Meeting Title: Magic Spoon Data Aggregation Sync Date: 2026-01-28 Meeting participants: Demilade Agboola, Ashwini Sharma
WEBVTT
1 00:01:36.050 ⇒ 00:01:38.010 Demilade Agboola: I’m trying to see if Autumn…
2 00:01:38.790 ⇒ 00:01:41.640 Demilade Agboola: Hop on. Alright, oh, he has a call.
3 00:01:53.640 ⇒ 00:01:57.379 Demilade Agboola: So it does appear Autumn has a call right now, so I think we could just quickly try and debrief.
4 00:01:57.660 ⇒ 00:01:59.200 Demilade Agboola: And then, just C.
5 00:02:00.030 ⇒ 00:02:08.040 Demilade Agboola: But do you feel like you have, like, the clarity to go ahead with the cuts? Like, the aggregations across, like, the different…
6 00:02:08.229 ⇒ 00:02:10.560 Demilade Agboola: Bronze, geography level.
7 00:02:10.850 ⇒ 00:02:22.199 Ashwini Sharma: Yeah, I’ve got the new data with me, right, which is much better shape. I don’t really have the clarity of the aggregation at the level that they are asking.
8 00:02:22.310 ⇒ 00:02:35.330 Ashwini Sharma: it got quite a bit confusing, and I didn’t want to ask too many questions over there. I thought Utam has understood some of those stuff, right? But what I can understand is they want to aggregate it at a geography level, right?
9 00:02:35.430 ⇒ 00:02:44.180 Ashwini Sharma: So they want to sum up those things, maybe… maybe some kind of a pivot table on… on the data table. On the table that we have created, that… that might help.
10 00:02:44.440 ⇒ 00:02:51.880 Ashwini Sharma: Just trying to, they did talk about Kroger, right, somewhere?
11 00:02:51.880 ⇒ 00:02:55.190 Demilade Agboola: Yeah, you mentioned Kroger. Kroger is… early brands.
12 00:02:55.190 ⇒ 00:03:01.350 Ashwini Sharma: I failed to understand what exactly did they mean when they said, like, they need something broker.
13 00:03:01.880 ⇒ 00:03:07.409 Demilade Agboola: So Kroger is… so let me send… let me send… type it out to you. So this is Kroger, this is how it’s spelled.
14 00:03:07.540 ⇒ 00:03:12.630 Demilade Agboola: It’s a brand, it’s one of their, like, customers, so I’m guessing instead of Magic Spoon.
15 00:03:12.940 ⇒ 00:03:16.430 Ashwini Sharma: Because I don’t have Kroger in my data, right?
16 00:03:17.260 ⇒ 00:03:21.429 Demilade Agboola: Yes, because I think you filtered out everything that is not Magic Spoon, right?
17 00:03:22.350 ⇒ 00:03:23.200 Ashwini Sharma: Yes.
18 00:03:23.580 ⇒ 00:03:25.840 Demilade Agboola: Okay, so please, can you check if you can see…
19 00:03:26.000 ⇒ 00:03:30.140 Demilade Agboola: any data for Kroger. Instead of… so instead of using Magic Spoon, can you translate.
20 00:03:30.140 ⇒ 00:03:34.209 Ashwini Sharma: I can’t do that immediately, because that’s,
21 00:03:35.010 ⇒ 00:03:38.240 Ashwini Sharma: Okay. I’ll run the entire thing again, right?
22 00:03:38.760 ⇒ 00:03:49.279 Demilade Agboola: Okay, sure, but the idea is, so we need to just kind of test that to be sure that they also get in. But you don’t need to do it as granular, we don’t need to break it down that much, we just may need to do a sum.
23 00:03:50.190 ⇒ 00:03:56.070 Demilade Agboola: of Kroger by geography level, for instance, or some of Kroger by…
24 00:03:56.350 ⇒ 00:04:01.689 Demilade Agboola: You know, different, like, different levels, different, things by brands as well.
25 00:04:02.420 ⇒ 00:04:06.429 Demilade Agboola: So that way, we can kind of see, and they can compare.
26 00:04:07.970 ⇒ 00:04:13.500 Demilade Agboola: The internal team as to how exactly the aggregations are working, and if it’s working properly.
27 00:04:32.890 ⇒ 00:04:38.020 Ashwini Sharma: I’m still kind of confused about the aggregation that they are looking for.
28 00:04:38.930 ⇒ 00:04:46.520 Demilade Agboola: So, what they need is, they need aggregations by, retail… by… not retail, sorry, let me open the sheet.
29 00:04:46.910 ⇒ 00:04:50.089 Demilade Agboola: By geography level, geography level is definitely one.
30 00:04:50.500 ⇒ 00:04:55.280 Demilade Agboola: By geography level, By geography as well.
31 00:04:55.650 ⇒ 00:04:59.639 Demilade Agboola: by, say, product level or category level. Like, basically.
32 00:04:59.780 ⇒ 00:05:04.750 Demilade Agboola: For the brand Magic Spoon, overall 52 weeks, what does it look like for the category?
33 00:05:05.370 ⇒ 00:05:07.040 Demilade Agboola: Or, you know, put the…
34 00:05:07.040 ⇒ 00:05:11.759 Ashwini Sharma: At the category level, right? They’re asking us to aggregate at the category level, right?
35 00:05:11.760 ⇒ 00:05:23.569 Demilade Agboola: Yeah, so for different things, so for the brand, Magic Spoon, for the product level, sum it across… again, sum it for, like, 52 weeks, some it for, you know, 4 weeks.
36 00:05:24.090 ⇒ 00:05:24.490 Ashwini Sharma: Yeah.
37 00:05:24.490 ⇒ 00:05:32.769 Demilade Agboola: geography as well, for the geography level as well. So the idea is they can also now take these numbers, look at what they’re seeing on the platform.
38 00:05:33.570 ⇒ 00:05:36.560 Demilade Agboola: I can say, oh, for Magic Spawns, for geography.
39 00:05:37.050 ⇒ 00:05:45.340 Demilade Agboola: for the last 4 weeks, or the last 52 weeks, they can then compare and see that the Spins API matches the numbers on such a high level.
40 00:05:45.680 ⇒ 00:05:52.749 Ashwini Sharma: Okay, okay. All right, I think I’ll create some pivot tables out of the data that I have, right? And then,
41 00:05:52.850 ⇒ 00:05:57.070 Ashwini Sharma: Maybe, like, when we look at it tomorrow, we’ll be in a better shape.
42 00:05:57.480 ⇒ 00:06:11.899 Demilade Agboola: Okay, also, yeah, that would be great. Also, can we also try and see if we can get some of the Kroger data? Because, like they said, Kroger, Walmart, and… can’t remember the Instacart, I believe, make up, like, 50%, so they want to be sure that their major clients are… the numbers look good for them as well.
43 00:06:12.580 ⇒ 00:06:14.309 Ashwini Sharma: Google Data. Yes.
44 00:06:14.310 ⇒ 00:06:17.790 Demilade Agboola: Kroger So that’s K-R-R-O-G-E-R.
45 00:06:18.220 ⇒ 00:06:20.649 Ashwini Sharma: Kroger, but what’s the brand for it?
46 00:06:21.270 ⇒ 00:06:23.639 Demilade Agboola: Kroger, I believe Koga should be the brand.
47 00:06:24.150 ⇒ 00:06:25.790 Ashwini Sharma: Rover is a brand? Yeah.
48 00:06:25.790 ⇒ 00:06:28.520 Demilade Agboola: Yes, Pogo is a band. It’s… it’s a company.
49 00:06:31.610 ⇒ 00:06:36.820 Ashwini Sharma: But it’s not there in the… is it there in the QA data program?
50 00:06:37.710 ⇒ 00:06:40.289 Demilade Agboola: In the QA data, no. Everything is by Magic Spoon.
51 00:06:41.170 ⇒ 00:06:43.450 Ashwini Sharma: No, and they created a…
52 00:06:54.770 ⇒ 00:07:07.490 Demilade Agboola: So, no, no, I don’t think… I don’t think, no, I don’t think they sent the QA data with Kroger in it. You might check, but, like, I’m not sure they did. But I think what they want to do is, if we also aggregate and send it to them, they can also look. But you can check and just confirm.
53 00:07:08.270 ⇒ 00:07:14.680 Demilade Agboola: But I guess this is another way they want to use to see if the numbers that we’re getting can match the numbers they’re getting on the platform.
54 00:07:15.690 ⇒ 00:07:20.890 Ashwini Sharma: Yeah, I’m just… yeah, I’ll have to rerun the pipeline if we need Kroger data.
55 00:07:21.190 ⇒ 00:07:22.330 Demilade Agboola: Okay.
56 00:07:23.070 ⇒ 00:07:30.340 Ashwini Sharma: But what I need is the brand name for the Kroger, right? I don’t know if Kroger itself is a brand.
57 00:07:31.010 ⇒ 00:07:31.750 Demilade Agboola: Yeah, cool guys.
58 00:07:32.390 ⇒ 00:07:35.639 Ashwini Sharma: Kroger is the brand, I’ll show you F… G.
59 00:07:37.650 ⇒ 00:07:45.829 Ashwini Sharma: G-G-H-I-J-K-R-O… No. What’s the spelling? Spell it out, Kroger.
60 00:07:46.760 ⇒ 00:07:48.490 Demilade Agboola: K-R-O-G-R.
61 00:07:48.800 ⇒ 00:07:55.710 Ashwini Sharma: No, it’s not there in the brand. Oh, it’s not there in the… sorry, it’s not there in the QA data, right?
62 00:07:56.210 ⇒ 00:07:59.599 Ashwini Sharma: Let’s see if it is there in the API.
63 00:08:00.280 ⇒ 00:08:01.640 Demilade Agboola: That’s the important thing.
64 00:08:04.930 ⇒ 00:08:05.780 Ashwini Sharma: Any…
65 00:08:11.290 ⇒ 00:08:12.340 Ashwini Sharma: Brad’s…
66 00:10:59.040 ⇒ 00:11:01.969 Ashwini Sharma: No, Kroger’s is not there, it’s not a brand.
67 00:11:06.760 ⇒ 00:11:07.990 Ashwini Sharma: No, not a brand.
68 00:11:08.400 ⇒ 00:11:10.889 Demilade Agboola: Is there a way you can get the list of all the brands that they have?
69 00:11:11.330 ⇒ 00:11:17.939 Ashwini Sharma: Yeah, I can get it. You have to send a request outside, right? And then it’s paginated, so you have to paginate over.
70 00:11:18.710 ⇒ 00:11:22.809 Ashwini Sharma: But it comes in alphabetical order, and Kroger’s is not there in the list.
71 00:11:24.190 ⇒ 00:11:27.010 Demilade Agboola: Okay. Can you say Walmart, or can you say Instacart?
72 00:11:28.860 ⇒ 00:11:31.480 Ashwini Sharma: G-H-I-J-K, right?
73 00:11:34.570 ⇒ 00:11:36.300 Ashwini Sharma: Instacart is right there.
74 00:11:36.970 ⇒ 00:11:38.100 Demilade Agboola: Instacarts is there.
75 00:11:38.540 ⇒ 00:11:39.590 Ashwini Sharma: Let me see.
76 00:11:41.520 ⇒ 00:11:43.040 Ashwini Sharma: INSDA?
77 00:11:43.800 ⇒ 00:11:47.410 Demilade Agboola: I-N-S-T-A-K-C-A-R-T.
78 00:11:50.880 ⇒ 00:11:56.540 Ashwini Sharma: No, I-N-S, right? I-N-C, I-N-D, I-N-F…
79 00:11:56.990 ⇒ 00:12:01.700 Ashwini Sharma: Innate Eno Foods and Inspired International Instacart, no?
80 00:12:01.840 ⇒ 00:12:07.420 Ashwini Sharma: Instacart is also not there. They are not brands. They might be something else, but definitely they are not brands.
81 00:12:07.980 ⇒ 00:12:09.570 Demilade Agboola: Plus one might need to figure out.
82 00:12:14.180 ⇒ 00:12:16.570 Demilade Agboola: Do they have… is there anything called retailer?
83 00:12:19.110 ⇒ 00:12:21.569 Demilade Agboola: Is there a filter for that?
84 00:12:31.520 ⇒ 00:12:36.110 Ashwini Sharma: Hold on a second, I just have to… let me work on that, this thing.
85 00:12:39.160 ⇒ 00:12:43.310 Demilade Agboola: Are you… are you still up for a while? Or, like, are you still working for a while?
86 00:12:44.060 ⇒ 00:12:44.770 Ashwini Sharma: Sorry?
87 00:12:45.020 ⇒ 00:12:46.260 Demilade Agboola: He’s still working for a while.
88 00:12:47.280 ⇒ 00:12:48.210 Ashwini Sharma: Working for?
89 00:12:48.510 ⇒ 00:12:51.299 Demilade Agboola: Are you still working… are you still going to be working for a while, or are you going.
90 00:12:51.300 ⇒ 00:12:54.240 Ashwini Sharma: No, it’s almost like 4 AM in the morning.
91 00:12:54.240 ⇒ 00:12:55.600 Demilade Agboola: Oh, okay, okay.
92 00:12:55.830 ⇒ 00:12:56.460 Ashwini Sharma: Yeah.
93 00:12:56.960 ⇒ 00:13:04.230 Ashwini Sharma: I’ll kind of sleep, and then, I’ll try to get it out tomorrow morning, as soon as I wake up.
94 00:13:05.940 ⇒ 00:13:14.300 Demilade Agboola: Because I just want us to have, like, clarity, because when you wake up, you know, Utan won’t be awake, and I might not probably not be awake as well, so… it’s just like…
95 00:13:14.480 ⇒ 00:13:25.949 Ashwini Sharma: Yeah, let’s, you know, let’s, let me share my screen, and then maybe we can… I can write it out, what exactly we need, right? Okay, alright, sounds good. And, yeah, give me a second.
96 00:13:29.810 ⇒ 00:13:31.300 Ashwini Sharma: Honored.
97 00:13:32.330 ⇒ 00:13:34.830 Ashwini Sharma: So, this is a copy of the…
98 00:13:34.950 ⇒ 00:13:37.549 Ashwini Sharma: That, copy of that file, okay.
99 00:13:37.650 ⇒ 00:13:39.920 Ashwini Sharma: So, what do we need? Basically.
100 00:13:40.440 ⇒ 00:13:43.309 Ashwini Sharma: Let’s try to write it over here, right?
101 00:13:44.400 ⇒ 00:13:48.540 Ashwini Sharma: For a different brand. Brand is only Magic Spoon, right, huh?
102 00:13:51.890 ⇒ 00:13:52.660 Ashwini Sharma: Right.
103 00:13:52.850 ⇒ 00:13:55.989 Ashwini Sharma: What do we need is at the geography level, right?
104 00:13:58.170 ⇒ 00:13:58.740 Demilade Agboola: Yeah.
105 00:14:00.670 ⇒ 00:14:04.220 Ashwini Sharma: Let’s say geography equals to… let me pull one geography out of here.
106 00:14:04.610 ⇒ 00:14:06.210 Ashwini Sharma: Let’s hit this one, right?
107 00:14:09.740 ⇒ 00:14:10.360 Demilade Agboola: so…
108 00:14:10.360 ⇒ 00:14:11.910 Ashwini Sharma: There’s an article.
109 00:14:12.060 ⇒ 00:14:15.740 Ashwini Sharma: Yeah, what do we need here? Different categories, right?
110 00:14:15.740 ⇒ 00:14:18.880 Demilade Agboola: Different… different categories, yeah. So, like, wellness…
111 00:14:25.830 ⇒ 00:14:32.850 Demilade Agboola: Different geography… level…
112 00:14:36.790 ⇒ 00:14:38.320 Ashwini Sharma: Is this… is this fine?
113 00:14:40.160 ⇒ 00:14:43.519 Demilade Agboola: So that would be the sum of everything across that, yeah.
114 00:14:44.080 ⇒ 00:14:46.380 Ashwini Sharma: This is what we need, like, so basically.
115 00:14:46.620 ⇒ 00:14:50.569 Ashwini Sharma: Category… let’s pull something, right? Category is this one.
116 00:14:54.570 ⇒ 00:15:00.049 Ashwini Sharma: And then… Subcategory is this one.
117 00:15:06.670 ⇒ 00:15:10.770 Ashwini Sharma: Or, basically, are we trying to, you know, sum up everything under…
118 00:15:10.960 ⇒ 00:15:13.849 Ashwini Sharma: Maybe we should remove this subcategory, right?
119 00:15:14.330 ⇒ 00:15:17.199 Demilade Agboola: Yeah, let’s remove the subcategory.
120 00:15:25.770 ⇒ 00:15:39.649 Ashwini Sharma: brand message, geography, this one. So, basically, we are aggregating for a single geography, and then, we’re trying to say, okay, for this category, what will be the sum of 4 weeks, what will be the sum of 12 weeks, 24 weeks, 52 weeks, right?
121 00:15:39.820 ⇒ 00:15:41.780 Demilade Agboola: Yes. That is what we are trying to do, right?
122 00:15:41.900 ⇒ 00:15:46.599 Ashwini Sharma: I don’t know why this is, like, such a big deal, right?
123 00:15:47.010 ⇒ 00:15:50.480 Ashwini Sharma: We’re looking at this one, right? So, let’s say…
124 00:15:53.320 ⇒ 00:15:58.410 Ashwini Sharma: Alright, now we’re just looking at this one. Do we also need it at product level?
125 00:16:00.960 ⇒ 00:16:07.569 Demilade Agboola: Yes, well, product level, I’m not sure. But they mentioned geography level as well, so let’s do geography level, so markets, whatever that is.
126 00:16:09.480 ⇒ 00:16:11.660 Ashwini Sharma: Geography level is just market, right?
127 00:16:12.870 ⇒ 00:16:14.059 Demilade Agboola: No, none of the Elva stuff.
128 00:16:17.790 ⇒ 00:16:22.750 Demilade Agboola: There’s market region, there’s market region, census, census region, total US.
129 00:16:23.230 ⇒ 00:16:29.880 Ashwini Sharma: No, this is the only one, right? Geography level, there… see, when I selected a geography, it tells us, okay, food.
130 00:16:30.130 ⇒ 00:16:32.180 Ashwini Sharma: It’s just, market.
131 00:16:32.180 ⇒ 00:16:34.650 Demilade Agboola: Yes, but that’s for… that’s for Tulsa.
132 00:16:34.890 ⇒ 00:16:35.510 Ashwini Sharma: Yeah.
133 00:16:36.800 ⇒ 00:16:38.950 Ashwini Sharma: Do we want to aggregate it at geography level?
134 00:16:39.360 ⇒ 00:16:40.130 Demilade Agboola: Yes.
135 00:16:40.540 ⇒ 00:16:41.470 Demilade Agboola: as well.
136 00:16:43.380 ⇒ 00:16:46.269 Ashwini Sharma: Okay, this is what he has written over here, right? Okay.
137 00:16:46.600 ⇒ 00:16:48.670 Ashwini Sharma: So let me sell it all.
138 00:16:49.440 ⇒ 00:16:51.770 Ashwini Sharma: Let’s do just market, right?
139 00:16:53.560 ⇒ 00:16:54.500 Ashwini Sharma: Okay.
140 00:16:54.900 ⇒ 00:16:55.680 Ashwini Sharma: No?
141 00:17:00.330 ⇒ 00:17:01.420 Ashwini Sharma: Oh, come on.
142 00:17:03.610 ⇒ 00:17:04.390 Ashwini Sharma: Yeah.
143 00:17:04.770 ⇒ 00:17:09.260 Ashwini Sharma: Region, census region, total U.S. These are, what, different geographies?
144 00:17:09.740 ⇒ 00:17:10.869 Demilade Agboola: geographic level.
145 00:17:10.869 ⇒ 00:17:13.600 Ashwini Sharma: Geographic score level. Yeah, geographic level, okay.
146 00:17:24.480 ⇒ 00:17:25.639 Ashwini Sharma: What next?
147 00:17:28.170 ⇒ 00:17:32.120 Demilade Agboola: Can you put in that sheet that we want to aggregate by geography level as well?
148 00:17:32.580 ⇒ 00:17:33.340 Ashwini Sharma: Okay.
149 00:17:36.530 ⇒ 00:17:42.289 Demilade Agboola: No, no, we’re going to agree by geography as well as geography level. They’re not the same, they’re not the same cut.
150 00:17:49.190 ⇒ 00:17:49.940 Ashwini Sharma: Alright.
151 00:17:55.320 ⇒ 00:17:56.270 Ashwini Sharma: And then?
152 00:18:03.390 ⇒ 00:18:06.000 Demilade Agboola: Okay, so yes, once we have that…
153 00:18:08.210 ⇒ 00:18:13.539 Demilade Agboola: I would like us to figure out where this Kroger data is, because I know they mentioned it.
154 00:18:16.030 ⇒ 00:18:18.449 Demilade Agboola: I’m not sure where the hell it will be.
155 00:18:21.360 ⇒ 00:18:24.680 Ashwini Sharma: Let me drop in a message to Utam, or maybe in the channel.
156 00:18:30.570 ⇒ 00:18:32.930 Demilade Agboola: Yeah, so, like, number one is what we’re talking about.
157 00:18:36.740 ⇒ 00:18:38.320 Demilade Agboola: So, did you see Tom’s message?
158 00:18:40.000 ⇒ 00:18:41.440 Ashwini Sharma: Sorry, one second.
159 00:18:44.860 ⇒ 00:18:54.769 Ashwini Sharma: Can we get those aggregations over to them? As I said, we just need to show 4-week aggregations at the geo and whatever other levels. Yeah, this is what he’s saying, right? What does it mean?
160 00:18:56.670 ⇒ 00:19:00.460 Demilade Agboola: Yeah, that’s what… that’s what we’re talking about right now. That’s what I’m saying… that’s what I’m saying.
161 00:19:01.320 ⇒ 00:19:04.950 Demilade Agboola: So, just aggregate the Magic Spoon data.
162 00:19:05.340 ⇒ 00:19:07.950 Ashwini Sharma: Or, like, pull it. I don’t know if you need to pull it again.
163 00:19:07.950 ⇒ 00:19:13.840 Demilade Agboola: But you need to pull it from the Spins API. Actually, no, can you pull… is it possible for you to pull it from the Spins API?
164 00:19:14.350 ⇒ 00:19:18.400 Demilade Agboola: And just sum it by the geo level.
165 00:19:18.970 ⇒ 00:19:26.250 Demilade Agboola: So not granular. Just go directly to it, this means API, and get…
166 00:19:26.380 ⇒ 00:19:32.119 Demilade Agboola: for Magic Spoon, what the market level 4 weeks, 24 weeks, and…
167 00:19:32.480 ⇒ 00:19:46.590 Ashwini Sharma: like, what the different geography is for those… No, but that is what we are going to derive, right? So, we can get it. I just want to, you know, some basic questions over here, right? So, when my geo level is market.
168 00:19:46.710 ⇒ 00:19:47.440 Ashwini Sharma: Right?
169 00:19:48.570 ⇒ 00:19:54.199 Ashwini Sharma: what data am I showing? Am I showing data for all the geographies that fall under market?
170 00:19:54.410 ⇒ 00:20:00.670 Demilade Agboola: No. You’re just showing it for markets. You’re showing… I believe what they want to see is the sum.
171 00:20:01.720 ⇒ 00:20:10.450 Demilade Agboola: Over… of markets, some of sensors, some of that, like… the same way when you’re going to do for a category, you’re doing wellness and snacks.
172 00:20:10.450 ⇒ 00:20:16.030 Ashwini Sharma: So, this is what happens, right? See, right now, what we are seeing is market, right?
173 00:20:16.640 ⇒ 00:20:18.339 Ashwini Sharma: Okay, this is all market.
174 00:20:20.460 ⇒ 00:20:21.340 Ashwini Sharma: Alright.
175 00:20:21.470 ⇒ 00:20:22.430 Ashwini Sharma: No.
176 00:20:22.710 ⇒ 00:20:29.280 Ashwini Sharma: This is all market. Or maybe, hold on a second, right? Let’s go to this one. Okay?
177 00:20:33.700 ⇒ 00:20:36.909 Ashwini Sharma: Yeah, maybe I can create a pivot table out of this one.
178 00:20:38.480 ⇒ 00:20:42.109 Ashwini Sharma: And this is not Excel, right? So, CVS…
179 00:20:50.280 ⇒ 00:20:51.500 Ashwini Sharma: Excellent.
180 00:21:01.260 ⇒ 00:21:02.440 Ashwini Sharma: Oh, come on.
181 00:21:07.230 ⇒ 00:21:09.400 Ashwini Sharma: Alright, this is all Excel now.
182 00:21:15.510 ⇒ 00:21:19.329 Ashwini Sharma: Mmm… How do I create a new pivot table?
183 00:22:19.720 ⇒ 00:22:21.209 Ashwini Sharma: Why? It didn’t select?
184 00:22:27.320 ⇒ 00:22:28.300 Ashwini Sharma: Oh.
185 00:23:10.880 ⇒ 00:23:11.660 Ashwini Sharma: Alright.
186 00:23:12.480 ⇒ 00:23:15.230 Ashwini Sharma: So we want to do it at what,
187 00:23:18.330 ⇒ 00:23:19.920 Ashwini Sharma: Where is geography level?
188 00:24:12.170 ⇒ 00:24:13.819 Ashwini Sharma: Is this what we are trying to do?
189 00:24:16.200 ⇒ 00:24:19.419 Demilade Agboola: Sure, I’m trying to, put some follow-up message in here.
190 00:24:26.160 ⇒ 00:24:27.680 Demilade Agboola: No.
191 00:24:27.870 ⇒ 00:24:36.130 Demilade Agboola: Oh, yes, but for, like, the different… For the different, geo… What’s it called?
192 00:24:37.160 ⇒ 00:24:41.640 Demilade Agboola: Different geographies, so it would… Short every single geography.
193 00:24:42.500 ⇒ 00:24:47.089 Ashwini Sharma: Yeah, so why did it not return to a level over here?
194 00:24:48.070 ⇒ 00:24:53.670 Ashwini Sharma: Oh, I didn’t select it, huh? Okay, hold on a second, hold on a second.
195 00:25:05.930 ⇒ 00:25:09.420 Ashwini Sharma: Okay, hold on a second, sorry.
196 00:25:13.230 ⇒ 00:25:14.710 Ashwini Sharma: How do I edit this?
197 00:25:17.440 ⇒ 00:25:18.370 Ashwini Sharma: Alright.
198 00:25:19.050 ⇒ 00:25:27.200 Ashwini Sharma: Insert… pivot table… So, you go up from here.
199 00:25:27.740 ⇒ 00:25:28.770 Ashwini Sharma: Yeah…
200 00:25:40.590 ⇒ 00:25:48.800 Demilade Agboola: Sorry, just out of curiosity, is it possible to pull, like, the summation of these things, like, from the API, or does the API have to give you
201 00:25:49.030 ⇒ 00:25:51.570 Demilade Agboola: Like, granular data this way.
202 00:25:53.290 ⇒ 00:25:55.729 Ashwini Sharma: Api will give me granular data.
203 00:25:56.100 ⇒ 00:26:00.380 Demilade Agboola: Okay. Alright, so the only way we can get this is by summing it this way. That’s what we’re trying to say.
204 00:26:01.560 ⇒ 00:26:09.599 Ashwini Sharma: Yeah, so now, now we have, right? Okay, so what we need is geography level, right? And then you also need a geography filter, right?
205 00:26:09.780 ⇒ 00:26:12.040 Ashwini Sharma: No. What else do we need?
206 00:26:12.870 ⇒ 00:26:15.879 Ashwini Sharma: Do we need a category filter also? There you go.
207 00:26:16.430 ⇒ 00:26:20.859 Ashwini Sharma: And what we need to sum up is Pence dollar, right?
208 00:26:21.320 ⇒ 00:26:25.360 Ashwini Sharma: And, what else is it?
209 00:26:26.090 ⇒ 00:26:27.150 Demilade Agboola: the units.
210 00:26:27.490 ⇒ 00:26:29.559 Ashwini Sharma: PF dollars,
211 00:26:30.280 ⇒ 00:26:32.499 Demilade Agboola: We’re not using PF dollars.
212 00:26:32.830 ⇒ 00:26:33.240 Ashwini Sharma: Yeah.
213 00:26:34.460 ⇒ 00:26:41.150 Ashwini Sharma: So, basically, this is… Sort of, like, some of, dollars from spins, and some of dollars from platform.
214 00:26:41.580 ⇒ 00:26:42.389 Demilade Agboola: That’s really neat.
215 00:26:42.480 ⇒ 00:26:44.300 Ashwini Sharma: This is not what we need to send to them.
216 00:26:46.450 ⇒ 00:26:51.569 Demilade Agboola: Like, the numbers, yes, but not how this is not the format in which they want to see it.
217 00:26:51.900 ⇒ 00:26:53.350 Demilade Agboola: What they want to see.
218 00:26:53.750 ⇒ 00:26:59.000 Demilade Agboola: What they want to see is a list Of every single geography level.
219 00:26:59.930 ⇒ 00:27:05.170 Demilade Agboola: Right? And then the sum on… as, like, a value for each row.
220 00:27:05.630 ⇒ 00:27:11.740 Demilade Agboola: for Magic Spoon. Then, for geography, a list of all the geographies and their sums.
221 00:27:12.200 ⇒ 00:27:20.869 Demilade Agboola: And then the list of categories, and then sums for each row. So that way, it’s easier for them to go into and crash-track and evaluate
222 00:27:21.070 ⇒ 00:27:23.549 Demilade Agboola: Spins data versus this.
223 00:27:24.540 ⇒ 00:27:26.280 Demilade Agboola: Versus what they have on the platform.
224 00:27:27.210 ⇒ 00:27:30.730 Demilade Agboola: Because the idea is they want to send it to Heather. Heather has someone on their team.
225 00:27:30.970 ⇒ 00:27:42.070 Demilade Agboola: So we need to give them in a way the header can look at, and actually just go through random ones and select and confirm. Or if she wants to aggregate it and just sum it up, we can also put the sum at the bottom.
226 00:27:44.340 ⇒ 00:27:50.310 Ashwini Sharma: Okay, so what you’re saying is, like, I create one sheet, right? Let’s go over here.
227 00:27:52.910 ⇒ 00:27:56.549 Ashwini Sharma: Let’s add one sheet, right? Now, this is geo-level.
228 00:27:58.450 ⇒ 00:27:59.200 Ashwini Sharma: Bye.
229 00:27:59.800 ⇒ 00:28:00.570 Ashwini Sharma: So…
230 00:28:12.790 ⇒ 00:28:14.080 Ashwini Sharma: This is what you’re saying?
231 00:28:15.090 ⇒ 00:28:15.760 Demilade Agboola: Yeah.
232 00:28:15.910 ⇒ 00:28:18.959 Demilade Agboola: And then the list of every single geo-level should be here.
233 00:28:19.670 ⇒ 00:28:25.520 Ashwini Sharma: Yeah, every single geo-level over here, and then sum it across geo-level. Now, there will be another sheet, which is…
234 00:28:25.860 ⇒ 00:28:27.390 Ashwini Sharma: Geography, right?
235 00:28:28.030 ⇒ 00:28:28.580 Demilade Agboola: Yeah.
236 00:28:29.160 ⇒ 00:28:32.169 Ashwini Sharma: So for against each geography, I just have to.
237 00:28:33.020 ⇒ 00:28:34.240 Demilade Agboola: For the song, exactly.
238 00:28:34.240 ⇒ 00:28:39.519 Ashwini Sharma: Alright, this is some… Is this what you’re talking about?
239 00:28:40.130 ⇒ 00:28:43.339 Demilade Agboola: Yeah, and then category as well, just, like, so…
240 00:28:45.500 ⇒ 00:28:46.480 Ashwini Sharma: Getty, yaddy.
241 00:28:47.140 ⇒ 00:28:50.909 Demilade Agboola: And then at the bottom of each thing, you can then put the sum.
242 00:28:51.310 ⇒ 00:28:55.050 Demilade Agboola: of… the PF dollars, so, like, that you could have the.
243 00:28:55.050 ⇒ 00:28:58.049 Ashwini Sharma: Where does this thing come into now?
244 00:28:58.660 ⇒ 00:29:00.460 Ashwini Sharma: 4 weeks, this thing.
245 00:29:01.280 ⇒ 00:29:01.970 Ashwini Sharma: Over here?
246 00:29:06.510 ⇒ 00:29:07.310 Demilade Agboola: Yeah.
247 00:29:09.980 ⇒ 00:29:13.709 Ashwini Sharma: This is, let’s say, market, right? So this is 4 weeks?
248 00:29:14.130 ⇒ 00:29:15.220 Demilade Agboola: Exactly.
249 00:29:15.410 ⇒ 00:29:15.890 Ashwini Sharma: Again, huh.
250 00:29:15.890 ⇒ 00:29:19.989 Demilade Agboola: at the game, and then… Cool, yeah.
251 00:29:24.590 ⇒ 00:29:25.260 Ashwini Sharma: Right?
252 00:29:25.720 ⇒ 00:29:26.330 Demilade Agboola: Yeah.
253 00:29:30.460 ⇒ 00:29:32.209 Ashwini Sharma: This is… this is what we need to populate?
254 00:29:32.650 ⇒ 00:29:33.290 Demilade Agboola: Yeah.
255 00:29:34.570 ⇒ 00:29:41.930 Ashwini Sharma: Okay, let’s confirm with Utam as well, right? I’ll just put this document in the…
256 00:29:42.180 ⇒ 00:29:48.690 Ashwini Sharma: Not document, but, I’ll just put a bunch of screenshots over here, and…
257 00:30:19.500 ⇒ 00:30:28.780 Ashwini Sharma: The aggregation geography… Alright, let me pull one data, just to have example, right?
258 00:30:43.930 ⇒ 00:30:46.100 Ashwini Sharma: And there should be a time frame also, right?
259 00:31:03.550 ⇒ 00:31:06.569 Ashwini Sharma: And then some varies over here, and then another geography, right?
260 00:31:08.460 ⇒ 00:31:09.100 Demilade Agboola: Yeah.
261 00:31:16.220 ⇒ 00:31:16.900 Ashwini Sharma: Okay.
262 00:31:32.060 ⇒ 00:31:34.750 Demilade Agboola: Can you tap Otam specifically, so he can see it?
263 00:31:34.750 ⇒ 00:31:37.190 Ashwini Sharma: Yeah, he’s about to type something.
264 00:31:54.740 ⇒ 00:32:01.480 Ashwini Sharma: I don’t know, it makes sense to have this thing, right? It’s better to have a pivot table which can do all of these things.
265 00:32:03.050 ⇒ 00:32:04.090 Ashwini Sharma: What do you think?
266 00:32:05.270 ⇒ 00:32:07.450 Demilade Agboola: Yes, the paper, though, was easier to show.
267 00:32:08.260 ⇒ 00:32:14.089 Demilade Agboola: It’s easier to show the final values, yes, but it’s harder for them to debug. That’s the problem.
268 00:32:14.290 ⇒ 00:32:16.589 Demilade Agboola: So if they were to see something looks off.
269 00:32:16.980 ⇒ 00:32:20.919 Demilade Agboola: They can’t really see where things look off from.
270 00:32:30.990 ⇒ 00:32:33.809 Ashwini Sharma: Let me share this Excel sheet with Otam.
271 00:33:00.040 ⇒ 00:33:02.000 Ashwini Sharma: One sheet per aggregation.
272 00:33:05.110 ⇒ 00:33:06.620 Ashwini Sharma: Oh, man, okay.
273 00:33:26.010 ⇒ 00:33:30.720 Ashwini Sharma: Alright, I think I got it, Demilhardi, what we want to send. I’ll send it tomorrow morning now.
274 00:33:30.900 ⇒ 00:33:32.420 Ashwini Sharma: I’ll just mention this.
275 00:33:33.410 ⇒ 00:33:34.630 Demilade Agboola: Oh, I see.
276 00:33:37.150 ⇒ 00:33:39.080 Ashwini Sharma: It’s already morning for me, man.
277 00:33:39.810 ⇒ 00:33:44.469 Demilade Agboola: Yeah, I guess… so, like, This is… this is,
278 00:33:49.040 ⇒ 00:33:53.709 Demilade Agboola: Yeah, so this is, yeah, basically what we want to send to the client right now.
279 00:33:54.340 ⇒ 00:34:00.720 Demilade Agboola: I know what time is trying to say we should sign in tonight, but can you just flag that, like, it’s really late for you?
280 00:34:01.360 ⇒ 00:34:04.729 Demilade Agboola: So that he doesn’t have any expectations.
281 00:34:04.730 ⇒ 00:34:05.730 Ashwini Sharma: Yeah, yeah.
282 00:34:07.380 ⇒ 00:34:10.579 Demilade Agboola: And you’ll do it first thing tomorrow morning, so it’s very clear to him.
283 00:34:53.719 ⇒ 00:34:57.220 Ashwini Sharma: Alright, let’s create a copy of this thing.
284 00:35:22.820 ⇒ 00:35:25.250 Ashwini Sharma: Maybe I should not have done that, huh?
285 00:35:25.420 ⇒ 00:35:26.969 Ashwini Sharma: Let’s create a new…
286 00:35:32.920 ⇒ 00:35:34.260 Ashwini Sharma: Oh…
287 00:36:00.710 ⇒ 00:36:01.560 Ashwini Sharma: Alright.
288 00:36:01.710 ⇒ 00:36:04.919 Ashwini Sharma: what changes he had done? This was the one, right?
289 00:36:08.290 ⇒ 00:36:10.740 Ashwini Sharma: Oh, no, this is not going to work, buddy.
290 00:41:33.910 ⇒ 00:41:38.049 Demilade Agboola: Okay, so Tom says he can work with this, so, that’s fine. We can just…
291 00:42:01.190 ⇒ 00:42:07.399 Ashwini Sharma: See, it’ll be easier this way, right, if we do it this way, right? So, now I just did it by geography level, right?
292 00:42:07.540 ⇒ 00:42:10.119 Ashwini Sharma: I can do it by geography.
293 00:42:10.760 ⇒ 00:42:11.450 Ashwini Sharma: Alright.
294 00:42:14.150 ⇒ 00:42:17.320 Ashwini Sharma: Geography, and then, maybe order that?
295 00:42:18.530 ⇒ 00:42:26.600 Ashwini Sharma: Spins, spends dollars, which spins dollars, and… This, right?
296 00:42:28.960 ⇒ 00:42:32.370 Ashwini Sharma: This is based on… Geography, right?
297 00:42:33.040 ⇒ 00:42:33.620 Demilade Agboola: Yeah.
298 00:42:34.100 ⇒ 00:42:34.880 Ashwini Sharma: And…
299 00:42:35.950 ⇒ 00:42:36.730 Demilade Agboola: time frame.
300 00:42:37.070 ⇒ 00:42:42.360 Ashwini Sharma: Maybe, I can add another pivot table that’s based on category, right?
301 00:42:43.030 ⇒ 00:42:48.170 Demilade Agboola: Yeah, so category can work. Can you also put the time frame as well, the different time periods?
302 00:42:48.340 ⇒ 00:42:52.100 Ashwini Sharma: Yes, yes, I think that was missing over here.
303 00:43:02.340 ⇒ 00:43:04.429 Demilade Agboola: Can you get rid of the other ones up top?
304 00:43:04.430 ⇒ 00:43:11.660 Ashwini Sharma: Okay, let me delete these things, right? I think it’s easier to… Do it that way.
305 00:43:19.410 ⇒ 00:43:20.260 Ashwini Sharma: Alright.
306 00:43:21.190 ⇒ 00:43:24.740 Ashwini Sharma: Now let’s add a pivot table with this range.
307 00:43:26.820 ⇒ 00:43:34.660 Ashwini Sharma: Alright, we want… Geography level, and… time period.
308 00:43:34.920 ⇒ 00:43:35.660 Ashwini Sharma: Right?
309 00:43:36.510 ⇒ 00:43:37.160 Demilade Agboola: Yeah.
310 00:43:37.870 ⇒ 00:43:42.540 Ashwini Sharma: And we need this summation of this thing.
311 00:43:42.720 ⇒ 00:43:44.450 Ashwini Sharma: And spin staller.
312 00:43:46.330 ⇒ 00:43:47.770 Ashwini Sharma: Yeah, is this good?
313 00:43:50.330 ⇒ 00:43:54.280 Demilade Agboola: Yes.
314 00:43:55.810 ⇒ 00:43:58.220 Demilade Agboola: Is it possible to make a rose instead?
315 00:43:59.670 ⇒ 00:44:00.230 Ashwini Sharma: What?
316 00:44:00.830 ⇒ 00:44:02.960 Demilade Agboola: Is it possible… can I go back to the test?
317 00:44:03.450 ⇒ 00:44:06.450 Demilade Agboola: So this was supposed to make it H1A row, so, like…
318 00:44:08.040 ⇒ 00:44:08.550 Ashwini Sharma: Visible.
319 00:44:09.960 ⇒ 00:44:14.179 Demilade Agboola: Yeah, but it’s… so, TotalUS has, like, 4 rows underneath it, right?
320 00:44:15.710 ⇒ 00:44:17.840 Ashwini Sharma: Do you want me to make these columns?
321 00:44:20.070 ⇒ 00:44:21.560 Demilade Agboola: Mmm…
322 00:44:23.270 ⇒ 00:44:26.680 Demilade Agboola: Okay, let’s look at… let’s leave it like this for now. Let’s see.
323 00:44:27.000 ⇒ 00:44:32.460 Ashwini Sharma: The geography one is going to be even more, I don’t know, maybe, maybe, let’s see…
324 00:44:32.460 ⇒ 00:44:36.639 Demilade Agboola: I’m thinking… I’m thinking… Okay, sure, let’s, listen.
325 00:44:38.260 ⇒ 00:44:41.210 Ashwini Sharma: Right, now, here it’s going to be geography.
326 00:44:41.320 ⇒ 00:44:42.170 Ashwini Sharma: Right?
327 00:44:42.620 ⇒ 00:44:48.490 Ashwini Sharma: Okay, maybe, like, for the time period, let me add it over here, right?
328 00:44:48.640 ⇒ 00:44:52.839 Ashwini Sharma: And then I can do, spins dollars and PF dollars, right?
329 00:44:55.820 ⇒ 00:44:56.810 Ashwini Sharma: Like this?
330 00:44:59.130 ⇒ 00:45:05.320 Demilade Agboola: Mmm, no, this is too confusing. Looks weird.
331 00:45:06.270 ⇒ 00:45:13.680 Ashwini Sharma: Yeah, geography is going to look weird, right? I don’t know why they wanted to do it this way, but maybe if you select only a certain geography, right?
332 00:45:14.680 ⇒ 00:45:15.580 Ashwini Sharma: You can see…
333 00:45:15.580 ⇒ 00:45:19.829 Demilade Agboola: Yeah, even with that, it still looks… it’s all… it’s a bit confusing.
334 00:45:21.430 ⇒ 00:45:24.050 Demilade Agboola: That’s sort of what I’m saying. Like, it’s hard to tell…
335 00:45:25.000 ⇒ 00:45:27.020 Demilade Agboola: how to read it, that’s what I’m trying to say.
336 00:45:27.020 ⇒ 00:45:29.600 Ashwini Sharma: I’ll just send this Excel sheet over them.
337 00:45:38.360 ⇒ 00:45:41.470 Ashwini Sharma: I need to get some sleep, man, it’s,
338 00:45:45.230 ⇒ 00:45:47.360 Ashwini Sharma: Kind of too late for me today.
339 00:45:48.330 ⇒ 00:45:49.140 Demilade Agboola: Yeah, that’s fine.
340 00:45:50.280 ⇒ 00:45:52.109 Ashwini Sharma: Normally, how long do you work?
341 00:45:54.120 ⇒ 00:46:00.180 Demilade Agboola: It depends on the day. So right now, for me, it is… 1148.
342 00:46:00.490 ⇒ 00:46:05.229 Demilade Agboola: So it depends on the day. Some days I can work till, like, 2 o’clock, maybe 3.
343 00:46:06.040 ⇒ 00:46:15.129 Demilade Agboola: But on some days, I don’t, like, I just will shut down, like, at 12, like, midnight. It depends on… on how much work there is to be done.
344 00:46:15.820 ⇒ 00:46:24.000 Demilade Agboola: And what the pressure is. I think for Magic Spoon, Utam really wants to have, like, a good impression today, so I will try and stay up a little bit late to get some stuff done.
345 00:46:24.980 ⇒ 00:46:28.799 Ashwini Sharma: Yeah, it’s already… in another 2 hours, my kids will be up.
346 00:46:30.530 ⇒ 00:46:33.270 Demilade Agboola: Yeah, yeah, that’ll be… that’ll be a tough one.
347 00:47:00.410 ⇒ 00:47:06.069 Ashwini Sharma: It’s going to… I don’t know how they’re going to look at this data, because it’s really confusing, right?
348 00:47:09.190 ⇒ 00:47:15.079 Demilade Agboola: Yeah, I think this one… this is maybe a bit too… that’s why I was saying, like, the structure is a bit too much, in the sense of…
349 00:47:15.860 ⇒ 00:47:19.579 Ashwini Sharma: If I add, make, make this rose, right? This thing?
350 00:47:19.990 ⇒ 00:47:25.170 Ashwini Sharma: just like what I did for geo-level, it’s going to be further confusing, right? This one.
351 00:47:30.670 ⇒ 00:47:34.049 Ashwini Sharma: Yeah, if I make it like this, it’s going to be further confusing, right?
352 00:47:37.380 ⇒ 00:47:38.320 Ashwini Sharma: Very both.
353 00:47:56.930 ⇒ 00:47:57.670 Ashwini Sharma: Mmm…
354 00:48:11.190 ⇒ 00:48:13.390 Ashwini Sharma: The category would be further audible.
355 00:48:24.720 ⇒ 00:48:29.970 Ashwini Sharma: Oh, category is better, because there is only two categories that I have, right?
356 00:48:30.750 ⇒ 00:48:32.999 Demilade Agboola: Yeah, so can you, can we make your rose, please?
357 00:48:42.040 ⇒ 00:48:45.890 Demilade Agboola: Can you make geography rows? Let’s just… let’s just see what it looks like.
358 00:48:46.080 ⇒ 00:48:52.109 Demilade Agboola: I think it might just be easier. Yes, it’s going to be very long, yes, but I think it’s easier to read.
359 00:48:53.540 ⇒ 00:48:54.200 Ashwini Sharma: Doctor?
360 00:48:55.010 ⇒ 00:48:57.050 Demilade Agboola: It’s long, yes, but it’s easier to read.
361 00:49:01.950 ⇒ 00:49:03.660 Demilade Agboola: Yeah, I think we should leave it this way.
362 00:49:04.760 ⇒ 00:49:11.550 Ashwini Sharma: Alright, let’s see what Gotham wants to do with it, yeah.
363 00:49:15.540 ⇒ 00:49:17.490 Ashwini Sharma: Let me, let me send it again.
364 00:49:46.670 ⇒ 00:49:51.850 Ashwini Sharma: Excel will not convert to this one, right? Google Sheet, directly?
365 00:49:54.420 ⇒ 00:50:00.399 Demilade Agboola: I’m not sure, I don’t… I think you lose some of the… the pivots and stuff.
366 00:50:03.340 ⇒ 00:50:04.260 Ashwini Sharma: Let’s see…
367 00:50:41.070 ⇒ 00:50:47.690 Ashwini Sharma: No, it didn’t give a pivot, but it gave it in a… This format.
368 00:50:48.530 ⇒ 00:50:49.560 Demilade Agboola: Yeah, it’s…
369 00:51:52.490 ⇒ 00:51:53.699 Ashwini Sharma: Does this look good?
370 00:51:55.610 ⇒ 00:51:57.289 Demilade Agboola: Yeah, yeah, this is fine.
371 00:52:06.790 ⇒ 00:52:11.280 Demilade Agboola: maybe we can add, like, commas, that’s the only thing I’ll say, so it’s easier to read, but yeah.
372 00:53:08.340 ⇒ 00:53:09.160 Ashwini Sharma: Alright.
373 00:53:20.610 ⇒ 00:53:21.729 Ashwini Sharma: This is the word.
374 00:53:24.360 ⇒ 00:53:25.690 Ashwini Sharma: Let me delete this.
375 00:54:20.590 ⇒ 00:54:22.619 Ashwini Sharma: Alright, I think this is good enough, right?
376 00:54:24.040 ⇒ 00:54:25.570 Demilade Agboola: Yeah. Yeah, thank you.
377 00:54:50.360 ⇒ 00:54:52.359 Ashwini Sharma: I don’t think this total is needed.
378 00:54:53.520 ⇒ 00:54:58.909 Ashwini Sharma: Grand total, right? Grand total might not be needed. Anyways, Utham can figure that out here.
379 00:54:59.200 ⇒ 00:55:01.780 Demilade Agboola: Yeah, Granted, I don’t think Grand Salt will be needed, but it’s fine.
380 00:55:01.780 ⇒ 00:55:04.119 Ashwini Sharma: Yeah, it doesn’t make sense to summit over, like.
381 00:55:04.120 ⇒ 00:55:05.620 Demilade Agboola: Right, yeah, yeah.
382 00:55:08.000 ⇒ 00:55:13.979 Ashwini Sharma: On that, man, I’ll stop now. It’s terribly late. It’s 4.30 in the morning, yeah.
383 00:55:14.840 ⇒ 00:55:15.680 Demilade Agboola: No problem. Take care, yourself.
384 00:55:15.980 ⇒ 00:55:16.790 Ashwini Sharma: Take care. Bye.