇒ 00:19:30.020 Demilade Agboola: Okay, so…
225 00:19:30.020 ⇒ 00:19:36.530 Justin Tabarini: The only on-site display Did I not?
226 00:19:38.220 ⇒ 00:19:43.100 Justin Tabarini: I, I might have… goofed with on-site, I’m looking right now.
227 00:19:44.920 ⇒ 00:19:47.150 Justin Tabarini: I thought I wrote about the turret.
228 00:19:47.150 ⇒ 00:19:51.940 Demilade Agboola: Yeah, so, like, for life of me, I was writing it, but I was just not confident in what I was, like, doing, to be very honest, and I was like.
229 00:19:52.270 ⇒ 00:19:54.540 Demilade Agboola: I think I need to, like, clarify this.
230 00:19:54.540 ⇒ 00:19:56.939 Justin Tabarini: Yeah, no, of course, and also, like, that’s…
231 00:19:57.650 ⇒ 00:20:04.029 Justin Tabarini: Yeah, I’m not gonna build a perfect doc, so, like, please, like, just Slack when you have the questions.
232 00:20:05.660 ⇒ 00:20:06.770 Justin Tabarini: So…
233 00:20:11.050 ⇒ 00:20:19.260 Justin Tabarini: Actually… So the… Display on all of them are off-site displays.
234 00:20:19.750 ⇒ 00:20:25.960 Justin Tabarini: There’s only one media file with on-site displays, and we actually got an updated version that’s gonna be easier to deal with.
235 00:20:27.310 ⇒ 00:20:28.170 Demilade Agboola: Okay, so…
236 00:20:28.540 ⇒ 00:20:28.920 Justin Tabarini: obsessed.
237 00:20:28.920 ⇒ 00:20:32.339 Demilade Agboola: On that note, how do we determine on-site display right now?
238 00:20:32.870 ⇒ 00:20:35.869 Justin Tabarini: Right now, everything you see is off-site.
239 00:20:36.700 ⇒ 00:20:39.270 Demilade Agboola: So that means there isn’t any on-site display right now.
240 00:20:39.540 ⇒ 00:20:42.870 Justin Tabarini: I’m going to tag a new dataset.
241 00:20:43.530 ⇒ 00:20:45.989 Justin Tabarini: Okay, so I can delete this, that’s fine.
242 00:20:46.730 ⇒ 00:20:49.620 Justin Tabarini: Delete, sorry, I’m… I’m, like, in the file itself.
243 00:20:49.820 ⇒ 00:20:55.219 Justin Tabarini: On-site display? Yeah, well, you’re still gonna need to be able to use the logic of that.
244 00:20:57.130 ⇒ 00:21:01.939 Justin Tabarini: But anything that goes in display will land… yeah, I guess you’re right, you can delete that for now, sorry.
245 00:21:01.940 ⇒ 00:21:02.530 Demilade Agboola: Okay.
246 00:21:03.170 ⇒ 00:21:04.090 Justin Tabarini: That one’s fine.
247 00:21:04.370 ⇒ 00:21:09.780 Justin Tabarini: Yeah, we got an updated file that’ll be easier to parse through.
248 00:21:10.290 ⇒ 00:21:17.709 Justin Tabarini: So instead of target… so it’s… it’s called target on-site, off-site data. Previously, we had target off-site display look-back.
249 00:21:18.120 ⇒ 00:21:20.460 Justin Tabarini: We’re actually replacing this file.
250 00:21:21.260 ⇒ 00:21:26.630 Justin Tabarini: So, like, that target off-site, on-site display look-back is no longer going to be the file we’ll use.
251 00:21:27.340 ⇒ 00:21:28.100 Justin Tabarini: Mmm. This…
252 00:21:28.100 ⇒ 00:21:29.030 Demilade Agboola: K…
253 00:21:30.860 ⇒ 00:21:35.620 Justin Tabarini: This one actually makes it easier for you to cut between on-site and off-site data.
254 00:21:36.570 ⇒ 00:21:39.659 Demilade Agboola: Alright, and that would also be used for this as well, right?
255 00:21:43.050 ⇒ 00:21:44.330 Demilade Agboola: That would also be used for the offset.
256 00:21:44.330 ⇒ 00:21:46.040 Justin Tabarini: Yes, for the off-site, yes.
257 00:21:46.280 ⇒ 00:21:51.059 Demilade Agboola: Alright, so in that case, I can kind of just get rid of everything here.
258 00:21:53.500 ⇒ 00:21:56.710 Demilade Agboola: But this… platform. Will this be…
259 00:21:56.710 ⇒ 00:21:58.030 Justin Tabarini: target.
260 00:21:59.350 ⇒ 00:22:01.230 Demilade Agboola: So, right now, okay.
261 00:22:01.520 ⇒ 00:22:14.859 Justin Tabarini: Well, so off-site includes everything from above. Like, when you see display on all of the other ones, that filters through off-site, just there’s the last thing, which is then Target also has its own thing, where you have to dive into…
262 00:22:16.630 ⇒ 00:22:20.259 Justin Tabarini: So yeah, these all still are valid for when it’s display.
263 00:22:21.000 ⇒ 00:22:23.110 Demilade Agboola: Okay, so this will be display…
264 00:22:23.570 ⇒ 00:22:27.660 Demilade Agboola: And then, does it have to be OTT and display, or just display? No, just display.
265 00:22:27.660 ⇒ 00:22:29.300 Justin Tabarini: Display. Okay. Display.
266 00:22:29.970 ⇒ 00:22:32.320 Demilade Agboola: Okay, so media type is display.
267 00:22:33.520 ⇒ 00:22:41.430 Demilade Agboola: And then OLD… And then… Not yet there, but we will also include…
268 00:22:51.160 ⇒ 00:22:54.519 Justin Tabarini: So I updated with the logic, the new file in the logic.
269 00:23:03.160 ⇒ 00:23:05.000 Demilade Agboola: So I’ll also include this as well.
270 00:23:05.140 ⇒ 00:23:09.369 Demilade Agboola: So would that platform for off-site be target as well? Or would that be different?
271 00:23:09.370 ⇒ 00:23:12.840 Justin Tabarini: Off-site will be not target. It’s target and not equal to target.
272 00:23:13.450 ⇒ 00:23:16.429 Justin Tabarini: It might be called Target.com, you’ll have to take a look.
273 00:23:16.990 ⇒ 00:23:20.489 Justin Tabarini: Forget what they… let me just actually just equals unique.
274 00:23:22.790 ⇒ 00:23:27.379 Justin Tabarini: But it’s, like, called Platform Name, and it’s Target.com.
275 00:23:28.580 ⇒ 00:23:29.220 Demilade Agboola: Oh, okay.
276 00:23:29.400 ⇒ 00:23:32.900 Justin Tabarini: And so one’s… Target.com.
277 00:23:34.220 ⇒ 00:23:38.259 Demilade Agboola: I mean… It’s not going to be close to Target.com.
278 00:23:39.780 ⇒ 00:23:41.820 Demilade Agboola: Okay, and this will be, of course, too.
279 00:23:42.100 ⇒ 00:23:43.480 Demilade Agboola: Target.com.
280 00:23:43.910 ⇒ 00:23:51.509 Justin Tabarini: And the one other thing, though, is the same filter applies that existed in the previous one. This is just cut in an easier way, which is…
281 00:23:51.780 ⇒ 00:23:54.459 Justin Tabarini: It’s… so right here, we have the…
282 00:23:55.550 ⇒ 00:23:58.669 Justin Tabarini: If you go back to on-site, platform name equals target.com.
283 00:23:59.050 ⇒ 00:24:05.439 Justin Tabarini: And then campaign name is not equal to CSV, or CTV, sorry. Doesn’t contain CTV.
284 00:24:09.450 ⇒ 00:24:14.440 Demilade Agboola: Campaign… Name…
285 00:24:14.440 ⇒ 00:24:15.340 Justin Tabarini: name…
286 00:24:16.460 ⇒ 00:24:17.940 Demilade Agboola: Not in…
287 00:24:18.970 ⇒ 00:24:20.590 Justin Tabarini: Doesn’t include CTV.
288 00:24:20.910 ⇒ 00:24:21.740 Demilade Agboola: CTV.
289 00:24:22.300 ⇒ 00:24:23.280 Demilade Agboola: Basically.
290 00:24:23.950 ⇒ 00:24:29.350 Justin Tabarini: Yeah, and that’s gonna flow for both the programmatic and the on-site and off-site.
291 00:24:29.930 ⇒ 00:24:30.850 Demilade Agboola: Okay, so I’ll…
292 00:24:30.850 ⇒ 00:24:31.300 Justin Tabarini: Same…
293 00:24:31.300 ⇒ 00:24:34.229 Demilade Agboola: And campaign… campaigning is not sensitive, basically.
294 00:24:34.230 ⇒ 00:24:36.850 Justin Tabarini: And then we want to put that in CTV.
295 00:24:40.550 ⇒ 00:24:43.439 Demilade Agboola: So, CTV, which is here.
296 00:24:43.720 ⇒ 00:24:44.170 Justin Tabarini: Yep.
297 00:24:44.170 ⇒ 00:24:46.569 Demilade Agboola: Well, basically, so this will be all CTV.
298 00:24:46.950 ⇒ 00:24:55.690 Justin Tabarini: Yep, so I want to include that. Those 3 below, we just want to include where the campaign name includes CTV, or the media type is OTT or CTV.
299 00:24:58.190 ⇒ 00:24:58.900 Demilade Agboola: Okay…
300 00:25:04.630 ⇒ 00:25:10.470 Demilade Agboola: So we have this, where the… Media type.
301 00:25:11.380 ⇒ 00:25:12.240 Demilade Agboola: Sorry.
302 00:25:13.120 ⇒ 00:25:14.250 Demilade Agboola: Got a bit lost there.
303 00:25:15.620 ⇒ 00:25:20.740 Justin Tabarini: Okay, so we’ll go through it line by line. Line one, TV.
304 00:25:20.740 ⇒ 00:25:22.400 Demilade Agboola: Carsity. Good.
305 00:25:22.400 ⇒ 00:25:29.300 Justin Tabarini: profile lands. This next one, we only want to include where the campaign name Include CTV.
306 00:25:29.930 ⇒ 00:25:31.749 Justin Tabarini: It’s not gonna be is, but, you know.
307 00:25:31.990 ⇒ 00:25:35.959 Justin Tabarini: It’s… all of these are includes, so once you look at the details…
308 00:25:36.800 ⇒ 00:25:43.450 Demilade Agboola: When you say all CTV, that means everything in this table is CTV. Like, you saw that, and it’s already CTV. Gotcha, just to confirm.
309 00:25:43.450 ⇒ 00:25:44.370 Justin Tabarini: Pre-filtered.
310 00:25:45.170 ⇒ 00:25:45.909 Demilade Agboola: Oh, okay.
311 00:25:46.990 ⇒ 00:25:49.200 Demilade Agboola: Click for company name includes CTV.
312 00:25:50.540 ⇒ 00:25:53.609 Demilade Agboola: And now filtering for media type, or…
313 00:25:54.010 ⇒ 00:25:55.969 Justin Tabarini: Take those out, and it’s an ore.
314 00:25:56.520 ⇒ 00:25:57.270 Demilade Agboola: Okay.
315 00:25:58.490 ⇒ 00:26:05.929 Justin Tabarini: The target off-site display, we updated that file, so the new file that makes it easier for you to cut for on-site, off-site.
316 00:26:06.640 ⇒ 00:26:10.559 Justin Tabarini: You use the new file and include anything that does have
317 00:26:11.160 ⇒ 00:26:15.299 Justin Tabarini: CTV. Yeah, that replaces the line that… exactly.
318 00:26:17.150 ⇒ 00:26:17.810 Demilade Agboola: So…
319 00:26:17.810 ⇒ 00:26:21.080 Justin Tabarini: And we don’t care about where the platform is, we just care about…
320 00:26:22.070 ⇒ 00:26:27.920 Demilade Agboola: the city… Also, when would I get this target on-site? Is it there as well?
321 00:26:27.920 ⇒ 00:26:29.380 Justin Tabarini: Uploaded already, yep.
322 00:26:29.380 ⇒ 00:26:31.750 Demilade Agboola: Okay, alright, so I’ll look at it and just use it.
323 00:26:31.920 ⇒ 00:26:37.759 Demilade Agboola: And then spending by inventory… what inventory type is streaming TV is fine.
324 00:26:39.020 ⇒ 00:26:46.370 Justin Tabarini: And all of these might be, like, slightly off, like, maybe inventory is, like… I think it is streaming TV, but, like…
325 00:26:46.670 ⇒ 00:26:49.110 Justin Tabarini: You know, shouldn’t include the gist.
326 00:26:49.930 ⇒ 00:26:52.270 Demilade Agboola: Okay, alright, here’s this pay.
327 00:26:53.730 ⇒ 00:26:59.050 Demilade Agboola: Alright, so for OLV, so that means OLV here, like, this, this doesn’t… Ignore, you can ignore that one, yeah.
328 00:26:59.970 ⇒ 00:27:02.750 Demilade Agboola: So this would be… Well, V…
329 00:27:07.010 ⇒ 00:27:15.380 Demilade Agboola: Or… V… for LV, and then… OLV…
330 00:27:16.450 ⇒ 00:27:20.260 Demilade Agboola: And so we don’t need OTT or this, the media type would just be OOV, right?
331 00:27:20.660 ⇒ 00:27:22.689 Justin Tabarini: Yep, media type is equal to all of it, yes.
332 00:27:23.480 ⇒ 00:27:35.939 Demilade Agboola: Alright, thanks a lot for that. I should be able to push this out today, and then you can just have a look at it, and just let me know what you think. Like, I’ll push it to the same model you’re looking at, I will run it so you can see the numbers.
333 00:27:36.290 ⇒ 00:27:40.100 Justin Tabarini: Yeah, that’d be great, push the tag model, and then also if you could push your…
334 00:27:41.380 ⇒ 00:27:51.020 Justin Tabarini: code in the PR as well. That makes it easy for… if I see an issue, I can then look at the code and then understand, so… push both, Slack me when you do it, I’ll…
335 00:27:51.020 ⇒ 00:27:51.370 Demilade Agboola: Well done.
336 00:27:51.370 ⇒ 00:27:52.580 Justin Tabarini: my QA.
337 00:27:53.450 ⇒ 00:27:54.270 Demilade Agboola: Alright, Ben.
338 00:27:55.570 ⇒ 00:27:58.869 Demilade Agboola: So just, like, final things were basically done.
339 00:27:59.030 ⇒ 00:28:05.659 Demilade Agboola: I guess the… since we’re at this point, the major things left would just likely be scoping for, like, February, and just, like.
340 00:28:06.020 ⇒ 00:28:11.139 Demilade Agboola: Ensuring that we resolve, like, the tidy of the T’s and dot the I’s.
341 00:28:11.880 ⇒ 00:28:14.580 Demilade Agboola: on… Spins, as well as the mat.
342 00:28:17.300 ⇒ 00:28:18.449 Mary Burke: Yeah, a line there.
343 00:28:21.930 ⇒ 00:28:23.400 Demilade Agboola: Okay, alright, so…
344 00:28:23.710 ⇒ 00:28:32.089 Demilade Agboola: Thank you. Does anyone have any questions or feedback or thoughts about certain processes or things we could be doing, or should be doing about certain things?
345 00:28:34.060 ⇒ 00:28:42.850 Mary Burke: No, I think, just echoing JT’s point earlier, if questions come up, too, don’t feel like you have to wait until we’re on a call night, you can Slack us whenever.
346 00:28:43.200 ⇒ 00:28:46.150 Mary Burke: I’m, like, signing JT up for that, but…
347 00:28:46.150 ⇒ 00:28:48.569 Justin Tabarini: And I can hop on a huddle.
348 00:28:48.570 ⇒ 00:28:54.829 Uttam Kumaran: We were debating this internally before, and then literally, like, 45 minutes ago, and I was like, it’s…
349 00:28:55.060 ⇒ 00:28:58.900 Uttam Kumaran: We’ll just use the call, and then I was like, Demi, just wrap it up, yeah.
350 00:28:59.650 ⇒ 00:29:09.689 Justin Tabarini: Yeah. And also, like, don’t spend too much time debating it. Like, I’m right here, a Slack will be quite easy, and then also, I can hop on a huddle for 5 minutes. Especially with, like.
351 00:29:09.950 ⇒ 00:29:12.909 Justin Tabarini: Input variable definition, which is super weird.
352 00:29:13.630 ⇒ 00:29:15.199 Demilade Agboola: Yeah, it’s all good.
353 00:29:16.200 ⇒ 00:29:18.580 Demilade Agboola: Alright then, thanks so much, see you next time.
354 00:29:18.580 ⇒ 00:29:23.999 Ashwini Sharma: Actually, sorry, quick question, this March and April data, what’s the baseline date for this?
355 00:29:24.680 ⇒ 00:29:26.539 Uttam Kumaran: We’re gonna do end of April, right?
356 00:29:26.680 ⇒ 00:29:27.860 Ashwini Sharma: End of April, okay.
357 00:29:29.490 ⇒ 00:29:34.749 Ashwini Sharma: And then one week worth of data going back till March 1st.
358 00:29:37.570 ⇒ 00:29:38.300 Uttam Kumaran: That’s correct.
359 00:29:38.680 ⇒ 00:29:44.469 Michael Thorson: Yeah, what… just double-checking that, I… what time frames do you want me to pull out of…
360 00:29:45.380 ⇒ 00:29:50.190 Michael Thorson: the platform. Do you just want, like, a 4-week time frame, or do you want…
361 00:29:50.300 ⇒ 00:29:55.030 Michael Thorson: Like, raw data that goes back a little bit further as well.
362 00:29:55.530 ⇒ 00:29:56.110 Michael Thorson: It’s this…
363 00:29:56.110 ⇒ 00:29:58.480 Uttam Kumaran: I think if we can just do a week, and…
364 00:29:59.130 ⇒ 00:30:04.100 Uttam Kumaran: and we just do the 8 weeks, and we start there, I think that’d be easiest to sort of just match.
365 00:30:04.730 ⇒ 00:30:08.960 Uttam Kumaran: Like, one week each week for the… for those two months.
366 00:30:14.580 ⇒ 00:30:22.349 Michael Thorson: Wait, sorry, you cut out a little bit on my side. You said you wanted… you want the, like, trended, like, single-week snapshots, or do you want, like, a…
367 00:30:22.750 ⇒ 00:30:33.199 Ashwini Sharma: Yeah, single-week snapshots. Single-week snapshots would be good, because we get that from… I mean, we can pull the 4-week and 12, 24, 52 also.
368 00:30:33.200 ⇒ 00:30:36.309 Uttam Kumaran: Let’s do… let’s just do the one week and start there. Yeah. Yeah.
369 00:30:36.620 ⇒ 00:30:41.840 Michael Thorson: Okay, and how many weeks do you want me to work back just one, or do you want, like, four, or, like, a wider bid?
370 00:30:42.060 ⇒ 00:30:44.700 Ashwini Sharma: like, end of April till beginning of.
371 00:30:44.700 ⇒ 00:30:45.900 Uttam Kumaran: Yeah, so the 8 weeks.
372 00:30:45.900 ⇒ 00:30:46.750 Ashwini Sharma: 8 weeks.
373 00:30:47.300 ⇒ 00:30:48.120 Michael Thorson: Hey, Wiggs.
374 00:30:49.960 ⇒ 00:30:50.640 Michael Thorson: Cool.
375 00:30:52.360 ⇒ 00:30:56.540 Michael Thorson: Great, I’ll have that in a couple minutes. I was just looking at it, making sure I’m extracting the right thing.
376 00:30:57.350 ⇒ 00:30:57.910 Uttam Kumaran: Okay.
377 00:30:58.460 ⇒ 00:30:58.970 Ashwini Sharma: Yep.
378 00:30:59.300 ⇒ 00:31:06.320 Ashwini Sharma: Can you give download access to that data? Because, last time the… Platform data was not accessible.
379 00:31:06.770 ⇒ 00:31:10.450 Michael Thorson: Yeah, I’ll make sure that’s accessible. Hmm…
380 00:31:12.480 ⇒ 00:31:26.550 Michael Thorson: Yeah, I’ll, I’ll let you know what I can pull out. A lot of the data that comes out of platform, you can only pull in, like, aggregate chunks of, like, 4, 12, etc, like, the available options, so they might be pre-aggregated into timeframe buckets.
381 00:31:26.780 ⇒ 00:31:27.860 Michael Thorson: Okay.
382 00:31:28.200 ⇒ 00:31:29.520 Michael Thorson: Yeah, I’ll…
383 00:31:29.520 ⇒ 00:31:32.330 Uttam Kumaran: Maybe we’ll just see what you get, and then we can collaborate, yeah.
384 00:31:32.830 ⇒ 00:31:38.819 Michael Thorson: Cool, that sounds good. Well, I’ll get that uploaded to S3, shortly, and just, like, outline via Slack what exactly it is.
385 00:31:39.590 ⇒ 00:31:40.530 Uttam Kumaran: Okay, okay.
386 00:31:42.820 ⇒ 00:31:43.660 Uttam Kumaran: Alright.
387 00:31:43.820 ⇒ 00:31:44.520 Justin Tabarini: Thanks, everyone.
388 00:31:44.810 ⇒ 00:31:45.440 Ashwini Sharma: Ciao.
389 00:31:45.440 ⇒ 00:31:46.299 Mary Burke: Thank you.
390 00:31:46.560 ⇒ 00:31:47.290 Uttam Kumaran: Bye.