Meeting Title: Magic Spoon SPINS sync Date: 2026-01-28 Meeting participants: Uttam Kumaran, Ashwini Sharma, Demilade Agboola, Awaish Kumar
WEBVTT
1 00:00:38.020 ⇒ 00:00:38.800 Demilade Agboola: Hello.
2 00:00:39.300 ⇒ 00:00:40.300 Uttam Kumaran: Hey, guys.
3 00:00:40.550 ⇒ 00:00:44.480 Uttam Kumaran: I guess maybe we can start… can I… can you walk me through getting just, like, Redshift?
4 00:00:44.630 ⇒ 00:00:48.000 Uttam Kumaran: access as we need? Can I just use your credentials?
5 00:00:48.140 ⇒ 00:00:51.770 Ashwini Sharma: Yeah, yeah, you can. It should be there in this thing.
6 00:00:51.770 ⇒ 00:00:53.010 Uttam Kumaran: Google Plus or something?
7 00:00:53.490 ⇒ 00:00:54.740 Ashwini Sharma: Dbeaver.
8 00:00:55.580 ⇒ 00:00:56.489 Uttam Kumaran: Okay, alright.
9 00:00:58.340 ⇒ 00:01:02.140 Ashwini Sharma: And it should be there in one pass somewhere. Let me just check here.
10 00:01:12.790 ⇒ 00:01:18.250 Uttam Kumaran: Magic Spoon… Yeah, database? Okay, cool.
11 00:01:19.860 ⇒ 00:01:23.689 Awaish Kumar: Okay, I… I think I didn’t understand, like, do you need me here?
12 00:01:23.690 ⇒ 00:01:26.849 Uttam Kumaran: No, no, no, I don’t need it here. Okay.
13 00:01:27.190 ⇒ 00:01:27.940 Uttam Kumaran: Thanks.
14 00:01:29.030 ⇒ 00:01:31.959 Uttam Kumaran: Alright, let me just get in first, and then I can at least…
15 00:01:31.960 ⇒ 00:01:34.739 Ashwini Sharma: Magic spoon, magic spoon.
16 00:01:37.750 ⇒ 00:01:40.940 Ashwini Sharma: Did you get the… Password?
17 00:01:41.130 ⇒ 00:01:42.300 Uttam Kumaran: Yeah, I did.
18 00:01:42.620 ⇒ 00:01:43.300 Ashwini Sharma: Okay.
19 00:01:43.990 ⇒ 00:01:45.550 Uttam Kumaran: I’m just gonna see…
20 00:01:50.310 ⇒ 00:01:51.519 Uttam Kumaran: It’s working.
21 00:02:01.440 ⇒ 00:02:03.020 Uttam Kumaran: Okay, yeah, alright.
22 00:02:03.560 ⇒ 00:02:11.880 Uttam Kumaran: Cool, so… Can we… yeah, can we… can you walk me through, Ashwini? Let’s start first with,
23 00:02:12.270 ⇒ 00:02:14.060 Uttam Kumaran: the QA dataset?
24 00:02:14.570 ⇒ 00:02:17.139 Uttam Kumaran: Can we just watch? Can you… Should I share my mic?
25 00:02:17.140 ⇒ 00:02:17.970 Ashwini Sharma: screen, or…
26 00:02:17.970 ⇒ 00:02:20.950 Uttam Kumaran: Yeah, if you want to share… if you want to share your screen, I think…
27 00:02:21.470 ⇒ 00:02:23.720 Uttam Kumaran: And even start higher level, like…
28 00:02:24.010 ⇒ 00:02:26.250 Uttam Kumaran: Refresh me on, like, what it is we’re doing.
29 00:02:26.900 ⇒ 00:02:28.699 Ashwini Sharma: Okay, yeah, once again.
30 00:02:47.300 ⇒ 00:02:49.100 Ashwini Sharma: Let’s screen share…
31 00:02:52.920 ⇒ 00:02:55.670 Ashwini Sharma: Hey, let me know if you’re able to see my screen.
32 00:02:55.670 ⇒ 00:02:56.440 Uttam Kumaran: Yes.
33 00:02:57.710 ⇒ 00:02:58.380 Ashwini Sharma: Alright.
34 00:02:58.610 ⇒ 00:03:03.020 Ashwini Sharma: So… Where should I start, right?
35 00:03:03.550 ⇒ 00:03:11.050 Ashwini Sharma: Basically, this is… what you see over here is the interface through which we can interact with the GraphQL API, right?
36 00:03:11.540 ⇒ 00:03:20.350 Ashwini Sharma: Now, what it does is we send it a request, and then it sends a bunch of data, and one of the main requests is basically this,
37 00:03:20.880 ⇒ 00:03:25.640 Ashwini Sharma: this request, right? Where we are getting market insights and trend, And…
38 00:03:26.120 ⇒ 00:03:34.680 Ashwini Sharma: This was returning huge volumes of data, because there was lots of data over there, pipeline was failing, and at one point, we exhausted the rate limit.
39 00:03:34.860 ⇒ 00:03:46.009 Ashwini Sharma: And so we decided that we are going to reduce the rate limit, I mean, reduce the volume of data that is extracted, so we applied certain filters on it, and then got a lesser volume of data, right?
40 00:03:46.320 ⇒ 00:03:54.940 Ashwini Sharma: Now, what it does is, it filters at various levels, right? One is, like, number of time frames. This we have selected as 52, so we get
41 00:03:55.190 ⇒ 00:03:58.719 Ashwini Sharma: Weekly data for up to last 52 weeks.
42 00:04:00.090 ⇒ 00:04:05.069 Ashwini Sharma: Right, from starting from, 1225, this is basically just my,
43 00:04:05.460 ⇒ 00:04:15.309 Ashwini Sharma: So starting with this date. And then we go back 52 weeks, and then we have one week of data. And in this one week of data, we get these fields.
44 00:04:15.710 ⇒ 00:04:16.180 Uttam Kumaran: Yeah.
45 00:04:16.180 ⇒ 00:04:17.430 Ashwini Sharma: these fields.
46 00:04:18.320 ⇒ 00:04:31.440 Ashwini Sharma: And there is a mixture of fields and attributes over here. Attributes and measures over here. So, attributes are things that can be used to filter the data. Measures are just measures of value, and you can’t use it in a filter.
47 00:04:31.990 ⇒ 00:04:34.000 Ashwini Sharma: And there is,
48 00:04:34.110 ⇒ 00:04:50.879 Ashwini Sharma: Some other things which are mandatory, for example, product universe has to be there, reporting level has to be there, and then there is also one more filter called Brand, which is Magic Spoon, and we only wanted to see data for Magic Spoon, so we fetched only that particular data, right?
49 00:04:50.880 ⇒ 00:04:51.280 Uttam Kumaran: Yeah.
50 00:04:51.280 ⇒ 00:04:55.829 Ashwini Sharma: Once we get this data, what we do, next step, is we put it into a redshift.
51 00:04:57.020 ⇒ 00:05:04.510 Ashwini Sharma: Alright, now, let me go over here… Alright.
52 00:05:04.620 ⇒ 00:05:11.470 Ashwini Sharma: So Basically, from spins.
53 00:05:33.290 ⇒ 00:05:35.110 Ashwini Sharma: And it’s commercial.
54 00:05:39.200 ⇒ 00:05:40.150 Ashwini Sharma: Yeah, this one.
55 00:05:44.220 ⇒ 00:05:45.080 Ashwini Sharma: Alright.
56 00:06:03.000 ⇒ 00:06:04.469 Uttam Kumaran: What schema are you in?
57 00:06:05.130 ⇒ 00:06:07.549 Ashwini Sharma: Schema is Pins Commercial.
58 00:06:09.510 ⇒ 00:06:11.169 Ashwini Sharma: Should I increase the font a little bit?
59 00:06:11.170 ⇒ 00:06:12.820 Uttam Kumaran: No, no, no, I got it, I got it.
60 00:06:13.450 ⇒ 00:06:17.170 Uttam Kumaran: So there’s Marketing Insights one week. That’s from our dbt.
61 00:06:17.400 ⇒ 00:06:21.359 Ashwini Sharma: No, no, this is just raw tables. This is raw, raw tables.
62 00:06:23.020 ⇒ 00:06:24.750 Uttam Kumaran: Oh, and what about…
63 00:06:25.370 ⇒ 00:06:27.390 Ashwini Sharma: We’re just comparing raw data to raw data.
64 00:06:27.390 ⇒ 00:06:30.870 Uttam Kumaran: What about the… what about the Prefect stuff? Where is that landing?
65 00:06:31.810 ⇒ 00:06:34.810 Ashwini Sharma: No, this is… basically, Prefect is landing it over here.
66 00:06:35.750 ⇒ 00:06:38.650 Uttam Kumaran: Oh, okay, but you just did a… you just did one week?
67 00:06:38.790 ⇒ 00:06:40.769 Uttam Kumaran: Before kicking off the whole thing.
68 00:06:41.210 ⇒ 00:06:44.590 Ashwini Sharma: No, no, this is, like, the grain is one week.
69 00:06:45.060 ⇒ 00:06:46.920 Ashwini Sharma: It goes up to 52 weeks.
70 00:06:46.920 ⇒ 00:06:47.640 Uttam Kumaran: Oh, I see.
71 00:06:47.640 ⇒ 00:06:54.589 Demilade Agboola: I’m just… Yeah, but this… This table, right, this is your table, or is it their table? Which one?
72 00:06:55.460 ⇒ 00:07:01.370 Ashwini Sharma: this is my table. And what I mean by my table is, like, I have put the data in this table.
73 00:07:01.480 ⇒ 00:07:06.259 Demilade Agboola: Alright, that’s fine. I think that’s what, what I’m trying to figure out, like, where’s the data coming from?
74 00:07:06.270 ⇒ 00:07:10.270 Ashwini Sharma: This is the data from Spin’s API, right?
75 00:07:10.700 ⇒ 00:07:11.430 Demilade Agboola: And then he’ll.
76 00:07:11.430 ⇒ 00:07:19.180 Ashwini Sharma: That’s why I mentioned earlier, data extracted from SPINC API is in this table, right? Now, there is another data set, which is, data…
77 00:07:20.010 ⇒ 00:07:21.890 Ashwini Sharma: from platform.
78 00:07:25.150 ⇒ 00:07:25.950 Ashwini Sharma: Word.
79 00:07:26.250 ⇒ 00:07:36.330 Ashwini Sharma: And that is, load form data… This one.
80 00:07:50.320 ⇒ 00:07:53.220 Ashwini Sharma: Now, this… Okay.
81 00:07:55.840 ⇒ 00:07:57.749 Ashwini Sharma: This contains a lot more data.
82 00:07:58.400 ⇒ 00:08:04.830 Ashwini Sharma: So we have to filter it to, you know, so that it can match as close as possible to the data that we have, right?
83 00:08:04.950 ⇒ 00:08:09.380 Ashwini Sharma: And that’s why, if you look at this query, I have applied certain filters where
84 00:08:09.590 ⇒ 00:08:15.300 Ashwini Sharma: time period is either in these, or… and brand is Magic Spoon.
85 00:08:17.580 ⇒ 00:08:18.200 Uttam Kumaran: Okay.
86 00:08:18.630 ⇒ 00:08:19.260 Ashwini Sharma: -
87 00:08:23.030 ⇒ 00:08:36.529 Ashwini Sharma: Now, if I do this, it’s going to reduce. But if you see here, these are some of the attributes, right? For example, channel outlet, geography, geography, level, time period. Time period, if you see, it’s 4 weeks, 52 weeks, 12 weeks, 24 weeks.
88 00:08:36.900 ⇒ 00:08:37.940 Ashwini Sharma: Like that, right?
89 00:08:38.059 ⇒ 00:08:43.729 Ashwini Sharma: And then time period end date is all 12-28, so nothing… Over here…
90 00:08:43.730 ⇒ 00:08:55.590 Uttam Kumaran: Yeah, so I think that makes sense. I guess, tell me about this QA thing, then. Like, so basically what they’re trying to see is, like, how is the stuff from Prefect different than stuff from the UI?
91 00:08:55.810 ⇒ 00:08:58.259 Ashwini Sharma: Yeah, so let, let me, let me show…
92 00:08:58.450 ⇒ 00:09:07.549 Uttam Kumaran: And I saw your query, so I’m comfortable with your query. I’m just trying to get to, like, what else can we show, you know? It’s like, there’s differences, right? So…
93 00:09:08.450 ⇒ 00:09:21.150 Ashwini Sharma: Yeah, give me… give me a second. This redshirt is really, really slow right now. If you see here, right, let’s just take a look at here. I think if I narrow it down to a certain thing, it will become easier to… easier on the eyes.
94 00:09:21.280 ⇒ 00:09:27.219 Ashwini Sharma: For example, like, if I take, you know, one particular channel outlet, one particular geography.
95 00:09:27.460 ⇒ 00:09:35.159 Ashwini Sharma: As well as, maybe, product level and category. It will be, give me a second, let me see.
96 00:09:35.280 ⇒ 00:09:37.470 Ashwini Sharma: If I have that query somewhere.
97 00:10:02.210 ⇒ 00:10:08.040 Ashwini Sharma: Maybe we can, not right, because it has to match on the other side also, so…
98 00:10:08.330 ⇒ 00:10:09.580 Ashwini Sharma: Let me take this.
99 00:10:12.420 ⇒ 00:10:13.240 Ashwini Sharma: Alright.
100 00:10:16.390 ⇒ 00:10:17.880 Ashwini Sharma: Okay,
101 00:11:01.720 ⇒ 00:11:04.900 Ashwini Sharma: Okay. I should also add a geography over here.
102 00:11:18.130 ⇒ 00:11:22.570 Ashwini Sharma: Oh, maybe I could take that sheet, right? Hold on a second.
103 00:12:14.270 ⇒ 00:12:16.040 Demilade Agboola: I just wanted to say that I think…
104 00:12:16.170 ⇒ 00:12:22.370 Demilade Agboola: from, like, conversations I have heard them talk about, it appears that, you know.
105 00:12:22.670 ⇒ 00:12:28.140 Demilade Agboola: We’re trying to match the dates… the data from Spin’s API to their UI data.
106 00:12:28.620 ⇒ 00:12:35.570 Demilade Agboola: Well, obviously, some things will not match in terms of, like, aggregate values, so things like ACV and TDP.
107 00:12:35.570 ⇒ 00:12:36.540 Ashwini Sharma: Yes, exactly.
108 00:12:36.540 ⇒ 00:12:38.150 Demilade Agboola: And that is what I…
109 00:12:38.490 ⇒ 00:12:41.150 Ashwini Sharma: pushed in the sheet, right? Okay, let’s see this.
110 00:12:41.540 ⇒ 00:12:42.409 Demilade Agboola: I need to leave.
111 00:12:42.410 ⇒ 00:12:43.600 Ashwini Sharma: CRM, yeah.
112 00:12:44.290 ⇒ 00:12:48.980 Demilade Agboola: Yeah, so what I’m trying to say is, I think what they want, is they want us to be able to…
113 00:12:49.410 ⇒ 00:12:52.470 Demilade Agboola: Tell… tell, like, why certain things are not matching.
114 00:12:52.470 ⇒ 00:12:53.030 Uttam Kumaran: Yes.
115 00:12:53.030 ⇒ 00:13:09.990 Demilade Agboola: our plan to be able to make them match is. Because ultimately, they want to be able to see that data in a dashboard, right? And they want it to come from the Spins API. So we need to be able to say, okay, so it’s not matching right now, but if we perform these calculations on the
116 00:13:10.360 ⇒ 00:13:21.190 Demilade Agboola: grain of data by the geography, we will have the appropriate ACV or TDP. I think it’s just a function of, like, they want us to be able to show how we can make that data work.
117 00:13:21.370 ⇒ 00:13:24.960 Demilade Agboola: in the… in, like, Omni at the end of the day.
118 00:13:25.160 ⇒ 00:13:25.980 Demilade Agboola: So we need to be able to.
119 00:13:25.980 ⇒ 00:13:32.629 Ashwini Sharma: Let’s take a look at this one, right? This was the data, right? Now, there are some mismatches here, right? For example, dollar def.
120 00:13:32.760 ⇒ 00:13:36.529 Ashwini Sharma: Where it is zero, it means, like, It matched perfectly.
121 00:13:36.530 ⇒ 00:13:41.000 Uttam Kumaran: Yeah, but I guess I just wanna… I just wanna even highlight, like, a couple things, even… so one is…
122 00:13:41.180 ⇒ 00:13:41.990 Uttam Kumaran: like…
123 00:13:42.100 ⇒ 00:13:52.079 Uttam Kumaran: this is just gonna be really annoying, so just bear with me. Whenever we put stuff into Google Sheets, we just have to make it really, really clear. So a couple things that I did is I, like.
124 00:13:52.230 ⇒ 00:14:05.239 Uttam Kumaran: change the title, change some of the columns, but again, it’s very hard to see these differences, so maybe, like, let’s just walk through a couple things that, like, I would suggest do. One is turn this… you should turn this column into…
125 00:14:05.350 ⇒ 00:14:07.640 Uttam Kumaran: Dollars into currency.
126 00:14:08.050 ⇒ 00:14:08.760 Uttam Kumaran: So, I wouldn’t…
127 00:14:08.760 ⇒ 00:14:09.210 Ashwini Sharma: Okay.
128 00:14:09.210 ⇒ 00:14:13.620 Uttam Kumaran: I would… I would just click the currency… Thing at the top.
129 00:14:13.740 ⇒ 00:14:15.630 Uttam Kumaran: Here, you can click here, yeah.
130 00:14:18.540 ⇒ 00:14:24.219 Uttam Kumaran: So, yeah, select the whole column. So let’s just first go make sure all the units on all the columns are perfect.
131 00:14:25.290 ⇒ 00:14:29.929 Ashwini Sharma: Okay, this might not be dollars, so I’m not really sure.
132 00:14:29.930 ⇒ 00:14:30.560 Uttam Kumaran: That’s fine.
133 00:14:30.560 ⇒ 00:14:37.949 Ashwini Sharma: It could be, so, PF means Platform Spins means Getting it from Spence, right?
134 00:14:38.190 ⇒ 00:14:42.909 Ashwini Sharma: So, this is probably the units of… Items that have been sold.
135 00:14:42.910 ⇒ 00:14:47.849 Uttam Kumaran: Yeah, so I don’t think that… I guess, what is TDP, what is… and what is ACV in this?
136 00:14:47.850 ⇒ 00:14:48.960 Demilade Agboola: excuse me, yeah.
137 00:14:48.960 ⇒ 00:14:58.460 Ashwini Sharma: Yeah, TDP is total distribution points. Now, what that means, I don’t know, right? Okay. ACV is, average.
138 00:14:58.720 ⇒ 00:15:03.159 Uttam Kumaran: But I think this is where we just… we should know that. What is… we should know what TDP is.
139 00:15:03.930 ⇒ 00:15:07.200 Ashwini Sharma: Yeah, unfortunately, it’s not there in the documentation, yeah.
140 00:15:07.200 ⇒ 00:15:17.520 Demilade Agboola: Yeah, so I think that’s part of what we need to, like, control, in the sense of we need to be able to get a call, for instance, with either,
141 00:15:18.060 ⇒ 00:15:28.530 Demilade Agboola: you know, Michael or whoever on the team is, and get, like, actual calculations and say, okay, so what does TDP… what’s the formula for TDP that you use internally?
142 00:15:28.530 ⇒ 00:15:40.020 Demilade Agboola: Or how do you think of TDP internally? And then we can try the formulas and say, okay, so these formulas that you’re trying to use, it gets us to the TDP that we’re getting out of the platform.
143 00:15:40.220 ⇒ 00:15:41.450 Demilade Agboola: Right, all your UI.
144 00:15:41.570 ⇒ 00:15:45.469 Demilade Agboola: If not, we can think of, okay, how do we
145 00:15:45.990 ⇒ 00:15:54.240 Demilade Agboola: do we need new data points? Is it a function of the filters we’re using? Are we, like, does… are there certain values excluded from…
146 00:15:54.550 ⇒ 00:16:01.479 Demilade Agboola: on the Spins API that are present in the platform API, or vice versa, I think that’s kind of what they want. They want us to be able to, like.
147 00:16:01.650 ⇒ 00:16:12.080 Demilade Agboola: ensure that whatever spins data that we’re going to use gives them the answers that they need, and in situations where it doesn’t give us the… give them the answers that they need.
148 00:16:12.080 ⇒ 00:16:23.580 Demilade Agboola: we can explain why. We can say maybe it’s a filter, it’s the geography, it’s maybe something’s been excluded, maybe the promo or no promo, like, basically, I wanna, like, I think that’s kind of what they want.
149 00:16:24.790 ⇒ 00:16:29.160 Ashwini Sharma: Yeah, I think best course of action is to set up a meeting with them, let’s…
150 00:16:29.260 ⇒ 00:16:34.700 Ashwini Sharma: At least, I have the numbers over here, right? I know what matches, what does not match.
151 00:16:34.840 ⇒ 00:16:35.799 Ashwini Sharma: And then water…
152 00:16:35.800 ⇒ 00:16:44.099 Uttam Kumaran: But let’s keep going, because I still don’t think this spreadsheet is, like, good enough. Like, this… I don’t… I don’t feel comfortable calling them until this is clean.
153 00:16:44.180 ⇒ 00:16:55.820 Uttam Kumaran: Right? So, like, there’s still a couple things that are wrong here. One is, like, did… do we… do we need to abbreviate TDP? Can we just put total distribution points? Is that… they call it TDP on their side?
154 00:16:56.210 ⇒ 00:17:00.160 Ashwini Sharma: Yeah. Okay, okay. Alright, that’s fine. They call it TDP here.
155 00:17:00.160 ⇒ 00:17:06.049 Uttam Kumaran: let’s leave it for now. The other thing I’m gonna do, Ashwin, if you click on the,
156 00:17:06.300 ⇒ 00:17:09.060 Uttam Kumaran: If you click on this, this one.
157 00:17:09.060 ⇒ 00:17:10.660 Ashwini Sharma: Hmm… Okay.
158 00:17:10.849 ⇒ 00:17:14.009 Uttam Kumaran: And then you go… just type in help, and type in freeze.
159 00:17:16.559 ⇒ 00:17:17.459 Ashwini Sharma: Right here.
160 00:17:17.529 ⇒ 00:17:21.009 Uttam Kumaran: Search, just type in freeze, one row.
161 00:17:21.199 ⇒ 00:17:24.469 Ashwini Sharma: So now this is gonna fix this, so if you scroll down…
162 00:17:25.649 ⇒ 00:17:27.159 Uttam Kumaran: It’s gonna fix this.
163 00:17:27.329 ⇒ 00:17:31.009 Uttam Kumaran: The second piece, let’s add conditional formatting to the diff.
164 00:17:33.390 ⇒ 00:17:34.650 Ashwini Sharma: Mmm, okay.
165 00:17:34.650 ⇒ 00:17:41.410 Uttam Kumaran: So, click on that, you can format, conditional formatting.
166 00:17:42.660 ⇒ 00:17:45.160 Uttam Kumaran: And you can do color scale.
167 00:17:46.720 ⇒ 00:17:52.129 Uttam Kumaran: And then you can change this to basically red, like, green to red, or white to red.
168 00:17:52.580 ⇒ 00:17:53.630 Uttam Kumaran: To click on this, yeah.
169 00:17:55.810 ⇒ 00:17:56.780 Ashwini Sharma: Okay.
170 00:17:57.440 ⇒ 00:18:02.600 Uttam Kumaran: And then… I guess this is where we need to know.
171 00:18:03.810 ⇒ 00:18:05.660 Uttam Kumaran: Like, what the issue is.
172 00:18:06.530 ⇒ 00:18:10.559 Uttam Kumaran: So… Like, what is a bad value here?
173 00:18:11.540 ⇒ 00:18:25.410 Ashwini Sharma: The bad value is basically, like, for example, things that don’t match, right? For example, over here, right, 110. Now, this is not matching, because I have a theory over here. This is a pretty relatively recent data, right?
174 00:18:25.870 ⇒ 00:18:32.529 Uttam Kumaran: So, Ashrini, that’s exactly what we need to put in the sheet, your theory.
175 00:18:32.530 ⇒ 00:18:50.700 Demilade Agboola: So I guess, so that’s kind of what we need to, again, like, they want us to be able to say, okay, so maybe any data prior to XYZ day, the disparity is very high, so we know that there’s a two-week or a one-month window where things might need to settle before the accuracy can be relied upon.
176 00:18:51.000 ⇒ 00:18:58.940 Demilade Agboola: that’s kind of, like, I think that’s kind of what they want us to be able to QA, and so that we can show that, okay, for, you know, over 4 weeks.
177 00:18:59.080 ⇒ 00:19:11.559 Demilade Agboola: time period, this is what we might need to wait for. Can we look at it from a geography level as well? Because, like, instead of just viewing it, can we sum up on a geography level, do the units match? Do the,
178 00:19:11.700 ⇒ 00:19:17.430 Demilade Agboola: sums match. I guess that’s kind of where they… they kind of want us to be able to…
179 00:19:17.550 ⇒ 00:19:19.689 Demilade Agboola: Show where things might not match.
180 00:19:19.850 ⇒ 00:19:21.200 Demilade Agboola: If we somewhat.
181 00:19:21.200 ⇒ 00:19:21.950 Ashwini Sharma: Yeah, so…
182 00:19:22.450 ⇒ 00:19:35.230 Ashwini Sharma: Right, as I said, right, this data… the grain of the data is at a weak level, right? And if it is not matching at one-week level, it is not going to match at a four-week level, or it is not going to match at
183 00:19:35.610 ⇒ 00:19:48.810 Ashwini Sharma: you know, some higher levels, right? And if we sum up, like, across geographies, if you fix a geography, and then you look at all the sales that have happened within that geography, it’s not going to match somewhere. Because we are looking at the very basic unit of
184 00:19:49.050 ⇒ 00:19:49.860 Ashwini Sharma: You know.
185 00:19:50.170 ⇒ 00:19:55.460 Ashwini Sharma: the revenue that has been generated over here. Yeah.
186 00:19:55.810 ⇒ 00:19:58.609 Ashwini Sharma: So, I’ll put my theory over here, right?
187 00:19:58.610 ⇒ 00:20:05.999 Uttam Kumaran: Well, so let… yeah, let’s… let’s, let’s just keep… let’s finish this spreadsheet first, so go, so let’s keep, let’s change this, this max value.
188 00:20:06.320 ⇒ 00:20:11.019 Ashwini Sharma: Let’s just put it at, like, we shouldn’t see more than… I mean, I don’t know, we’re…
189 00:20:11.030 ⇒ 00:20:13.560 Uttam Kumaran: Like, see this max value?
190 00:20:13.750 ⇒ 00:20:17.139 Ashwini Sharma: I should change this thing to numeric somewhere.
191 00:20:18.160 ⇒ 00:20:19.269 Uttam Kumaran: It is numeric.
192 00:20:21.540 ⇒ 00:20:22.900 Uttam Kumaran: Right? 0.1.
193 00:20:24.220 ⇒ 00:20:28.190 Ashwini Sharma: I think it is a string, that’s why it didn’t give any value over here.
194 00:20:29.060 ⇒ 00:20:29.810 Uttam Kumaran: Oh, no, no.
195 00:20:29.810 ⇒ 00:20:33.109 Ashwini Sharma: Oh, main value is what number? Okay. Main value is what?
196 00:20:33.110 ⇒ 00:20:33.530 Uttam Kumaran: Yeah.
197 00:20:33.530 ⇒ 00:20:34.510 Ashwini Sharma: 1,0…
198 00:20:34.940 ⇒ 00:20:41.469 Uttam Kumaran: And then I would just change the… you could change the max to, like, the point at which you want to flag it.
199 00:20:43.310 ⇒ 00:20:47.230 Ashwini Sharma: Max value should be anything… anything more than…
200 00:20:47.500 ⇒ 00:20:58.590 Ashwini Sharma: What, 1,000. Yeah. Oh, no, in terms of units, it’s… Fuh.
201 00:20:58.590 ⇒ 00:20:59.670 Uttam Kumaran: You put one?
202 00:21:00.240 ⇒ 00:21:03.120 Uttam Kumaran: Anything above… 1 or above is going to be bright red.
203 00:21:04.090 ⇒ 00:21:04.980 Ashwini Sharma: Yeah.
204 00:21:05.130 ⇒ 00:21:06.140 Ashwini Sharma: Okay.
205 00:21:07.920 ⇒ 00:21:08.610 Uttam Kumaran: Okay.
206 00:21:08.730 ⇒ 00:21:11.770 Uttam Kumaran: Let’s just leave that for now. Let’s just keep moving, because I…
207 00:21:11.770 ⇒ 00:21:12.810 Ashwini Sharma: Alright, alright.
208 00:21:12.810 ⇒ 00:21:14.940 Uttam Kumaran: want us to keep going. So then let’s.
209 00:21:14.940 ⇒ 00:21:17.199 Ashwini Sharma: We want to do the same thing for the dollars also, right?
210 00:21:17.200 ⇒ 00:21:20.010 Uttam Kumaran: Yes, any diff column, let’s do the same thing.
211 00:21:21.050 ⇒ 00:21:21.790 Ashwini Sharma: Okay.
212 00:21:24.510 ⇒ 00:21:35.030 Ashwini Sharma: All right, any diff columns? Yeah, so I didn’t add a diff over here, because there is no way that this is going to match.
213 00:21:35.350 ⇒ 00:21:42.820 Ashwini Sharma: These are calculated columns based on some kind of averages, right? TDP and average ACP both.
214 00:21:43.310 ⇒ 00:21:48.060 Uttam Kumaran: So, putting a diff over here does not make any sense. It is definitely not going to match.
215 00:21:48.910 ⇒ 00:21:50.049 Demilade Agboola: Alright, so for.
216 00:21:51.140 ⇒ 00:22:06.129 Uttam Kumaran: Okay, but, like, but I do want to, like, flag that. So, like, okay, but let’s… let’s… sorry, guys, I just want to finish the spreadsheet first before we talk about, like, the triage. So, the last thing… so, are we good on… but dollars diff… there still isn’t the conditional formatting.
217 00:22:06.130 ⇒ 00:22:10.080 Ashwini Sharma: Okay, let’s add that. But let’s just, yeah, let’s just keep pushing.
218 00:22:10.580 ⇒ 00:22:13.010 Ashwini Sharma: Data, data, data, format…
219 00:22:13.010 ⇒ 00:22:18.420 Uttam Kumaran: I’m just gonna get… I feel like I’ll just do a little bit of… I’ll probably start to do Google Sheets.
220 00:22:18.840 ⇒ 00:22:25.729 Uttam Kumaran: formatting less session for everybody. But this is fine. So yeah, also, same thing.
221 00:22:27.120 ⇒ 00:22:34.449 Ashwini Sharma: You know, max value is anything more than, let’s say, $200 is sort of to be flagged, right?
222 00:22:34.890 ⇒ 00:22:36.060 Ashwini Sharma: Alright, yeah.
223 00:22:36.280 ⇒ 00:22:48.860 Uttam Kumaran: Okay, great. Next thing, next thing I’m gonna say is, select columns A until… A until…
224 00:22:50.540 ⇒ 00:22:55.540 Uttam Kumaran: Like, what are the relevant dimensions that people always want to see?
225 00:22:55.690 ⇒ 00:22:56.210 Ashwini Sharma: Head.
226 00:22:56.210 ⇒ 00:22:58.139 Uttam Kumaran: We’re gonna freeze those. Okay.
227 00:22:58.140 ⇒ 00:22:58.760 Ashwini Sharma: Okay.
228 00:22:58.760 ⇒ 00:23:01.269 Uttam Kumaran: So those… but I think those are too much.
229 00:23:03.530 ⇒ 00:23:10.089 Uttam Kumaran: So, like, what I can do is, like, I don’t think brand… it’s always Magic Spoon brand, and it’s always 4-week time period.
230 00:23:10.340 ⇒ 00:23:13.749 Uttam Kumaran: So that’s not as… that’s always grocery, right?
231 00:23:15.300 ⇒ 00:23:19.710 Ashwini Sharma: It could be something else also. Let me add a filter over here.
232 00:23:20.140 ⇒ 00:23:21.930 Uttam Kumaran: It looks like it’s all grocery.
233 00:23:29.920 ⇒ 00:23:35.359 Ashwini Sharma: Department. Okay, it’s grocery only, yeah. So, let me hide this thing, department in that case.
234 00:23:36.320 ⇒ 00:23:37.280 Ashwini Sharma: Do you want to sue?
235 00:23:37.280 ⇒ 00:23:46.650 Uttam Kumaran: Well, let’s… let’s leave it, so let’s… so let’s just… let’s make sure that columns A until… C… R…
236 00:23:46.780 ⇒ 00:23:47.990 Uttam Kumaran: are frozen.
237 00:23:48.270 ⇒ 00:23:59.470 Uttam Kumaran: So… Just freeze those, just, just select this, shift, and then until… select this, yeah? Same thing, just…
238 00:23:59.620 ⇒ 00:24:01.330 Uttam Kumaran: Yeah, just type in freeze.
239 00:24:01.890 ⇒ 00:24:04.309 Uttam Kumaran: Freeze up till current column.
240 00:24:07.640 ⇒ 00:24:13.099 Uttam Kumaran: Perfect. Now when you scroll, it’s gonna go there. Okay, next thing we can do is…
241 00:24:14.510 ⇒ 00:24:20.129 Uttam Kumaran: The other thing I’ll suggest is if you click… click this, double-click on the middle bar here.
242 00:24:20.610 ⇒ 00:24:23.189 Uttam Kumaran: It’s gonna align to the smallest value.
243 00:24:23.820 ⇒ 00:24:26.760 Uttam Kumaran: So do this… you could do the same thing here.
244 00:24:28.700 ⇒ 00:24:37.850 Uttam Kumaran: So what, what, yeah, you could do the same thing, and then the other thing we could do is you can highlight these, and you can… you can do, like, a wrap.
245 00:24:38.030 ⇒ 00:24:42.849 Uttam Kumaran: So you can go to Help, And then type in rap.
246 00:24:44.900 ⇒ 00:24:50.720 Uttam Kumaran: And then type in wrap text, yeah. Now, you can just make it smaller, it’ll… it’ll expand the cell.
247 00:24:54.940 ⇒ 00:24:55.670 Uttam Kumaran: Yeah.
248 00:24:56.370 ⇒ 00:24:57.140 Uttam Kumaran: Great.
249 00:24:57.470 ⇒ 00:24:59.530 Uttam Kumaran: Cool, so this seems better.
250 00:24:59.900 ⇒ 00:25:06.139 Uttam Kumaran: The last thing I will suggest is, select this first column.
251 00:25:08.870 ⇒ 00:25:10.870 Uttam Kumaran: No, I’m sorry, sorry, the first row, sorry.
252 00:25:11.460 ⇒ 00:25:12.110 Ashwini Sharma: Okay.
253 00:25:12.560 ⇒ 00:25:16.519 Uttam Kumaran: And then, go ahead and center the,
254 00:25:17.560 ⇒ 00:25:20.590 Uttam Kumaran: So under the, yeah, the headers?
255 00:25:21.120 ⇒ 00:25:28.660 Uttam Kumaran: And then also, I would also suggest, doing,
256 00:25:29.670 ⇒ 00:25:37.650 Uttam Kumaran: What you can also do is, basically, hit X here.
257 00:25:38.720 ⇒ 00:25:40.260 Ashwini Sharma: Sorry, what is that?
258 00:25:40.260 ⇒ 00:25:41.890 Uttam Kumaran: Hit X on the top right here.
259 00:25:42.910 ⇒ 00:25:43.910 Ashwini Sharma: X, okay.
260 00:25:44.960 ⇒ 00:25:47.320 Uttam Kumaran: Yeah, don’t save, yeah.
261 00:25:48.150 ⇒ 00:25:56.420 Uttam Kumaran: And then one thing you can do that’s also, like, a new feature in Google Sheets is you can create a table. So just hit,
262 00:25:56.630 ⇒ 00:25:59.560 Uttam Kumaran: Command-A, or Apple A.
263 00:26:01.280 ⇒ 00:26:02.780 Ashwini Sharma: And then right-click?
264 00:26:04.370 ⇒ 00:26:06.950 Uttam Kumaran: Anywhere. Do convert to table.
265 00:26:08.520 ⇒ 00:26:09.180 Uttam Kumaran: Yeah.
266 00:26:11.380 ⇒ 00:26:16.599 Ashwini Sharma: If an insert table over a merged range, consider unmerging cells or moving to the new table.
267 00:26:16.950 ⇒ 00:26:18.300 Uttam Kumaran: What has emerged?
268 00:26:27.220 ⇒ 00:26:29.610 Uttam Kumaran: Oh, there’s a merged range here?
269 00:26:35.320 ⇒ 00:26:37.580 Ashwini Sharma: No, this is all out of a single query.
270 00:26:41.690 ⇒ 00:26:42.400 Uttam Kumaran: Mmm.
271 00:26:44.740 ⇒ 00:26:47.650 Uttam Kumaran: Okay, alright, that’s fine for now.
272 00:26:48.250 ⇒ 00:26:51.030 Ashwini Sharma: And then, I guess, so… Or the book,
273 00:26:52.460 ⇒ 00:26:55.419 Ashwini Sharma: Create… what is that? Create a table, right?
274 00:26:55.420 ⇒ 00:26:58.260 Uttam Kumaran: Yeah, so it’s… it’s actually okay, it’s… I think, like.
275 00:26:58.470 ⇒ 00:27:09.329 Uttam Kumaran: It’s fine for now. One thing we can also do is, I just added a filter, so you can filter now any of the columns. The last thing… so the next thing I’m gonna have you do is let’s sort by dollar diff.
276 00:27:12.370 ⇒ 00:27:14.290 Ashwini Sharma: Alright.
277 00:27:17.110 ⇒ 00:27:19.110 Uttam Kumaran: So go… no, no, just go to the dollar diff…
278 00:27:19.110 ⇒ 00:27:20.059 Demilade Agboola: That’s about it.
279 00:27:20.220 ⇒ 00:27:23.650 Uttam Kumaran: Yeah, go to the dollar diff column here. No, no, no, no, just go to the dollar diff.
280 00:27:23.650 ⇒ 00:27:24.980 Ashwini Sharma: Okay.
281 00:27:25.290 ⇒ 00:27:28.449 Uttam Kumaran: Yeah, go to the dollar… just wherever the dollar diff is.
282 00:27:29.600 ⇒ 00:27:30.479 Ashwini Sharma: Oh, okay, yeah.
283 00:27:30.480 ⇒ 00:27:31.080 Uttam Kumaran: Yeah.
284 00:27:31.720 ⇒ 00:27:33.979 Ashwini Sharma: So then, then, just click here.
285 00:27:33.980 ⇒ 00:27:34.630 Demilade Agboola: Quick.
286 00:27:36.600 ⇒ 00:27:37.690 Uttam Kumaran: And then sort.
287 00:27:37.870 ⇒ 00:27:42.080 Uttam Kumaran: A to Z. Or Z to A, I don’t know what it is, whatever. Yeah. Z to A.
288 00:27:42.080 ⇒ 00:27:44.940 Demilade Agboola: I think it would be easy for you, so the largest to lowest.
289 00:27:47.770 ⇒ 00:27:48.280 Ashwini Sharma: Yep.
290 00:27:49.430 ⇒ 00:27:53.370 Uttam Kumaran: So… This is the issue now.
291 00:27:53.710 ⇒ 00:27:54.220 Uttam Kumaran: Right.
292 00:27:54.220 ⇒ 00:28:03.079 Demilade Agboola: Alright, so for these, like, it’s also a function of the time, so it’s for 52 weeks. So my question is, why do we have 52 weeks and 4 weeks and 24 weeks? Like…
293 00:28:03.300 ⇒ 00:28:09.080 Ashwini Sharma: We’re looking at data at different levels, right? See, in the SPINS data.
294 00:28:09.520 ⇒ 00:28:10.929 Uttam Kumaran: There’s no Spins dollar.
295 00:28:11.470 ⇒ 00:28:13.080 Demilade Agboola: Yeah, but what I’m trying to say is…
296 00:28:13.850 ⇒ 00:28:16.410 Demilade Agboola: Yes, what I’m trying to say is.
297 00:28:16.800 ⇒ 00:28:22.459 Demilade Agboola: We shouldn’t have 52 weeks, 4 weeks, and 24 weeks all in the same table. It’s confusing.
298 00:28:24.720 ⇒ 00:28:26.610 Demilade Agboola: You want me to put it in different tables?
299 00:28:26.870 ⇒ 00:28:43.389 Demilade Agboola: Yeah, so if there’s… so we can see what the comparison looks like after 4 weeks, or, like, the 4 weeks, granularity, the 52 weeks granularity, and 24 weeks granularity. Now, for 52 weeks now, we can see that there are no spin dollars, so we can explain, maybe try and figure out what’s going on with that.
300 00:28:43.610 ⇒ 00:28:50.690 Demilade Agboola: If we… if spins, you know, I don’t know, I can’t come up with a hypothesis right now.
301 00:28:50.800 ⇒ 00:28:55.329 Demilade Agboola: But we’ll need to be able to explain why there’s no data for, you know, spin dollars for 52 weeks.
302 00:28:55.530 ⇒ 00:29:07.779 Ashwini Sharma: Yeah, I kept it all in the same way, because it allows you to see things at a, you know, maybe… like, for example, let’s say I want to see only brand, and I want to see only, let’s say.
303 00:29:08.170 ⇒ 00:29:10.030 Demilade Agboola: Yes, but it’s my point.
304 00:29:10.030 ⇒ 00:29:10.510 Ashwini Sharma: Right?
305 00:29:10.510 ⇒ 00:29:15.079 Demilade Agboola: It’s hard to… yes, but we can look at that from different tables.
306 00:29:15.300 ⇒ 00:29:16.300 Demilade Agboola: Easily.
307 00:29:16.410 ⇒ 00:29:25.709 Demilade Agboola: if I go to the different tables and I see for 4 weeks, I can make my comparisons, for 2 weeks, I can make my comparisons, and for 24… because now the dollar a day for the unit diff.
308 00:29:26.970 ⇒ 00:29:38.639 Demilade Agboola: it looks… it looks funny because of the disparity. So, because we’re comparing over for 2 weeks, the disparity is very large compared to, you know, 4 weeks, for instance.
309 00:29:38.790 ⇒ 00:29:47.649 Demilade Agboola: So for this, we can see, like, okay, the issue appears to be for market or for the brand, there’s no… there doesn’t appear to be Spins dollars available at all.
310 00:29:48.390 ⇒ 00:29:49.419 Demilade Agboola: So, I think.
311 00:29:49.420 ⇒ 00:29:57.350 Ashwini Sharma: Yeah, for this, it was not available, because the filters that we applied to extract data from spins didn’t include this entry.
312 00:29:57.530 ⇒ 00:30:00.449 Ashwini Sharma: Right? And that’s why we are seeing a huge dollar diff.
313 00:30:00.610 ⇒ 00:30:04.670 Ashwini Sharma: But if there was some entry over here, right, maybe.
314 00:30:06.160 ⇒ 00:30:08.290 Demilade Agboola: So, can we remove that, like…
315 00:30:09.570 ⇒ 00:30:13.440 Demilade Agboola: Can we remove these, then, if we don’t have any spins data to compare?
316 00:30:13.440 ⇒ 00:30:19.999 Uttam Kumaran: Well, I guess… but this is where, like, I want to put the… this needs to go into the triage step, right? Like, so…
317 00:30:20.520 ⇒ 00:30:30.120 Uttam Kumaran: like, let’s… can you zoom out a little bit, Ashwini, on this? So now let’s just… let’s go ahead and create… just zoom out, yeah. Just hit Command-minus. Yeah.
318 00:30:30.300 ⇒ 00:30:33.560 Uttam Kumaran: So, let’s create a new column on the far right.
319 00:30:34.080 ⇒ 00:30:35.770 Ashwini Sharma: Called triage.
320 00:30:36.270 ⇒ 00:30:41.149 Uttam Kumaran: Like, discrepancy reason, or something. Let’s just do discrepancy reason.
321 00:30:42.690 ⇒ 00:30:43.910 Uttam Kumaran: So…
322 00:30:44.500 ⇒ 00:30:58.979 Uttam Kumaran: for anything that is an issue, we need to put the reasoning, right? So it’s clear that you know the reasons. So my ask for you is that anything that is flagged as red, we need to have a reason in the column next to it.
323 00:30:59.080 ⇒ 00:31:07.170 Uttam Kumaran: Right? So, the other thing is, again, if the spins data… right now, these are all red because there’s no spins data.
324 00:31:07.300 ⇒ 00:31:10.529 Ashwini Sharma: So, before we jump to answering that.
325 00:31:10.740 ⇒ 00:31:13.229 Uttam Kumaran: Is that our fault? That’s my first question.
326 00:31:14.280 ⇒ 00:31:15.020 Ashwini Sharma: No.
327 00:31:15.020 ⇒ 00:31:16.930 Uttam Kumaran: Okay, so if it’s not all…
328 00:31:16.930 ⇒ 00:31:17.760 Ashwini Sharma: He told me to use a.
329 00:31:17.760 ⇒ 00:31:36.569 Uttam Kumaran: No, no, wait, wait, wait, wait, don’t wait, wait, wait, wait, wait, please, let’s just go step by step, because I’m telling you, this is how we have to go pretty rigorously like this, otherwise the client is going to be as confused as we are. And so, first thing is, if it’s not our fault, then in the triage step, we need to just put in
330 00:31:36.860 ⇒ 00:31:39.950 Uttam Kumaran: Exactly why it is the way it is.
331 00:31:47.140 ⇒ 00:31:49.370 Uttam Kumaran: Perfect. But what… I don’t still…
332 00:31:50.000 ⇒ 00:31:55.030 Uttam Kumaran: I still don’t get what that… what is that… can you explain to me if, like, I was the client saying, okay, what does that mean?
333 00:31:55.510 ⇒ 00:32:00.659 Ashwini Sharma: Okay, so this means that we applied certain filters to pull the data out of Spin’s API.
334 00:32:01.400 ⇒ 00:32:03.480 Demilade Agboola: And we’ll filter those… we’ll filter here…
335 00:32:03.480 ⇒ 00:32:05.609 Uttam Kumaran: What filters did we use?
336 00:32:05.920 ⇒ 00:32:06.770 Demilade Agboola: Yeah, yeah.
337 00:32:06.770 ⇒ 00:32:12.299 Ashwini Sharma: I, I will tell that, give me a second. So, one of the filters is,
338 00:32:13.540 ⇒ 00:32:16.289 Ashwini Sharma: brand was Magic Spoon, right?
339 00:32:16.980 ⇒ 00:32:21.250 Ashwini Sharma: We had filters for brand, we had filters for geography.
340 00:32:21.680 ⇒ 00:32:24.489 Ashwini Sharma: What else did we have?
341 00:32:25.070 ⇒ 00:32:27.730 Demilade Agboola: Okay, but my question is, like, what is making it null?
342 00:32:27.910 ⇒ 00:32:31.679 Demilade Agboola: Is it the geography? Is it the brand? Is it the… like…
343 00:32:31.680 ⇒ 00:32:37.870 Ashwini Sharma: Rob, most probably, it’s the geography that made it now. So basically, you see here, the geography is this one, right? New York.
344 00:32:38.190 ⇒ 00:32:40.639 Ashwini Sharma: And why Mulu? So…
345 00:32:40.640 ⇒ 00:32:45.550 Demilade Agboola: I think it’s important for us to just be able to put, the geography filter
346 00:32:45.810 ⇒ 00:32:50.829 Demilade Agboola: we’re only looking at XYZ spots, or XYZ geography, and this doesn’t…
347 00:32:50.830 ⇒ 00:32:54.840 Ashwini Sharma: There, geography, geography, this geography is there, but why didn’t we get it, okay?
348 00:32:56.050 ⇒ 00:32:58.970 Ashwini Sharma: Brand, brand cat is there.
349 00:33:03.630 ⇒ 00:33:06.260 Ashwini Sharma: I didn’t add category filters.
350 00:33:14.620 ⇒ 00:33:16.590 Ashwini Sharma: This is not there, okay.
351 00:33:17.890 ⇒ 00:33:22.459 Ashwini Sharma: Okay, it seems like there is some work to do in that case, because this was
352 00:33:22.730 ⇒ 00:33:25.770 Ashwini Sharma: And look at individual records. This was part of the…
353 00:33:28.450 ⇒ 00:33:30.519 Ashwini Sharma: Oh, hold on a second, why is this?
354 00:33:33.050 ⇒ 00:33:36.910 Ashwini Sharma: This got merged, it seems like, when I was creating the CSV.
355 00:33:37.630 ⇒ 00:33:42.229 Ashwini Sharma: Category and subcategory is getting merged, and that’s why it was not able to create that table.
356 00:33:42.430 ⇒ 00:33:49.570 Uttam Kumaran: Yes, perfect. So, I would… that would be… yeah, so we should… it’d be great to, like, fix that.
357 00:33:50.060 ⇒ 00:33:57.269 Uttam Kumaran: So these are the details. So let’s… so now… so I think that’s… there’s a bunch of these for… for which that’s probably the case.
358 00:33:58.640 ⇒ 00:34:07.209 Uttam Kumaran: Okay, so my next suggestion is, let’s filter out anything where the spin’s dollars is null.
359 00:34:09.239 ⇒ 00:34:10.269 Ashwini Sharma: Dollar is not…
360 00:34:10.270 ⇒ 00:34:16.239 Uttam Kumaran: So go to the spins dollars cap, go to the spins dollars column, yeah, filter out anything, yep.
361 00:34:16.440 ⇒ 00:34:19.240 Uttam Kumaran: Perfect. Now, let’s go through these.
362 00:34:19.650 ⇒ 00:34:20.280 Ashwini Sharma: Hmm.
363 00:34:21.010 ⇒ 00:34:27.799 Ashwini Sharma: Yeah, so now whenever you see this difference, right, if you notice, this number is never large.
364 00:34:31.360 ⇒ 00:34:31.860 Ashwini Sharma: Say.
365 00:34:31.860 ⇒ 00:34:42.430 Uttam Kumaran: Okay, but, like, go even, consider me even dumber than you, than you think. If I’m like, hi, I’m… I’m Magic Spoon client, I see $500, it’s red.
366 00:34:42.550 ⇒ 00:34:44.609 Uttam Kumaran: Why is… why is it red?
367 00:34:45.400 ⇒ 00:34:51.340 Ashwini Sharma: Yeah, so I don’t have an answer to that, right? Because… see, but I just have a theory of telling ways.
368 00:34:51.340 ⇒ 00:34:53.259 Uttam Kumaran: So what is the… what is your theory?
369 00:34:53.260 ⇒ 00:35:02.690 Ashwini Sharma: The theory is, like, this is relatively recent data, and that’s why we see some of the changes. When I extract data from, from spins, there is a…
370 00:35:03.120 ⇒ 00:35:08.880 Ashwini Sharma: It kind of, you know, gives me information up until certain point in time, right?
371 00:35:09.160 ⇒ 00:35:13.319 Ashwini Sharma: And… Okay, Spence Dollar is more than this one. Okay, hold on a second.
372 00:35:13.320 ⇒ 00:35:14.620 Demilade Agboola: Yes, I’m so interested in that.
373 00:35:14.620 ⇒ 00:35:19.010 Ashwini Sharma: more, right? Yeah. And everywhere, okay, so… Right, yeah.
374 00:35:19.330 ⇒ 00:35:27.220 Ashwini Sharma: So maybe some changes into spins have happened because of which it updated certain columns, right? It updated the net value.
375 00:35:27.660 ⇒ 00:35:37.210 Ashwini Sharma: So, what Spence does is it releases data on a certain cycle, right? Once every month it releases. And whenever it releases once every month, it releases for
376 00:35:37.300 ⇒ 00:35:47.869 Ashwini Sharma: the last 4 weeks, since the previous month, as well as any changes that have happened to the sales. Like, for example, somebody bought something, and then they returned it after a month.
377 00:35:48.630 ⇒ 00:35:52.119 Ashwini Sharma: Right? So that means the sales numbers have been impacted now.
378 00:35:52.510 ⇒ 00:35:52.990 Uttam Kumaran: Okay.
379 00:35:52.990 ⇒ 00:35:54.990 Ashwini Sharma: Those updates also happen.
380 00:35:54.990 ⇒ 00:35:58.579 Uttam Kumaran: That is exactly what needs to get put into the column.
381 00:35:58.870 ⇒ 00:36:01.359 Ashwini Sharma: So, you’re… I think you’re right.
382 00:36:01.390 ⇒ 00:36:05.769 Uttam Kumaran: I think your theory is probably as good as a theory as we have.
383 00:36:05.830 ⇒ 00:36:23.449 Uttam Kumaran: And so, the good thing is, is on average, our dollars is higher than the platform. So what… so if I was to say it back to you, you’re saying that the platform is probably most up-to-date, the Spins API, there is a revision period.
384 00:36:23.950 ⇒ 00:36:24.430 Ashwini Sharma: Yep.
385 00:36:24.430 ⇒ 00:36:27.910 Uttam Kumaran: In which the numbers get revised to include refunds?
386 00:36:28.160 ⇒ 00:36:28.700 Ashwini Sharma: Yeah.
387 00:36:28.890 ⇒ 00:36:37.449 Ashwini Sharma: And… and this is… this is not on the same date, right? So, the platform data, they extracted it sometime 2-3 weeks earlier, and then gave me a dump.
388 00:36:37.710 ⇒ 00:36:42.989 Ashwini Sharma: And then it took me some time to load it, so this is relatively latest data.
389 00:36:42.990 ⇒ 00:36:45.439 Uttam Kumaran: So let’s put that, let’s put that in the reason.
390 00:36:46.110 ⇒ 00:36:47.450 Ashwini Sharma: Yeah.
391 00:36:47.880 ⇒ 00:36:56.650 Uttam Kumaran: So let’s put… let’s put two potential… options, right? The first… is that the Spins platform
392 00:36:58.970 ⇒ 00:37:04.099 Uttam Kumaran: The Spins AP… so you’re saying that the Spins API is the most up-to-date?
393 00:37:05.450 ⇒ 00:37:17.600 Ashwini Sharma: Yes, compared to platform… so, this is what has happened, right? Platform data, they extracted on a certain date, and then they tried to load it first on a table, right? They could not do it.
394 00:37:17.670 ⇒ 00:37:25.479 Ashwini Sharma: And then they gave it to me, and then I loaded it. So it has been quite some time since PIN’s data has been loaded… sorry, platform data has been loaded, right? For example, like…
395 00:37:26.030 ⇒ 00:37:30.880 Ashwini Sharma: Probably this one, 0116-2026, so… 16th, I guess.
396 00:37:30.880 ⇒ 00:37:35.220 Demilade Agboola: Anyway… Is there any way we can use the Spins API to match
397 00:37:35.820 ⇒ 00:37:38.719 Demilade Agboola: The exact date that it was extracted.
398 00:37:38.720 ⇒ 00:37:42.240 Ashwini Sharma: it won’t work, right? Once the data has been updated, it will.
399 00:37:42.240 ⇒ 00:37:46.899 Uttam Kumaran: But I think, Ashwini, that’s… this is exactly, like, what I think we should… we have to put in the description.
400 00:37:46.900 ⇒ 00:37:47.730 Demilade Agboola: Yeah.
401 00:37:47.900 ⇒ 00:37:53.249 Uttam Kumaran: So let’s… let’s just go ahead and do that. I’m just gonna keep pushing us to close this, close this out.
402 00:37:53.250 ⇒ 00:37:56.330 Ashwini Sharma: This is relatively new data from SPENS, right?
403 00:37:56.800 ⇒ 00:38:08.010 Uttam Kumaran: Can we add a bit more detail? Just… Yeah, no, we need to add… basically everything me and you said, we need to add here. Like, this needs to live without me and you having to explain it, basically.
404 00:38:08.380 ⇒ 00:38:12.320 Demilade Agboola: There was a time difference between the upload and the spins run.
405 00:38:13.510 ⇒ 00:38:17.249 Demilade Agboola: So let’s say 4 days, 5 days, we’ve, you know.
406 00:38:18.840 ⇒ 00:38:26.890 Demilade Agboola: So that they can understand why there’s more, so that’s why there’s more spins data than… B… Platform data.
407 00:38:29.770 ⇒ 00:38:30.480 Ashwini Sharma: Alright.
408 00:38:33.560 ⇒ 00:38:37.630 Demilade Agboola: Can we… can we put, like, Can we put, you know.
409 00:38:38.120 ⇒ 00:38:43.869 Demilade Agboola: Maybe 5 days difference, or, you know, 7-day difference between when data was extracted and.
410 00:38:43.870 ⇒ 00:38:50.739 Ashwini Sharma: So, I don’t know when data was extracted from platform. I know when data was extracted from spins, which was probably Monday.
411 00:38:50.980 ⇒ 00:38:57.009 Uttam Kumaran: So then… but then, I guess my point at that point is, like, let’s put that… let’s create another column that’s, like…
412 00:38:57.570 ⇒ 00:38:59.240 Uttam Kumaran: recommendation?
413 00:39:01.180 ⇒ 00:39:02.799 Uttam Kumaran: Or next steps.
414 00:39:04.860 ⇒ 00:39:09.550 Uttam Kumaran: And then I would put in this… and then here, I would put… We’re not sure…
415 00:39:10.430 ⇒ 00:39:12.560 Uttam Kumaran: Like, like, I guess this is where I’m like…
416 00:39:12.770 ⇒ 00:39:18.409 Uttam Kumaran: I think, Ashwin, I want to be very clear that, like, it’s not important for you to convince me and Dangolade.
417 00:39:18.410 ⇒ 00:39:19.110 Ashwini Sharma: Yeah, yeah.
418 00:39:19.110 ⇒ 00:39:34.730 Uttam Kumaran: have to convince the client. And so, you need to give so much information so that when they read this without us in the room, they’re clear, right? That’s it. So, I think you’re right in that, like, look, they pulled the SPINS data from the platform.
419 00:39:35.160 ⇒ 00:39:39.699 Uttam Kumaran: Like, when… do you think they pulled it, like, a week ago? Two weeks ago?
420 00:39:40.790 ⇒ 00:39:42.080 Ashwini Sharma: Much earlier, I think.
421 00:39:42.610 ⇒ 00:39:43.189 Demilade Agboola: Yeah, simple, right?
422 00:39:43.430 ⇒ 00:39:44.570 Ashwini Sharma: Yeah. You can just say…
423 00:39:44.570 ⇒ 00:39:46.659 Uttam Kumaran: Are you gonna say they pulled it within a week?
424 00:39:47.030 ⇒ 00:39:47.880 Uttam Kumaran: We just…
425 00:39:47.880 ⇒ 00:39:48.500 Ashwini Sharma: Murder.
426 00:39:48.660 ⇒ 00:39:53.619 Ashwini Sharma: They did it in December, I think, if I’m correct. December, or early January.
427 00:39:54.200 ⇒ 00:39:55.519 Uttam Kumaran: Whatever it is, dude.
428 00:39:56.160 ⇒ 00:40:03.020 Uttam Kumaran: You just put the data as was pulled weeks before we pulled data from the API.
429 00:40:03.540 ⇒ 00:40:11.810 Uttam Kumaran: Given… like, the small amount of difference, our best guess is that
430 00:40:12.680 ⇒ 00:40:16.159 Uttam Kumaran: like, we actually have the latest data from the API.
431 00:40:16.860 ⇒ 00:40:19.599 Uttam Kumaran: And that is what is… that is what is leading to the issue.
432 00:40:20.380 ⇒ 00:40:22.130 Demilade Agboola: Yeah, and that’s his wife’s hire as well.
433 00:40:30.190 ⇒ 00:40:36.400 Ashwini Sharma: And what is that? Recommendation, not recommendation, what is that? Third.
434 00:40:39.100 ⇒ 00:40:42.089 Demilade Agboola: And different, and that is why the screen data is higher.
435 00:40:43.060 ⇒ 00:40:44.300 Ashwini Sharma: She insisted.
436 00:40:49.120 ⇒ 00:40:51.149 Demilade Agboola: I think I really want us to emphasize
437 00:40:51.990 ⇒ 00:40:56.229 Demilade Agboola: That the data is higher because there was time elapsed.
438 00:40:56.490 ⇒ 00:41:04.530 Demilade Agboola: So that they can see that the trend is that it’s higher, and because it is higher, and it’s higher because the Spanish data is later.
439 00:41:05.250 ⇒ 00:41:07.250 Demilade Agboola: Than the platform data.
440 00:41:09.530 ⇒ 00:41:11.690 Demilade Agboola: It’s higher, it’s higher with spins.
441 00:41:11.940 ⇒ 00:41:12.400 Uttam Kumaran: Yes.
442 00:41:12.400 ⇒ 00:41:13.640 Ashwini Sharma: Yeah. I wonder…
443 00:41:13.640 ⇒ 00:41:15.440 Demilade Agboola: I want that pattern. Yes.
444 00:41:15.440 ⇒ 00:41:18.700 Ashwini Sharma: It could be lower also, right? We don’t know that.
445 00:41:18.700 ⇒ 00:41:21.920 Uttam Kumaran: No, we do. Why? We do. There’s nothing that’s negative.
446 00:41:22.170 ⇒ 00:41:28.309 Ashwini Sharma: Hold on a second, because I want to correct. Because I’ve put absolute value, that’s why, difference is all…
447 00:41:28.310 ⇒ 00:41:29.320 Demilade Agboola: Right here.
448 00:41:29.840 ⇒ 00:41:34.452 Demilade Agboola: Yes, it’s positive, yes, but, like, there’s no… Do it.
449 00:41:34.980 ⇒ 00:41:49.640 Ashwini Sharma: Okay, there is no negative, because technically, it could be possible, right? For example, for a particular category, there was no sales, right? And suddenly, somebody returned a bulk of items that they had purchased. In that case, the sales numbers are going to come down.
450 00:41:49.640 ⇒ 00:41:55.230 Uttam Kumaran: Most of it is… most of it is zero, and then most of it is 1 or 2 cents off.
451 00:41:55.780 ⇒ 00:41:58.129 Uttam Kumaran: Okay, like, negative few cents off?
452 00:41:58.580 ⇒ 00:42:04.050 Uttam Kumaran: And then there is… everything else is positive. You can scroll, you can see it in 2 seconds.
453 00:42:04.420 ⇒ 00:42:06.529 Ashwini Sharma: Yeah, this is because it’s absolute, right?
454 00:42:06.530 ⇒ 00:42:12.480 Uttam Kumaran: No, no, no, no, no, I’m not even without the absolute value, dude. Look, most of the data set…
455 00:42:12.620 ⇒ 00:42:17.320 Uttam Kumaran: after row 3000 is 0. So ABS doesn’t affect those.
456 00:42:18.040 ⇒ 00:42:22.050 Ashwini Sharma: Yeah, yeah. So, for most of the… discrepancy is just 1 cents, or 2 cents, or.
457 00:42:22.050 ⇒ 00:42:23.460 Uttam Kumaran: I know, but I’m telling you, even
458 00:42:23.850 ⇒ 00:42:27.350 Uttam Kumaran: For the ones that there is a discrepancy.
459 00:42:27.380 ⇒ 00:42:29.100 Ashwini Sharma: It’s not negative.
460 00:42:29.100 ⇒ 00:42:29.919 Uttam Kumaran: You can see that.
461 00:42:30.480 ⇒ 00:42:31.050 Ashwini Sharma: Oh, true.
462 00:42:31.050 ⇒ 00:42:34.789 Uttam Kumaran: It’s just like, yeah, we’re filtered all of them, we’re seeing all of them right now.
463 00:42:34.940 ⇒ 00:42:35.360 Ashwini Sharma: Yeah.
464 00:42:35.360 ⇒ 00:42:44.729 Demilade Agboola: The major discrepancy, so I’m not looking at even the cents, anything above, like, $10 discrepancy appears to be a function of the time that has passed.
465 00:42:45.170 ⇒ 00:42:56.510 Demilade Agboola: Right? We could make you 100 discrepancy is a function of, we have more data from the Spins API than from the
466 00:42:56.820 ⇒ 00:43:01.110 Demilade Agboola: platform API, or from the platform data, and that’s why it’s higher.
467 00:43:01.930 ⇒ 00:43:04.149 Ashwini Sharma: We can say that, yeah, definitely.
468 00:43:06.520 ⇒ 00:43:09.110 Ashwini Sharma: Do you want me to highlight that in the reason?
469 00:43:09.590 ⇒ 00:43:21.359 Demilade Agboola: Yes, because I want them to be able to feel safe with the Spins data, so… and they feel safe that we know why the difference exists. So, if it’s just because, like, this is more recent than that.
470 00:43:21.580 ⇒ 00:43:27.330 Demilade Agboola: That’s why it’s higher. I think that we can put that there, so that’s a good explanation as to why it’s higher.
471 00:43:47.310 ⇒ 00:43:47.990 Ashwini Sharma: Alright.
472 00:43:52.250 ⇒ 00:43:52.910 Uttam Kumaran: Okay.
473 00:43:53.790 ⇒ 00:44:00.009 Uttam Kumaran: So… If we’re comfortable with these, I would drag these discrepancy reasons
474 00:44:00.190 ⇒ 00:44:06.010 Uttam Kumaran: For all the ones that you feel… Have a discrepancy Right.
475 00:44:06.550 ⇒ 00:44:08.709 Uttam Kumaran: So, I would just drag those down.
476 00:44:09.880 ⇒ 00:44:11.339 Ashwini Sharma: Up till here, I think.
477 00:44:11.500 ⇒ 00:44:11.860 Uttam Kumaran: Yeah.
478 00:44:11.860 ⇒ 00:44:15.350 Ashwini Sharma: See, once it is 10 cents, bye.
479 00:44:15.350 ⇒ 00:44:20.050 Uttam Kumaran: This is where I would, in those, I would say you should put in your discrepancy reason
480 00:44:20.270 ⇒ 00:44:27.320 Uttam Kumaran: Is this… this… this may be just within… like… Error bounds, right?
481 00:44:29.300 ⇒ 00:44:31.000 Ashwini Sharma: Okay, I’ll make that change.
482 00:44:31.480 ⇒ 00:44:38.849 Uttam Kumaran: So, do you wanna… do you wanna just take a crack at that, and then if you… can you… if you can… if you don’t mind, yeah, if you can fix the merge cells, and we can close that out.
483 00:44:39.140 ⇒ 00:44:51.100 Uttam Kumaran: then we can knock this out, I think, in the next hour or so. But, like, I guess overall, this is the type of, like, thing that the clients are going to want to see, so we have to spoon-feed it to them.
484 00:44:51.200 ⇒ 00:45:00.529 Uttam Kumaran: Like this, you know? So just be really, really mindful in this way, and yeah, let’s just make sure that every row with an issue that’s in red has a reason.
485 00:45:01.000 ⇒ 00:45:07.220 Uttam Kumaran: And then, when we go send this back, Ashwini, I can help you draft the message, where…
486 00:45:07.220 ⇒ 00:45:07.620 Ashwini Sharma: Okay.
487 00:45:07.620 ⇒ 00:45:14.810 Uttam Kumaran: You can explain, hey, we went through, we redid a bunch of this, here are the overall causes of issues that we found.
488 00:45:15.330 ⇒ 00:45:19.120 Uttam Kumaran: we also put this into the spreadsheet. Like, I think that would be great.
489 00:45:19.250 ⇒ 00:45:25.069 Uttam Kumaran: And then my last ask is if you can work on just a metrics definitions
490 00:45:25.480 ⇒ 00:45:30.299 Uttam Kumaran: You can, like, here on the left, top, bottom left, You can do this last.
491 00:45:30.430 ⇒ 00:45:37.330 Uttam Kumaran: But, if you can just put the definitions for each of these in here… That would be great.
492 00:45:37.330 ⇒ 00:45:40.559 Ashwini Sharma: Some of these, I don’t have an idea on what could be.
493 00:45:40.560 ⇒ 00:45:41.620 Uttam Kumaran: We’ll just leave it, leave it.
494 00:45:41.620 ⇒ 00:45:42.290 Ashwini Sharma: Yeah.
495 00:45:42.290 ⇒ 00:45:47.899 Uttam Kumaran: like, leave it blank, then one of us will fill it out, but I don’t want to ship this without a definition sheet, so…
496 00:45:47.900 ⇒ 00:45:48.380 Ashwini Sharma: Sure.
497 00:45:48.380 ⇒ 00:45:52.609 Demilade Agboola: Also, two things I might want to just add is…
498 00:45:53.210 ⇒ 00:46:04.550 Demilade Agboola: I know they will point out that TDP, and yes, obviously, TDP is an aggregate of a calculation, so it’s not going to work across time periods without recalculating it.
499 00:46:04.560 ⇒ 00:46:15.499 Demilade Agboola: So, I want us to get ahead of that and say, oh, for TDP and for ACV, how can we recalculate it so that we can also do a proper comparison? Because right now, it’s not a proper comparison.
500 00:46:15.640 ⇒ 00:46:27.640 Demilade Agboola: I don’t want them to bring the error back to us. I want us to be able to give them the error, and say, hey, how do we calculate TDP and ACV so we can also QA that, and get those numbers across to you
501 00:46:28.210 ⇒ 00:46:35.510 Demilade Agboola: I don’t want, like… basically, I don’t want them to, like, be on us. I want us to be on them in that regard. And also,
502 00:46:36.110 ⇒ 00:46:37.560 Demilade Agboola: By an all-in, I mean, you could do it in the.
503 00:46:37.560 ⇒ 00:46:43.210 Ashwini Sharma: Wait a second, let me reiterate. You want us to ask the question, how to calculate TDP to them?
504 00:46:43.620 ⇒ 00:46:49.870 Demilade Agboola: Yes, how to calculate CDP and ACV, because it does seem that CDP and ACV are not necessarily matching.
505 00:46:50.010 ⇒ 00:46:51.000 Demilade Agboola: Right?
506 00:46:51.440 ⇒ 00:46:53.850 Ashwini Sharma: Yeah, they won’t match, yeah, that’s what they…
507 00:46:53.850 ⇒ 00:46:54.980 Demilade Agboola: I agree, I agree.
508 00:46:54.980 ⇒ 00:46:55.910 Ashwini Sharma: Right, yeah.
509 00:46:56.340 ⇒ 00:47:02.939 Demilade Agboola: That’s fine, I understand that. What I’m saying is, they will want us to know how we’re gonna handle it.
510 00:47:03.120 ⇒ 00:47:17.999 Demilade Agboola: Right? They… I don’t want them to ask us, okay, so the TDP and ACV that are not matching, how are we going to go about it? I want us to tell them, and say, hey, can we get the calculation for TDP and ACV? And then they say, okay, this is how we will calculate it.
511 00:47:18.000 ⇒ 00:47:34.180 Demilade Agboola: and then we can look at the data that we have, so if it’s maybe, total number of units divided by, like, dollars or something, I’m just giving, like, random numbers, right? We can then do that calculation and compare and say, yes, the TDP matches, or the TDP doesn’t match, and
512 00:47:34.210 ⇒ 00:47:35.699 Demilade Agboola: This is what we need to do.
513 00:47:35.780 ⇒ 00:47:41.829 Demilade Agboola: But I don’t want them to be on us that we’re just, like, waiting for them to spoon-feed us. I want us to be able to get…
514 00:47:42.190 ⇒ 00:47:47.490 Demilade Agboola: our next steps before they tell us anything is wrong, because we know the TDP is Watching.
515 00:47:47.490 ⇒ 00:47:51.169 Ashwini Sharma: Let me send out a message to them on this question, and then…
516 00:47:51.490 ⇒ 00:47:57.689 Ashwini Sharma: maybe I can add that factor into our calculation to arrive at a different TDP numbers based on.
517 00:47:57.690 ⇒ 00:47:58.070 Demilade Agboola: Alright.
518 00:47:58.070 ⇒ 00:47:59.189 Ashwini Sharma: The weekly data,
519 00:48:00.210 ⇒ 00:48:13.180 Demilade Agboola: Sounds good. Because, again, all this, I just don’t want them to be the ones pointing out anything. I want us to be the ones telling them that, like, these are the issues, this is what we’re doing, this is fine, this is not fine, but we’re working on it, you know, that kind of thing.
520 00:48:14.380 ⇒ 00:48:15.320 Ashwini Sharma: Alright, yeah.
521 00:48:16.440 ⇒ 00:48:25.439 Demilade Agboola: And also, if… Oh, Tom, what do you think… should we… should we create a doc for this, in terms of just, like, high level… it could just be using, like, cursor to just create.
522 00:48:25.440 ⇒ 00:48:28.680 Uttam Kumaran: Yeah, I feel like… I’ll create.
523 00:48:28.880 ⇒ 00:48:31.579 Uttam Kumaran: We should maybe do something around this.
524 00:48:33.900 ⇒ 00:48:39.660 Uttam Kumaran: Maybe let’s, like, let’s just see if we can get all the reasons. But, Ashwini, Ashwini, before sending anything to them.
525 00:48:40.600 ⇒ 00:48:53.460 Uttam Kumaran: I really want to be careful here. I really… like, I just want to be… I want to be very clear. I got a message from basically being like, hey, this is, like, not the level of expectation we’re seeing, so we… I don’t want to rush anything.
526 00:48:53.610 ⇒ 00:49:03.600 Uttam Kumaran: So, if you have something to ask the client, let’s discuss internally, right? At this point, I don’t want to ask them about how to define TDP and ACV.
527 00:49:04.040 ⇒ 00:49:04.570 Ashwini Sharma: Okay.
528 00:49:04.570 ⇒ 00:49:05.810 Uttam Kumaran: Finish the sheet.
529 00:49:06.230 ⇒ 00:49:06.650 Ashwini Sharma: Yeah.
530 00:49:06.650 ⇒ 00:49:13.920 Uttam Kumaran: One thing at a time. Let’s finish the sheet first, please, and then let’s then… we can convene internally.
531 00:49:14.080 ⇒ 00:49:19.479 Uttam Kumaran: Right? And I can go figure out if I can find those definitions for you, or how to tee it up.
532 00:49:19.750 ⇒ 00:49:25.339 Uttam Kumaran: But I don’t want to send mess… I want to just, like, try to, like, come up with a unified front here.
533 00:49:27.490 ⇒ 00:49:28.020 Ashwini Sharma: Alright, yeah.
534 00:49:28.320 ⇒ 00:49:29.210 Uttam Kumaran: Okay, okay.
535 00:49:29.210 ⇒ 00:49:34.379 Ashwini Sharma: Okay, I have to run, but let’s just, like, let’s collaborate in the channel, and then…
536 00:49:34.610 ⇒ 00:49:43.919 Uttam Kumaran: we can try to ship this out. And then, Demi, maybe we can, chat after this, after we close this item, on modeling stuff, and you can loop me in there.
537 00:49:44.730 ⇒ 00:49:48.979 Demilade Agboola: Alright, sounds good. Okay, okay. Thank you guys, I appreciate it. Yeah, thank you. Bye.