Meeting Title: Magic Spoon Data Analysis Sync Date: 2026-02-10 Meeting participants: Ashwini Sharma, Michael Thorson
WEBVTT
1 00:01:40.300 ⇒ 00:01:41.640 Michael Thorson: Hey, Sweeney.
2 00:01:42.740 ⇒ 00:01:43.890 Ashwini Sharma: Hey, Michael.
3 00:01:44.550 ⇒ 00:01:45.280 Ashwini Sharma: How are you?
4 00:01:46.020 ⇒ 00:01:47.950 Michael Thorson: Good, how are you doing today?
5 00:01:47.950 ⇒ 00:01:48.820 Ashwini Sharma: I’m good.
6 00:01:49.530 ⇒ 00:01:53.080 Ashwini Sharma: Okay, let me quickly dive into that. Data…
7 00:01:53.080 ⇒ 00:01:53.670 Michael Thorson: Right.
8 00:01:54.010 ⇒ 00:01:59.470 Michael Thorson: Yeah, thanks for the… thanks for the work. You and, you, Tom, have been working hard. Appreciate it.
9 00:01:59.470 ⇒ 00:02:01.450 Ashwini Sharma: Oh, no problem. You’re welcome.
10 00:02:01.790 ⇒ 00:02:08.729 Ashwini Sharma: Okay, so I saw the formula that you have added for average of this thing, right? ACV?
11 00:02:09.840 ⇒ 00:02:13.880 Ashwini Sharma: Which was, let me just quickly open that as well.
12 00:02:16.310 ⇒ 00:02:18.540 Michael Thorson: Oh, good.
13 00:02:22.220 ⇒ 00:02:24.709 Ashwini Sharma: Was it this one? No, this is not the one.
14 00:02:31.200 ⇒ 00:02:32.339 Ashwini Sharma: This was the one.
15 00:02:33.570 ⇒ 00:02:44.430 Ashwini Sharma: Yeah, so basically it’s the average of, average percentage HCV for product across all weeks’ time, divided by the number of weeks. So it’s basically the average of
16 00:02:46.060 ⇒ 00:02:47.630 Ashwini Sharma: ACV, right?
17 00:02:48.440 ⇒ 00:02:49.160 Michael Thorson: Yeah.
18 00:02:50.150 ⇒ 00:02:55.349 Ashwini Sharma: This is the average, right? Sum divided by number of weeks. That’s the average, right?
19 00:02:55.550 ⇒ 00:03:01.849 Ashwini Sharma: But it does not match. So, if you see here, like, one of the examples is this one.
20 00:03:02.370 ⇒ 00:03:07.049 Ashwini Sharma: From the platform, I get it as 70.5, whereas
21 00:03:07.650 ⇒ 00:03:10.670 Ashwini Sharma: the Spence API aggregated, averaged out.
22 00:03:11.010 ⇒ 00:03:16.500 Ashwini Sharma: gives 67.37, right? We can go into the individual data and then see it.
23 00:03:17.210 ⇒ 00:03:18.180 Michael Thorson: Hmm.
24 00:03:20.880 ⇒ 00:03:22.979 Ashwini Sharma: Yeah, do you… what’s your.
25 00:03:24.420 ⇒ 00:03:27.740 Michael Thorson: Can you show me your average calculation really quick?
26 00:03:27.740 ⇒ 00:03:28.260 Ashwini Sharma: Yeah.
27 00:03:29.310 ⇒ 00:03:30.820 Ashwini Sharma: And then this shit.
28 00:03:33.900 ⇒ 00:03:34.670 Ashwini Sharma: This one.
29 00:03:35.640 ⇒ 00:03:39.000 Michael Thorson: Yeah, that’s probably what’s happening here.
30 00:03:40.010 ⇒ 00:03:44.449 Ashwini Sharma: We can look at the raw data and then see if that’s… this is…
31 00:03:45.050 ⇒ 00:03:49.460 Ashwini Sharma: This is wrong, right? Give me a second, right, let’s…
32 00:03:51.550 ⇒ 00:03:53.800 Ashwini Sharma: This sort of key account, okay.
33 00:04:00.340 ⇒ 00:04:03.810 Ashwini Sharma: Yeah, we’re looking at ACV average, this one, right?
34 00:04:04.630 ⇒ 00:04:08.859 Ashwini Sharma: Key account… And then Festival Foods.
35 00:04:17.440 ⇒ 00:04:19.519 Ashwini Sharma: Brand, this thing.
36 00:04:23.580 ⇒ 00:04:24.940 Ashwini Sharma: Oh, it’s the same.
37 00:04:27.160 ⇒ 00:04:29.020 Ashwini Sharma: Subcategory is this?
38 00:04:31.490 ⇒ 00:04:32.420 Michael Thorson: Yep, comment.
39 00:04:34.080 ⇒ 00:04:40.050 Ashwini Sharma: And… TPL is product universe.
40 00:04:42.200 ⇒ 00:04:43.070 Michael Thorson: Fitting…
41 00:04:45.020 ⇒ 00:04:45.420 Ashwini Sharma: Yeah.
42 00:04:45.420 ⇒ 00:04:46.260 Michael Thorson: Certainly not.
43 00:04:49.150 ⇒ 00:04:53.429 Michael Thorson: 58% for getting those.
44 00:04:56.400 ⇒ 00:04:57.290 Michael Thorson: I don’t gotten…
45 00:05:18.210 ⇒ 00:05:19.080 Michael Thorson: this.
46 00:05:20.750 ⇒ 00:05:21.290 Ashwini Sharma: Yeah.
47 00:05:21.730 ⇒ 00:05:24.490 Ashwini Sharma: So… see this…
48 00:05:26.480 ⇒ 00:05:28.960 Michael Thorson: This is what we’re getting. Oh, nice.
49 00:05:29.900 ⇒ 00:05:30.510 Ashwini Sharma: Sorry?
50 00:05:30.900 ⇒ 00:05:33.700 Michael Thorson: Okay, keep, keep going. Sorry.
51 00:05:33.700 ⇒ 00:05:34.620 Ashwini Sharma: Yeah, yeah.
52 00:05:34.790 ⇒ 00:05:38.810 Ashwini Sharma: So, I’m looking at, data for the last 4 weeks.
53 00:05:39.140 ⇒ 00:05:41.290 Ashwini Sharma: 420 to 313, right?
54 00:05:41.780 ⇒ 00:05:46.069 Ashwini Sharma: This is the TDP values, and let’s look at the…
55 00:05:46.550 ⇒ 00:05:49.709 Ashwini Sharma: ACV values, so this is the ACV values, right?
56 00:05:49.990 ⇒ 00:05:56.970 Ashwini Sharma: If I… let’s, let’s take all four of these, and… Reported over here.
57 00:05:58.320 ⇒ 00:06:02.289 Ashwini Sharma: The average comes at 67.375.
58 00:06:03.410 ⇒ 00:06:04.040 Michael Thorson: Let’s just…
59 00:06:04.040 ⇒ 00:06:09.479 Ashwini Sharma: 7.37, and… What did the platform say over here?
60 00:06:11.430 ⇒ 00:06:15.740 Ashwini Sharma: Oh, it did… oh, the platform was 70.5, right?
61 00:06:16.110 ⇒ 00:06:17.870 Michael Thorson: 70.5.
62 00:06:17.870 ⇒ 00:06:18.430 Ashwini Sharma: Yeah.
63 00:06:20.620 ⇒ 00:06:32.600 Michael Thorson: Cool. Can you go back to the other query really quick? So, yeah, and that’s kind of what’s expected, and I thought that’s what we were chatting about last week, where it’s like…
64 00:06:32.800 ⇒ 00:06:41.609 Michael Thorson: I’m… I’m… full transparency, like, I’m no master of, like, how spins define their metrics, but, like, just averaging across the four weeks.
65 00:06:41.900 ⇒ 00:06:49.010 Michael Thorson: outright won’t be the correct way to average. We actually need a weighted average, because the…
66 00:06:49.560 ⇒ 00:07:01.169 Michael Thorson: I think, and that’s why I was, like, interested, the market all commodity volume, like, does change slightly week to week, I think, which I think would change…
67 00:07:01.400 ⇒ 00:07:04.010 Ashwini Sharma: Yeah, I have that over here also, right?
68 00:07:04.010 ⇒ 00:07:09.340 Michael Thorson: Yeah, 113 is… A bit strange.
69 00:07:10.970 ⇒ 00:07:13.359 Michael Thorson: So maybe, like…
70 00:07:14.130 ⇒ 00:07:20.270 Michael Thorson: Let me double-check, then, my, like, notes really quick, and we can maybe get this resolved, because, you know what I’m saying, like.
71 00:07:20.720 ⇒ 00:07:28.730 Michael Thorson: the… from what I understand, like, the average ACV is, like, is kind of a statistical measure of, like, distrib… like, sorry, not distribution points, but, like, how…
72 00:07:29.060 ⇒ 00:07:35.450 Michael Thorson: like… Like, this… it’s, like, spread across stores or something?
73 00:07:35.620 ⇒ 00:07:39.089 Michael Thorson: I’m honestly, like, not even super clear in the definition.
74 00:07:39.190 ⇒ 00:07:44.279 Michael Thorson: But as the, like, total, like, market also changes in size.
75 00:07:44.410 ⇒ 00:07:52.860 Michael Thorson: Like, if we’re… if the total market is selling more one week, we just need to, like, actually weight the average by this next term.
76 00:07:53.650 ⇒ 00:07:54.840 Michael Thorson: This one?
77 00:07:55.000 ⇒ 00:08:02.580 Michael Thorson: I think that’s the right term, like, I want to double-check that really quick, because, like, I’m honestly… I haven’t done the…
78 00:08:03.150 ⇒ 00:08:10.750 Michael Thorson: like, maybe it’s worthwhile just penciling these calculations together. I think this is a really good slice of the pie. So let me just pull up these notes I had with…
79 00:08:11.020 ⇒ 00:08:11.670 Ashwini Sharma: Sure.
80 00:08:12.400 ⇒ 00:08:13.549 Michael Thorson: Yeah, yeah, yeah.
81 00:08:13.850 ⇒ 00:08:17.770 Ashwini Sharma: So, while you pull up, I’m going to pull the spins definitions, right?
82 00:08:18.810 ⇒ 00:08:19.970 Michael Thorson: Sweet, yeah.
83 00:08:19.970 ⇒ 00:08:21.440 Ashwini Sharma: some glossary.
84 00:08:31.970 ⇒ 00:08:33.779 Michael Thorson: Where did I put that?
85 00:08:39.049 ⇒ 00:08:40.560 Michael Thorson: I don’t have to scare.
86 00:08:47.490 ⇒ 00:08:51.940 Michael Thorson: Close, though. I can feel it.
87 00:09:06.020 ⇒ 00:09:08.680 Michael Thorson: Where did I put that?
88 00:09:16.440 ⇒ 00:09:17.350 Michael Thorson: Hmm.
89 00:09:53.150 ⇒ 00:09:57.540 Michael Thorson: Yeah, sorry, I’m, like, trying to find my notes, I’m not sure what… No problem.
90 00:09:58.720 ⇒ 00:10:01.450 Michael Thorson: Oh, here we go. I think this is what we wanted.
91 00:10:15.670 ⇒ 00:10:21.990 Michael Thorson: Good. Did you want to talk through this first? I’m still… Still looking for the field.
92 00:10:22.590 ⇒ 00:10:25.079 Ashwini Sharma: Sure, no problem. I’m just reading.
93 00:10:25.750 ⇒ 00:10:26.619 Michael Thorson: Okay, okay.
94 00:10:29.020 ⇒ 00:10:31.830 Michael Thorson: Where’d I put that?
95 00:11:00.640 ⇒ 00:11:01.490 Michael Thorson: Hmm.
96 00:11:54.200 ⇒ 00:12:01.019 Michael Thorson: Okay, I can’t find the file I was looking for, so… yeah, sorry about that.
97 00:12:01.020 ⇒ 00:12:06.229 Ashwini Sharma: No, no problem. I mean, we can look at the definition that Spence says, right? This is… this is for
98 00:12:06.400 ⇒ 00:12:09.250 Ashwini Sharma: Average ACV, or average percentage ACV?
99 00:12:09.370 ⇒ 00:12:12.789 Ashwini Sharma: They interchange it, right? Are you able to see my screen?
100 00:12:13.380 ⇒ 00:12:14.539 Michael Thorson: Yep, I see your screen.
101 00:12:14.540 ⇒ 00:12:18.739 Ashwini Sharma: Right, so this is what they do, right? So, let’s look at 4-week data.
102 00:12:18.970 ⇒ 00:12:23.369 Ashwini Sharma: And average ACV is 35, 50, 55, 65.
103 00:12:23.540 ⇒ 00:12:26.889 Ashwini Sharma: And what they’re doing is, you know, averaging it out, right?
104 00:12:27.350 ⇒ 00:12:28.220 Michael Thorson: I am.
105 00:12:29.960 ⇒ 00:12:37.460 Ashwini Sharma: But this does not work over here, so maybe, like, we should reach out to the Spins folks, and then ask, like, why this is the case.
106 00:12:37.810 ⇒ 00:12:45.709 Ashwini Sharma: Is there any other formula that’s involved? But at least as per the definition, this does not seem to be working out, right?
107 00:12:46.260 ⇒ 00:12:46.730 Michael Thorson: Yeah.
108 00:12:46.730 ⇒ 00:12:52.540 Ashwini Sharma: same… same cases there for TDP, right? So, if… if I go back, right, so yeah, TDP.
109 00:12:52.540 ⇒ 00:12:53.310 Michael Thorson: Huh.
110 00:12:53.310 ⇒ 00:13:02.970 Ashwini Sharma: So, what it says in DDP is, like, DDP is equal to the maximum of, ACV, right? So,
111 00:13:03.590 ⇒ 00:13:08.829 Ashwini Sharma: So, for example, like, sorry, over here, right?
112 00:13:12.150 ⇒ 00:13:17.920 Ashwini Sharma: So, again, this is the difference over here, right? You can see the TDB value is 90.
113 00:13:18.070 ⇒ 00:13:19.400 Ashwini Sharma: Right, for this week.
114 00:13:19.890 ⇒ 00:13:24.589 Ashwini Sharma: And max percent all, ACV is 57.
115 00:13:24.750 ⇒ 00:13:27.419 Ashwini Sharma: Ideally, TDP should have been equal to this one.
116 00:13:27.640 ⇒ 00:13:31.380 Ashwini Sharma: Oh, sorry, TDP should have been…
117 00:13:31.540 ⇒ 00:13:36.700 Ashwini Sharma: Yeah, this is… this is a weekly TDB. Yeah, weekly TDB should have been… Same as this thing.
118 00:13:36.880 ⇒ 00:13:37.920 Ashwini Sharma: But it’s not.
119 00:13:38.550 ⇒ 00:13:46.860 Ashwini Sharma: And since it’s… it’s a… UPC is not here, right? In these cases, there are no multiple… there’s not even a single UPC over here, right?
120 00:13:47.220 ⇒ 00:13:48.829 Michael Thorson: Yeah, cause we’re looking at the…
121 00:13:49.050 ⇒ 00:13:49.660 Ashwini Sharma: Yeah.
122 00:13:49.840 ⇒ 00:13:52.529 Michael Thorson: Yeah, because we’re looking at the subcategory level.
123 00:13:53.470 ⇒ 00:13:58.560 Ashwini Sharma: No, there are cases where a single subcategory can have multiple UPCs.
124 00:13:59.170 ⇒ 00:14:00.020 Michael Thorson: Okay.
125 00:14:00.390 ⇒ 00:14:00.770 Ashwini Sharma: Right.
126 00:14:00.770 ⇒ 00:14:02.679 Michael Thorson: Reporting level equals brand.
127 00:14:03.010 ⇒ 00:14:07.970 Ashwini Sharma: Right. In that case, it’s… it’s the sum of… TDPs.
128 00:14:08.170 ⇒ 00:14:10.340 Ashwini Sharma: over those UPCs.
129 00:14:11.060 ⇒ 00:14:13.630 Ashwini Sharma: So, if a subcategory has 3 UPCs.
130 00:14:14.100 ⇒ 00:14:19.280 Ashwini Sharma: the TDP value seems to be summed up value of all those UPCs.
131 00:14:19.980 ⇒ 00:14:21.730 Ashwini Sharma: And that matches.
132 00:14:21.880 ⇒ 00:14:26.250 Ashwini Sharma: But, but across 3 weeks, 4 weeks, right?
133 00:14:26.360 ⇒ 00:14:28.049 Ashwini Sharma: the TDB does not match.
134 00:14:29.390 ⇒ 00:14:36.580 Ashwini Sharma: Ideally, it should have been maximum of this one, and… somehow it… It’s not matching, so…
135 00:14:38.260 ⇒ 00:14:42.750 Ashwini Sharma: Yeah, that’s why, I just wanted to show you this to you,
136 00:14:44.080 ⇒ 00:14:52.519 Ashwini Sharma: And there is no other numeric value that I can utilize to, you know, calculate some kind of a weighted average or anything like that.
137 00:14:53.890 ⇒ 00:14:58.269 Michael Thorson: Because the, market all commodity volume is…
138 00:14:58.390 ⇒ 00:15:03.009 Michael Thorson: as interesting as this 1-13, I thought that this was supposed to be a much…
139 00:15:03.980 ⇒ 00:15:05.589 Michael Thorson: Like, that’s why I was like.
140 00:15:06.010 ⇒ 00:15:10.589 Michael Thorson: Let me… I’m gonna go and platform really quick and just, like, take a quick look at…
141 00:15:11.230 ⇒ 00:15:13.900 Michael Thorson: the available fields. Sure.
142 00:15:14.670 ⇒ 00:15:33.370 Michael Thorson: Maybe that’ll reveal, like… yeah, I confirmed with JT that that should be the field that we wait by, but I expected it to be, like, a much larger number than, like, 113, so I was like, maybe that’s the wrong weighting term? That’s my first thought. Maybe there is a waiting term that we need to dig into.
143 00:15:33.510 ⇒ 00:15:42.629 Michael Thorson: And to your point, like, yeah, I think Spins does, like, we need to take these questions to them and, like, figure out how to resolve this, but,
144 00:15:43.590 ⇒ 00:15:51.150 Michael Thorson: Yeah, we’ll see there. Okay, extracts… here, I can share my screen, too, so we’re just looking at the same place, in platform.
145 00:15:51.150 ⇒ 00:15:53.680 Ashwini Sharma: So I don’t know if you’ve been in here.
146 00:15:53.740 ⇒ 00:16:01.019 Michael Thorson: Yeah, sorry we couldn’t get you a login for Platform 2. We only have, like… we only pay for, like, two or something, so I share one.
147 00:16:01.020 ⇒ 00:16:02.740 Ashwini Sharma: That’s fine, not an issue.
148 00:16:02.740 ⇒ 00:16:14.420 Michael Thorson: Yeah, cool. But yeah, it’s like, you’ll recognize all the fields, it’s, you know, I’ll start to pull in all the products, you’ll see subcategories that, like, we have access to in Magic Spoon.
149 00:16:14.420 ⇒ 00:16:14.850 Ashwini Sharma: Yeah.
150 00:16:14.850 ⇒ 00:16:20.849 Michael Thorson: And then, like, you can look at, like, the brand level as well. So, like, the case of this…
151 00:16:21.070 ⇒ 00:16:22.920 Michael Thorson: Exercise?
152 00:16:23.080 ⇒ 00:16:26.790 Michael Thorson: Look at Magic Spoon… again, I can only look at, like.
153 00:16:27.270 ⇒ 00:16:32.130 Michael Thorson: time frames, and are you looking at… are you looking at the 2025 April?
154 00:16:32.130 ⇒ 00:16:33.680 Ashwini Sharma: 420. Yes, 420.
155 00:16:33.680 ⇒ 00:16:34.610 Michael Thorson: Funnett article.
156 00:16:34.940 ⇒ 00:16:38.149 Michael Thorson: So we’ll, like, I’ll just pull that in, and then geographies…
157 00:16:39.140 ⇒ 00:16:42.810 Michael Thorson: Is there a specific geo you were looking at, or…
158 00:16:42.810 ⇒ 00:16:49.409 Ashwini Sharma: I was, yes, I was looking at, geo was Festival Foods.
159 00:16:50.310 ⇒ 00:16:57.140 Michael Thorson: Okay, and, like, it’s… it’s layered, which is so annoying, like…
160 00:16:58.210 ⇒ 00:17:05.210 Michael Thorson: this is, like, how… this is interesting to see, because, like, they… I feel like the geography… levels.
161 00:17:05.569 ⇒ 00:17:13.409 Michael Thorson: in the UI, look a little differently. It’s like, you have your aggregated geography over here, and then also, like, on the retail account side.
162 00:17:13.410 ⇒ 00:17:14.460 Ashwini Sharma: Oh, okay.
163 00:17:14.740 ⇒ 00:17:20.319 Michael Thorson: Yeah, so it’s, like, it’s slightly segmented in the UI, which kind of has helped me understand.
164 00:17:20.530 ⇒ 00:17:25.530 Michael Thorson: Festival Foods…
165 00:17:26.010 ⇒ 00:17:32.579 Michael Thorson: And I think… oh, no, I, like, need to select that hierarchy. It’s, like, kind of a filtering mess.
166 00:17:33.730 ⇒ 00:17:40.800 Michael Thorson: Yeah. And then measures, this is where I was interested. It’s… Man
167 00:17:41.370 ⇒ 00:17:49.210 Michael Thorson: There’s, like, categories, and then I want… don’t want all the measures. I want, like, total… Market…
168 00:17:49.470 ⇒ 00:17:55.100 Michael Thorson: Here’s market ACV… And then… what was this?
169 00:17:56.430 ⇒ 00:18:00.230 Michael Thorson: This would be a measure of distribution, I think?
170 00:18:06.860 ⇒ 00:18:07.870 Michael Thorson: Yeah.
171 00:18:09.980 ⇒ 00:18:13.250 Michael Thorson: Hmm… HTTP…
172 00:18:21.180 ⇒ 00:18:28.690 Michael Thorson: I’m gonna add wait weeks, I don’t know if that’s required, that might just be… Unnecessary…
173 00:18:29.190 ⇒ 00:18:40.100 Michael Thorson: Average ACV, max AV TDP, average weekly TDP, Let’s take some available fields…
174 00:18:40.980 ⇒ 00:18:49.350 Michael Thorson: There should be, like, a market performance or something. First week selling, velocities… Velocities…
175 00:18:59.450 ⇒ 00:19:04.429 Michael Thorson: Thanks for the patience on this. We had a feeling this was going to be really straightforward, so…
176 00:19:05.970 ⇒ 00:19:08.899 Michael Thorson: Glad we all… glad we got to this point, I guess.
177 00:19:09.950 ⇒ 00:19:10.790 Michael Thorson: Yeah.
178 00:19:21.970 ⇒ 00:19:26.999 Michael Thorson: What’d this term be… Store performance, maybe?
179 00:19:28.220 ⇒ 00:19:30.679 Michael Thorson: Base pricing, promo…
180 00:19:44.330 ⇒ 00:19:54.580 Michael Thorson: Yeah, it’s like, it’s always been really interesting for me to, like, also see how these are categorized in the UI, just because, like, I’m not as familiar with all the fields, so it’s like…
181 00:19:54.900 ⇒ 00:20:01.719 Michael Thorson: sometimes a field is, like, very specific, but I thought it applied to the whole dataset. Like, this merch volume, I wasn’t…
182 00:20:02.120 ⇒ 00:20:04.410 Michael Thorson: And, like, how it’s split to conventional.
183 00:20:05.830 ⇒ 00:20:13.040 Ashwini Sharma: Yeah, that hierarchy part is not very clear, right? So, I mean, what’s the top-level entity, right?
184 00:20:14.650 ⇒ 00:20:15.160 Michael Thorson: Yeah.
185 00:20:15.240 ⇒ 00:20:21.250 Ashwini Sharma: Category has multiple subcategories, and a brand has multiple categories.
186 00:20:21.470 ⇒ 00:20:28.720 Michael Thorson: Yeah, I mean, this… that hierarchy for reporting level, I, like, I mapped it out in…
187 00:20:28.870 ⇒ 00:20:36.879 Michael Thorson: this shared document. I don’t know if you saw these, but, like, yeah, there’s, like, some mapping details there. It’s kind of hard to, like, parse out…
188 00:20:37.150 ⇒ 00:20:38.770 Michael Thorson: Took me a long time.
189 00:20:39.860 ⇒ 00:20:43.589 Michael Thorson: Okay, here’s average ACV, maybe that’s in here.
190 00:20:45.070 ⇒ 00:20:54.669 Michael Thorson: Tdp… There’s, like, slices…
191 00:20:59.120 ⇒ 00:21:08.960 Michael Thorson: Base dollars… sorry, I’m just chopping for… Promo, promotion effectiveness, velocity…
192 00:21:17.620 ⇒ 00:21:24.329 Michael Thorson: base… Demographics, market ACV, population.
193 00:21:24.470 ⇒ 00:21:25.840 Michael Thorson: I mean…
194 00:21:26.060 ⇒ 00:21:34.999 Michael Thorson: These are kind of the… yeah, it’s interesting. Market ACV is in demographics, so, like, this bucket, I’ll pull the other terms that are… or the measures that are in there.
195 00:21:35.290 ⇒ 00:21:39.189 Michael Thorson: And then… promotional effectiveness, we don’t want that.
196 00:21:39.530 ⇒ 00:21:47.160 Michael Thorson: Household… I’m just wondering if there’s another kind of, like, market-level bucket.
197 00:21:48.720 ⇒ 00:21:51.570 Michael Thorson: We need to look at base store performance…
198 00:21:53.530 ⇒ 00:21:56.900 Michael Thorson: Merch pricing… I don’t know. No.
199 00:21:57.510 ⇒ 00:22:03.250 Michael Thorson: Yeah, like, just confirming. Okay, I’ll pull this, though, and we can take a look at the Market ACV.
200 00:22:03.250 ⇒ 00:22:05.859 Ashwini Sharma: Sure, sure. Did you take a…
201 00:22:05.970 ⇒ 00:22:09.390 Ashwini Sharma: For 4 weeks, or was it for 52 weeks?
202 00:22:09.590 ⇒ 00:22:11.549 Ashwini Sharma: This is for 4 weeks. Okay.
203 00:22:11.930 ⇒ 00:22:18.100 Michael Thorson: Yeah Wait weeks, just kind of scanning through this really quick.
204 00:22:19.190 ⇒ 00:22:21.299 Michael Thorson: Max ACV…
205 00:22:22.600 ⇒ 00:22:27.499 Michael Thorson: Okay, let’s just give that, give that a shot. Also, like, I’ll pull in dollars and units.
206 00:22:27.500 ⇒ 00:22:28.130 Ashwini Sharma: Okay.
207 00:22:30.150 ⇒ 00:22:38.020 Michael Thorson: Yeah, I just want to see if, like, we’re getting the right number here, I guess, and wanted this at the UPC level, or what level are you looking at?
208 00:22:38.020 ⇒ 00:22:43.989 Ashwini Sharma: I’m looking at the UPC level, so there is category, subcategory, and everything else, right?
209 00:22:47.740 ⇒ 00:22:56.150 Michael Thorson: Right, you wanted category, so I’ve got a great brand, UPC. Like, for that specific… we were just thinking those four weeks, I think that was at the subcategory level, there was…
210 00:22:56.480 ⇒ 00:23:01.670 Ashwini Sharma: Yes, the UPC is null, but it goes up to subcategory.
211 00:23:03.840 ⇒ 00:23:06.810 Michael Thorson: Right, right, right. Sweet.
212 00:23:09.480 ⇒ 00:23:11.689 Michael Thorson: I’ll just pull this out really quick.
213 00:23:12.380 ⇒ 00:23:16.170 Michael Thorson: Thanks for the patience. I wasn’t sure what we needed to pull.
214 00:23:20.760 ⇒ 00:23:22.770 Ashwini Sharma: Does it take a lot of time, this thing?
215 00:23:22.900 ⇒ 00:23:23.900 Ashwini Sharma: Export?
216 00:23:24.250 ⇒ 00:23:28.969 Michael Thorson: No, it’s… it runs the same query, it’s just… I think the UI doesn’t refresh.
217 00:23:28.970 ⇒ 00:23:29.390 Ashwini Sharma: Oh.
218 00:23:30.300 ⇒ 00:23:32.930 Michael Thorson: It’s pretty… it’s pretty quick, though.
219 00:23:33.200 ⇒ 00:23:38.520 Michael Thorson: This should be a small query, too, so I’m a little nervous, but… We’ll see.
220 00:23:42.670 ⇒ 00:23:47.519 Michael Thorson: Just give it a refresh… saved extracts… there it is.
221 00:23:47.520 ⇒ 00:23:48.170 Ashwini Sharma: Huh?
222 00:23:50.150 ⇒ 00:23:52.370 Michael Thorson: Yeah, it’s like similar performance.
223 00:23:53.270 ⇒ 00:23:58.599 Michael Thorson: It’s another API that, are they kicking out in Google…
224 00:23:58.600 ⇒ 00:24:00.750 Ashwini Sharma: Oh, okay, this is the same, yeah.
225 00:24:02.700 ⇒ 00:24:03.850 Michael Thorson: Yeah, alright.
226 00:24:05.610 ⇒ 00:24:08.980 Michael Thorson: 36 megabytes…
227 00:24:12.650 ⇒ 00:24:20.259 Michael Thorson: Which one is this? So, you get a lot open.
228 00:24:20.850 ⇒ 00:24:25.130 Michael Thorson: And my friend, and the group that called Missouri people and all the
229 00:24:28.240 ⇒ 00:24:37.589 Michael Thorson: There it goes. Cool. So obviously, like, it’s gonna kick out way more geographies than we need, so I’ll just, like, filter this down really quick.
230 00:24:39.650 ⇒ 00:24:45.350 Michael Thorson: so we wanted… it was, like, Fest something?
231 00:24:45.350 ⇒ 00:24:49.000 Ashwini Sharma: Yeah, geography was Festival Foods.
232 00:24:49.540 ⇒ 00:24:55.390 Michael Thorson: Festival foods, 4 weeks, and then I’m just gonna filter down exactly to what you were looking at.
233 00:24:55.390 ⇒ 00:24:56.560 Ashwini Sharma: Yeah, yeah, I think.
234 00:24:56.560 ⇒ 00:24:59.740 Michael Thorson: Are you looking… Product-level brand, right?
235 00:25:00.040 ⇒ 00:25:02.440 Ashwini Sharma: Product level is brand, yes.
236 00:25:02.440 ⇒ 00:25:03.070 Michael Thorson: Yeah.
237 00:25:03.370 ⇒ 00:25:05.119 Michael Thorson: UPC is gone.
238 00:25:05.680 ⇒ 00:25:13.220 Ashwini Sharma: And, you already got 4 records. What’s the, this one, category?
239 00:25:15.680 ⇒ 00:25:16.749 Michael Thorson: What was that?
240 00:25:17.000 ⇒ 00:25:19.390 Ashwini Sharma: What’s the actual category over here?
241 00:25:20.450 ⇒ 00:25:22.040 Ashwini Sharma: Oh, you didn’t get category.
242 00:25:22.570 ⇒ 00:25:23.360 Michael Thorson: Oh.
243 00:25:23.640 ⇒ 00:25:24.800 Michael Thorson: My mistake.
244 00:25:24.800 ⇒ 00:25:29.649 Ashwini Sharma: No, no issues, no issues. This is for the, this is for what? This is,
245 00:25:30.610 ⇒ 00:25:33.440 Ashwini Sharma: UPC is null, yeah, yeah, yeah, yeah.
246 00:25:33.440 ⇒ 00:25:37.580 Michael Thorson: What’s your dollars? Just making sure we’re looking at the same thing.
247 00:25:37.890 ⇒ 00:25:47.619 Ashwini Sharma: No, that… the data doesn’t match, right? Product universe should be TPL. You’ve got only 2 records, I get 4 records.
248 00:25:48.780 ⇒ 00:25:50.110 Michael Thorson: Fair TPO.
249 00:25:50.480 ⇒ 00:25:51.150 Ashwini Sharma: Yeah.
250 00:25:53.390 ⇒ 00:25:54.270 Michael Thorson: Okay.
251 00:25:54.700 ⇒ 00:25:59.870 Michael Thorson: So there’s a filtering issue, for sure. On my end.
252 00:26:00.230 ⇒ 00:26:05.270 Michael Thorson: But let’s just look, maybe… I’m just, like, gonna walk down the list a little bit.
253 00:26:06.010 ⇒ 00:26:14.030 Michael Thorson: Interesting. Okay, market ACV is 113 as well, so, like…
254 00:26:14.170 ⇒ 00:26:17.420 Michael Thorson: So this is, like, a market-level term, that makes sense.
255 00:26:17.960 ⇒ 00:26:21.670 Michael Thorson: Cool.
256 00:26:22.210 ⇒ 00:26:25.679 Michael Thorson: Just, like, move up in the aggregation a little bit, but…
257 00:26:28.870 ⇒ 00:26:30.210 Michael Thorson: Alright.
258 00:26:35.310 ⇒ 00:26:39.830 Michael Thorson: This is one of, like, since we’re looking at this together, too,
259 00:26:42.580 ⇒ 00:26:46.660 Michael Thorson: Yeah, I think there’s a nuance in, I think, the way…
260 00:26:46.990 ⇒ 00:26:53.499 Michael Thorson: we’re looking at ACV, like, for the brands, like, for Magic Spoon, for example.
261 00:26:54.360 ⇒ 00:26:59.080 Ashwini Sharma: Yeah, and the dates are one year apart, right? So, probably…
262 00:26:59.080 ⇒ 00:26:59.620 Michael Thorson: Yo.
263 00:26:59.620 ⇒ 00:27:04.059 Ashwini Sharma: You took a one week a year ago, or something like that, right?
264 00:27:04.720 ⇒ 00:27:06.980 Michael Thorson: Yeah, so we really only have one row of information.
265 00:27:06.980 ⇒ 00:27:09.150 Ashwini Sharma: Yeah, that’s only one row, right?
266 00:27:09.600 ⇒ 00:27:12.280 Michael Thorson: Yeah, which makes sense, because…
267 00:27:12.670 ⇒ 00:27:26.190 Michael Thorson: the, like, based on the hierarchy, I think this would make sense. It’s basically saying, okay, TPL, like, at the brand level, you have multiple subcategories, and then you have multiple UPCs below that.
268 00:27:26.600 ⇒ 00:27:27.280 Ashwini Sharma: Yeah.
269 00:27:27.280 ⇒ 00:27:33.029 Michael Thorson: So, I would say, like, okay, subcategories, these are all, and then I’ll say this is only for…
270 00:27:39.290 ⇒ 00:27:40.060 Ashwini Sharma: Hello.
271 00:27:41.300 ⇒ 00:27:42.660 Michael Thorson: Where’d I go?
272 00:27:43.150 ⇒ 00:27:46.049 Ashwini Sharma: Yeah, yeah, I lost you there for some time, yeah.
273 00:27:47.070 ⇒ 00:27:50.370 Michael Thorson: Yeah, sorry. Oh, sorry. Hello?
274 00:27:50.370 ⇒ 00:27:51.700 Ashwini Sharma: Yeah, I can hear you now.
275 00:27:52.290 ⇒ 00:27:53.290 Michael Thorson: Okay.
276 00:27:53.530 ⇒ 00:27:58.649 Michael Thorson: Yeah, I was just gonna, like, filter down to, like, kind of what we would expect to see.
277 00:27:59.190 ⇒ 00:28:02.590 Michael Thorson: Only look at 2026.
278 00:28:03.850 ⇒ 00:28:05.419 Michael Thorson: Period end date.
279 00:28:07.060 ⇒ 00:28:11.640 Michael Thorson: Oh, interesting. Wow, something strange happened with that data, I guess.
280 00:28:12.750 ⇒ 00:28:14.130 Michael Thorson: Oh, that’s why.
281 00:28:14.470 ⇒ 00:28:17.010 Michael Thorson: We want to look at 2025, excuse me.
282 00:28:17.540 ⇒ 00:28:19.450 Michael Thorson: Yeah, I must have pulled this out.
283 00:28:19.640 ⇒ 00:28:20.790 Michael Thorson: Correctly.
284 00:28:24.500 ⇒ 00:28:25.750 Michael Thorson: There we go.
285 00:28:25.920 ⇒ 00:28:29.080 Michael Thorson: I’ll sort this descending, just for…
286 00:28:33.550 ⇒ 00:28:38.779 Michael Thorson: Earth Hunter. So is this… Is this kind of matching your expectations, I guess?
287 00:28:38.780 ⇒ 00:28:45.480 Ashwini Sharma: No, no, no, no. Product level is the reporting level, right? So that’s just a brand that I’m looking at.
288 00:28:47.360 ⇒ 00:28:49.640 Michael Thorson: Yeah, that is looking pretty…
289 00:28:50.360 ⇒ 00:28:56.410 Michael Thorson: Weird. Cause it’s… but do you understand… do you understand, like, this, oh, interesting.
290 00:28:57.530 ⇒ 00:28:59.750 Michael Thorson: Yeah, like, this seems to be, like…
291 00:29:02.960 ⇒ 00:29:03.520 Michael Thorson: That was true.
292 00:29:03.520 ⇒ 00:29:10.640 Ashwini Sharma: Dollar’s value. Yeah, dollars value for the first row seems very low for a 4-week period, right?
293 00:29:11.670 ⇒ 00:29:15.450 Michael Thorson: Are you… when you’re pulling this from the API, are you,
294 00:29:15.640 ⇒ 00:29:18.939 Michael Thorson: I’m just curious, cause… can you share your screen again?
295 00:29:18.940 ⇒ 00:29:20.290 Ashwini Sharma: Yeah, yeah, I can.
296 00:29:21.580 ⇒ 00:29:22.480 Michael Thorson: Okay.
297 00:29:27.180 ⇒ 00:29:31.610 Michael Thorson: This might be an over-filtering issue. It’s kind of my thought.
298 00:29:33.290 ⇒ 00:29:37.729 Ashwini Sharma: Yeah, so this is… this is what I’m looking at, right?
299 00:29:39.610 ⇒ 00:29:44.920 Ashwini Sharma: Geography level is this thing, geography is festival foods… are you able to see my screen?
300 00:29:46.550 ⇒ 00:29:47.690 Michael Thorson: Yeah, I can see your screen.
301 00:29:47.690 ⇒ 00:29:48.430 Ashwini Sharma: Yeah.
302 00:29:48.910 ⇒ 00:29:53.979 Ashwini Sharma: And… And category is this one, Purdue University.
303 00:29:53.980 ⇒ 00:29:54.360 Michael Thorson: Hmm.
304 00:29:54.360 ⇒ 00:30:01.410 Ashwini Sharma: TPL and subcategories, this thing, and I’m looking at Anything that’s more than… 3.30.
305 00:30:01.530 ⇒ 00:30:04.209 Ashwini Sharma: Since I pulled the data from 420,
306 00:30:04.340 ⇒ 00:30:06.859 Ashwini Sharma: So this, this is the boundary line at 4 weeks.
307 00:30:07.710 ⇒ 00:30:09.769 Ashwini Sharma: And this is what I get.
308 00:30:15.520 ⇒ 00:30:20.889 Ashwini Sharma: I think this, this comes to the same 200 and something that, that you got, right?
309 00:30:21.500 ⇒ 00:30:28.529 Michael Thorson: Would you… what would happen if we pulled all the… this is maybe a lot to look at really quick, but changed the reporting level…
310 00:30:29.350 ⇒ 00:30:35.839 Michael Thorson: Like, take the filter off reporting level and just show all three. You can just, like, comment that out for a second.
311 00:30:37.410 ⇒ 00:30:46.720 Michael Thorson: Because, do you understand that, like, these reporting levels should all roll up to each other? Like, the hierarchy is, you know, stepping down that list, so I’m wondering…
312 00:30:48.360 ⇒ 00:30:55.070 Michael Thorson: If we might be able to, like, aggregate at the lower level, and that’s… Gonna be actually more effective.
313 00:30:57.610 ⇒ 00:31:01.730 Ashwini Sharma: So, what you’ve done is rolled up by this date, right?
314 00:31:04.500 ⇒ 00:31:11.159 Michael Thorson: We could even, yeah, if you wanted to look at, like, one date, for example, we could also filter down to, like, just that 420 if we need to.
315 00:31:12.130 ⇒ 00:31:15.799 Michael Thorson: Just to make it a little bit, like, less confusing.
316 00:31:16.470 ⇒ 00:31:17.979 Michael Thorson: This is kind of a beast.
317 00:31:17.980 ⇒ 00:31:23.719 Ashwini Sharma: That would be just one week. Yeah, that would just be one week of data, right? But here it is right now.
318 00:31:23.720 ⇒ 00:31:24.750 Michael Thorson: Okay, okay.
319 00:31:26.390 ⇒ 00:31:29.839 Ashwini Sharma: We’ve got 16 records, brand and UPC.
320 00:31:30.050 ⇒ 00:31:36.329 Michael Thorson: Oh, yeah, okay, take the… also take the subcategory filter off.
321 00:31:39.710 ⇒ 00:31:42.290 Michael Thorson: Yeah, run that really quick.
322 00:31:48.150 ⇒ 00:31:48.545 Michael Thorson: Hmm…
323 00:31:54.490 ⇒ 00:31:55.480 Michael Thorson: Okay.
324 00:31:56.070 ⇒ 00:31:56.700 Michael Thorson: Hmm.
325 00:31:56.700 ⇒ 00:31:58.479 Ashwini Sharma: So now there’s Brand Cat 2.
326 00:31:59.690 ⇒ 00:32:00.540 Michael Thorson: Yeah.
327 00:32:01.530 ⇒ 00:32:19.769 Michael Thorson: And you could probably sort, reporting level, just sort that ascending, just for, like, a visual cue, because these should roll up to some degree, so it’s, like, UPC will roll up to a category… to, like, a subcategory, subcategories, like, all four of them will roll up to the brand.
328 00:32:20.120 ⇒ 00:32:25.849 Michael Thorson: okay, let’s just take a look at this. There’s multiple weeks…
329 00:32:32.190 ⇒ 00:32:37.510 Michael Thorson: So it should be all our categories, so, like, not just serial anymore.
330 00:32:37.720 ⇒ 00:32:40.129 Ashwini Sharma: Should I comment out of the category also?
331 00:32:40.880 ⇒ 00:32:42.850 Michael Thorson: Oh…
332 00:32:45.210 ⇒ 00:32:52.109 Michael Thorson: Well, like, I think this is what I’m curious… before we do that, it’s just, like, there’s some filtering
333 00:32:52.310 ⇒ 00:32:58.179 Michael Thorson: that will return a null subcategory, and that’s what I’m a little bit concerned of, is like…
334 00:32:59.830 ⇒ 00:33:02.880 Michael Thorson: Like, this is expected output, but…
335 00:33:04.470 ⇒ 00:33:06.879 Michael Thorson: I’m just making sure we’re, like, okay.
336 00:33:07.030 ⇒ 00:33:13.680 Michael Thorson: And then scroll over to the right a little bit, yeah.
337 00:33:15.010 ⇒ 00:33:21.380 Michael Thorson: Have we… have you tried doing the ACV… the average ACV at the UPC level, reporting level?
338 00:33:22.100 ⇒ 00:33:25.650 Ashwini Sharma: Average ECV at, UPC level.
339 00:33:26.360 ⇒ 00:33:30.499 Ashwini Sharma: Yeah, average kind of lines up pretty well.
340 00:33:31.020 ⇒ 00:33:37.290 Ashwini Sharma: The difference is quite small, but then, average is within 1 to 100, right? 0 to 100.
341 00:33:37.750 ⇒ 00:33:39.440 Ashwini Sharma: ACV,
342 00:33:40.000 ⇒ 00:33:47.489 Ashwini Sharma: And there is a variation of around 3%, right? That’s what I see. So this… this data, what you’re seeing over here, this is at the UPC level.
343 00:33:47.960 ⇒ 00:33:49.580 Michael Thorson: Oh, nice. Okay.
344 00:33:49.580 ⇒ 00:33:50.140 Ashwini Sharma: Right.
345 00:33:50.480 ⇒ 00:33:51.050 Ashwini Sharma: So…
346 00:33:51.050 ⇒ 00:33:51.490 Michael Thorson: So…
347 00:33:51.490 ⇒ 00:33:58.429 Ashwini Sharma: If we, if we take some record, like, where there’s a UPC involved, right, for example, over this one.
348 00:33:59.110 ⇒ 00:34:01.390 Ashwini Sharma: And, and compare the ACVs.
349 00:34:02.200 ⇒ 00:34:05.240 Ashwini Sharma: So we are something like 1.78 difference.
350 00:34:06.150 ⇒ 00:34:07.490 Ashwini Sharma: For this record.
351 00:34:12.679 ⇒ 00:34:19.149 Michael Thorson: Cool. Yeah, that makes… that’s not bad, but it’s like, it’s starting to break down, and I’m wondering… do you have…
352 00:34:20.109 ⇒ 00:34:29.139 Michael Thorson: Yeah, can you go back to that other… the other table we were just looking at? I think it was… yeah, this could be. And then scroll to the right. I wanted to see if the…
353 00:34:30.289 ⇒ 00:34:39.519 Michael Thorson: Keep scrolling… Yeah, I’m like, I really… I was expecting the market all-commodity volume to change here.
354 00:34:41.079 ⇒ 00:34:42.899 Michael Thorson: So maybe that’s…
355 00:34:44.659 ⇒ 00:34:47.359 Ashwini Sharma: Yeah, that’s… that’s a bit weird, right? It…
356 00:34:47.819 ⇒ 00:34:50.719 Ashwini Sharma: Should not be a constant number. Like, we’re.
357 00:34:50.719 ⇒ 00:34:51.039 Michael Thorson: I was in…
358 00:34:51.040 ⇒ 00:34:52.340 Ashwini Sharma: multiple products.
359 00:34:53.090 ⇒ 00:35:07.520 Michael Thorson: Yeah, and I think that’s where… my question to spins that we need to figure out is exactly that. It’s like, shouldn’t market all commodity volume change by subcategory, for example? Or do we need to, like, prepare a CTE that is…
360 00:35:07.870 ⇒ 00:35:15.090 Michael Thorson: a waiting term by subcategory, or, like, by… I think that would be our… Slice, right?
361 00:35:17.370 ⇒ 00:35:17.929 Ashwini Sharma: Yeah, this…
362 00:35:17.930 ⇒ 00:35:18.500 Michael Thorson: Yeah.
363 00:35:18.810 ⇒ 00:35:23.470 Ashwini Sharma: what does this mean? Market all commodity volume, right? This means that…
364 00:35:26.900 ⇒ 00:35:32.340 Ashwini Sharma: This is the total volume of this thing across… All the market?
365 00:35:33.550 ⇒ 00:35:35.689 Michael Thorson: Entire market, every brand.
366 00:35:41.520 ⇒ 00:35:43.110 Michael Thorson: So I’m just thinking…
367 00:35:48.310 ⇒ 00:35:50.530 Michael Thorson: We’re expected to change…
368 00:35:51.440 ⇒ 00:35:58.910 Michael Thorson: Week over week, though, to some degree, it’s… it’s kind of strange to me that it’s… the mark… the total market volume is not…
369 00:35:59.090 ⇒ 00:36:00.070 Michael Thorson: Shifting.
370 00:36:02.700 ⇒ 00:36:06.209 Ashwini Sharma: Yeah, it should change at least week on week. It can’t be the same, right?
371 00:36:06.890 ⇒ 00:36:07.560 Michael Thorson: Yeah.
372 00:36:08.270 ⇒ 00:36:11.580 Ashwini Sharma: It’s the same for, no, it should change here.
373 00:36:14.060 ⇒ 00:36:24.659 Michael Thorson: Yeah, and I’m… or, like, just bear with me for a second, if we need to combine the market all-commodity volume with the category we’re looking at, and almost, like, develop our own CTE, you know?
374 00:36:25.500 ⇒ 00:36:37.529 Michael Thorson: That’s what’s… that’s what’s really not clear in the SPIN’s calcs, is, like, what is the weighting term when we’re looking at the UBC level? Or, like, when we’re looking at the, subcategory level.
375 00:36:38.340 ⇒ 00:36:43.860 Michael Thorson: I… yeah, I think… because UPC is coming out.
376 00:36:44.660 ⇒ 00:36:50.500 Michael Thorson: The aggregation you’re saying was coming out pretty correct for 4-week aggregation?
377 00:36:50.730 ⇒ 00:36:54.530 Ashwini Sharma: Right, when there are multiple UPCs within a subcategory.
378 00:36:54.760 ⇒ 00:37:05.629 Ashwini Sharma: In that case, we can, you know, sum it up, right? For example, like, if it’s a hypothetical, but if these were two UPCs under a subcategory.
379 00:37:06.100 ⇒ 00:37:09.800 Ashwini Sharma: the TDP would have been 41.3 plus 22.4.
380 00:37:10.350 ⇒ 00:37:11.180 Michael Thorson: Hmm.
381 00:37:13.620 ⇒ 00:37:19.939 Ashwini Sharma: And then… and then, when you compare it across 4 weeks, for some of the entities, it is the maximum of
382 00:37:20.630 ⇒ 00:37:23.409 Ashwini Sharma: the TDP value across these 4 weeks.
383 00:37:23.780 ⇒ 00:37:25.480 Ashwini Sharma: But for some others, it’s not.
384 00:37:25.970 ⇒ 00:37:28.050 Ashwini Sharma: And that’s where the confusion arises.
385 00:37:29.340 ⇒ 00:37:30.410 Michael Thorson: Yeah.
386 00:37:32.160 ⇒ 00:37:34.490 Michael Thorson: Do you think there would be, like.
387 00:37:34.750 ⇒ 00:37:42.059 Michael Thorson: Like, do you think that’s because it’s coming from, like, some sort of weighted average, or do you think something else is going on?
388 00:37:43.220 ⇒ 00:37:51.110 Ashwini Sharma: It must be some kind of a weighted average, but I just do not know what number is used to calculate that weighted average, right? Because the market…
389 00:37:51.410 ⇒ 00:37:57.659 Ashwini Sharma: This thing was, this is a constant value. This won’t change if I use it.
390 00:37:58.100 ⇒ 00:38:04.890 Ashwini Sharma: And then I tried using this dollars value, so basically multiplied this thing with the dollars value.
391 00:38:05.170 ⇒ 00:38:13.500 Ashwini Sharma: And then added it up across all the weeks, and then divided by the total dollar sales amount, right? Sum of dollar sales amount. That… that didn’t return it either.
392 00:38:14.740 ⇒ 00:38:15.440 Ashwini Sharma: So…
393 00:38:15.440 ⇒ 00:38:15.990 Michael Thorson: Yeah.
394 00:38:16.940 ⇒ 00:38:23.899 Ashwini Sharma: I honestly don’t know at this point, like, what exactly is going on in the backend that derives those numbers.
395 00:38:25.320 ⇒ 00:38:36.679 Michael Thorson: Yeah, well, the good news is we don’t quite know, and we kind of expected this, to cause some issues, so I think it’s just formulating the, like, the, like, right question for the Spins team.
396 00:38:36.680 ⇒ 00:38:37.320 Ashwini Sharma: It’s… yeah.
397 00:38:37.320 ⇒ 00:38:44.199 Michael Thorson: How would you… yeah, what’s the question that we want to ask, then? Like, what are we trying to… is this a…
398 00:38:45.160 ⇒ 00:38:48.410 Michael Thorson: aggregation from 1 week to 4 weeks, and… Yes.
399 00:38:48.780 ⇒ 00:38:49.670 Michael Thorson: Okay.
400 00:38:50.870 ⇒ 00:38:52.739 Michael Thorson: Yeah, can you,
401 00:38:52.840 ⇒ 00:38:59.969 Michael Thorson: Can you share, like, from these tables we’re looking at, what do you think the table would… that would most exemplify the breakdown?
402 00:39:00.680 ⇒ 00:39:06.000 Michael Thorson: I think maybe we send that table as kind of like a preview to them, and we set up a call or something.
403 00:39:06.850 ⇒ 00:39:16.120 Ashwini Sharma: Should I put it in an Excel sheet and then send it, or, like, what do you think would be the best approach, right? I mean, like, giving access to this table
404 00:39:16.610 ⇒ 00:39:19.800 Ashwini Sharma: I mean, that… Might not be a really good idea, right?
405 00:39:19.800 ⇒ 00:39:32.369 Michael Thorson: Yeah, if you could just honestly toss this in an Excel sheet or, like, a G sheet, that should be fine. Like, whatever one we’ve been working in for the QA, I think is a perfect landing point, so the one that you and Utam have…
406 00:39:32.520 ⇒ 00:39:33.770 Michael Thorson: Had together.
407 00:39:34.040 ⇒ 00:39:36.759 Ashwini Sharma: Sure, yeah, yeah, I can do that. I can do that, yeah.
408 00:39:37.680 ⇒ 00:39:50.190 Michael Thorson: Yeah, yeah, if you can just throw that in there, and then if you could, like, just, yeah, comment, or, like, tag me, or, like, highlight a cell, or something that, like, is like, hey, this is where the exact breakdown occurs. I think that’s kind of what we need to, like, drive the conversation forward with.
409 00:39:51.190 ⇒ 00:39:52.200 Michael Thorson: Spins.
410 00:39:52.420 ⇒ 00:39:52.940 Ashwini Sharma: Yep.
411 00:39:53.860 ⇒ 00:39:54.440 Michael Thorson: Cool.
412 00:39:54.700 ⇒ 00:39:56.300 Michael Thorson: Sure, I’ll do that.
413 00:39:56.300 ⇒ 00:39:57.080 Ashwini Sharma: Yeah.
414 00:39:57.080 ⇒ 00:39:57.610 Michael Thorson: Great.
415 00:39:58.350 ⇒ 00:39:58.749 Ashwini Sharma: Yeah, if you can do that.
416 00:39:58.750 ⇒ 00:40:08.579 Michael Thorson: I’ll follow up with Heather, our, like, kind of spins master internally, and if she has any kind of, like, premonitions of the best way to calc.
417 00:40:08.710 ⇒ 00:40:10.090 Ashwini Sharma: Average ACV?
418 00:40:10.090 ⇒ 00:40:14.390 Michael Thorson: I’ll let you know. Otherwise, I think we should set up a Spence call, and we can all talk it out.
419 00:40:14.390 ⇒ 00:40:15.120 Ashwini Sharma: Sure.
420 00:40:15.310 ⇒ 00:40:18.510 Ashwini Sharma: I had one more quick question regarding
421 00:40:19.540 ⇒ 00:40:24.889 Ashwini Sharma: We’re talking about backfilling the data, because right now we just had for 420 and…
422 00:40:25.090 ⇒ 00:40:32.830 Ashwini Sharma: 320, sorry, April and March 2025, right? Yeah. So, when we are going for backfilling.
423 00:40:33.200 ⇒ 00:40:37.470 Ashwini Sharma: Do we want to retain those filters only for Magic Spoon, or is it going to be…
424 00:40:37.760 ⇒ 00:40:39.490 Ashwini Sharma: Across all the brands.
425 00:40:43.810 ⇒ 00:40:46.100 Michael Thorson: It’s a good question.
426 00:40:46.340 ⇒ 00:40:51.100 Michael Thorson: I think we just need to be strategic, because we might hit some rate limits. Yeah.
427 00:40:51.650 ⇒ 00:40:57.100 Michael Thorson: I think we would like to expand to all the brands. It’ll take… we may want to, like…
428 00:40:57.910 ⇒ 00:41:06.799 Michael Thorson: Yeah, it may take some time to, like, run the backfill, but yeah, we would want to do all brands, UPCs, only for, like, specific brands that we discussed.
429 00:41:06.930 ⇒ 00:41:12.670 Michael Thorson: And sorry, not all the brands, but the brands that we have outlined in the,
430 00:41:14.050 ⇒ 00:41:18.660 Michael Thorson: Excuse me, in the… shared. Okay, references.
431 00:41:18.660 ⇒ 00:41:20.619 Ashwini Sharma: Yeah, yeah, right, right, okay.
432 00:41:21.660 ⇒ 00:41:22.470 Michael Thorson: Yeah.
433 00:41:22.840 ⇒ 00:41:23.639 Ashwini Sharma: Cool.
434 00:41:23.640 ⇒ 00:41:24.250 Michael Thorson: bad.
435 00:41:24.490 ⇒ 00:41:25.090 Ashwini Sharma: Yeah.
436 00:41:27.490 ⇒ 00:41:30.850 Michael Thorson: Cool. Let me share my screen really quick, too. Does that make… does that make sense?
437 00:41:31.020 ⇒ 00:41:33.369 Ashwini Sharma: Yeah, yeah, yeah, it does, I think,
438 00:41:37.020 ⇒ 00:41:40.600 Ashwini Sharma: And, yeah, one more thing that I noticed is…
439 00:41:41.490 ⇒ 00:41:46.939 Ashwini Sharma: Somehow, though, those geographies change, right? I don’t know what’s going on in the back end, but…
440 00:41:47.290 ⇒ 00:41:53.360 Ashwini Sharma: I had a list of geographies that I got from this sheet, right? And then I pasted it in
441 00:41:53.690 ⇒ 00:41:54.939 Ashwini Sharma: in my filter.
442 00:41:55.710 ⇒ 00:42:04.579 Ashwini Sharma: And, like, yesterday, or maybe over the weekend sometime, I noticed that, That list was incomplete.
443 00:42:06.090 ⇒ 00:42:06.500 Michael Thorson: Yes.
444 00:42:06.810 ⇒ 00:42:15.390 Ashwini Sharma: think that… the number of geographies over which that client ID is accessed, That might change.
445 00:42:17.700 ⇒ 00:42:24.590 Michael Thorson: Interesting. And are you pulling the geography list from their… the query?
446 00:42:24.590 ⇒ 00:42:30.990 Ashwini Sharma: Yeah, and then I switched over to the query, right? Instead of static input for geographies, I made it dynamic.
447 00:42:31.150 ⇒ 00:42:37.019 Ashwini Sharma: But then, that’s kind of stuck in my mind, that what happens if it changes in future?
448 00:42:37.980 ⇒ 00:42:38.850 Michael Thorson: Yeah.
449 00:42:40.220 ⇒ 00:42:45.920 Ashwini Sharma: Because then we’ll be getting lesser data, right? I mean, some geographies will be missed out, and…
450 00:42:46.610 ⇒ 00:42:48.570 Ashwini Sharma: The question is, why is it changing?
451 00:42:51.570 ⇒ 00:42:58.359 Michael Thorson: Yeah, what… and what specifically changed? Like, what versus what? When did you notice that discrepancy emerge?
452 00:42:58.360 ⇒ 00:43:02.920 Ashwini Sharma: Yeah, the initial set of geographies I had was around 1400 and something.
453 00:43:03.280 ⇒ 00:43:10.460 Ashwini Sharma: And, when I noticed something was missing, one particular geography was missing, and I re-pulled it again.
454 00:43:10.660 ⇒ 00:43:14.040 Ashwini Sharma: That time, it was 2 or 3 more geographies were available.
455 00:43:17.800 ⇒ 00:43:21.980 Michael Thorson: Yeah, that… I think that makes sense.
456 00:43:23.930 ⇒ 00:43:38.639 Michael Thorson: because, like, just, as new store… like, as Spins collects new information, I’d imagine, like, if a new store or a new brand… yeah, I guess, because we’re talking about geography, if a new store is in their dataset.
457 00:43:38.780 ⇒ 00:43:47.199 Michael Thorson: then, yeah, the geography filter will expand over time. But I think, like, the best practice would be, to your point, like.
458 00:43:47.660 ⇒ 00:43:49.869 Michael Thorson: The geography would be…
459 00:43:50.180 ⇒ 00:43:51.800 Ashwini Sharma: Yeah, for example, this list.
460 00:43:51.800 ⇒ 00:43:52.580 Michael Thorson: geography.
461 00:43:52.810 ⇒ 00:44:00.310 Ashwini Sharma: Yeah, this list, right? This list, this is the geographies list, right? Oh, this does not even have a full list, yeah, yeah, yeah, yeah.
462 00:44:00.310 ⇒ 00:44:17.420 Michael Thorson: This, yeah, and sorry, like, this is what I was gonna say, is, like, this is, like, all geographies selling cereal, I think it is. So I pulled this list from platforms, and all I did here was I was basically, like, I pulled a while ago.
463 00:44:17.750 ⇒ 00:44:27.110 Michael Thorson: subcategory equals SS Cereal, and this is all geographies selling cereal, which, like, might not be a representative list, you know?
464 00:44:27.110 ⇒ 00:44:27.560 Ashwini Sharma: I thought.
465 00:44:27.560 ⇒ 00:44:36.509 Michael Thorson: I think, realistically, it’s all geographies, and like, that’s what I was a little bit confused with the API, because it’s like, you have to force a geography
466 00:44:36.880 ⇒ 00:44:38.300 Michael Thorson: filter, right?
467 00:44:38.300 ⇒ 00:44:45.350 Ashwini Sharma: Right. And what happens if a store closes down, right? Does this geography go out, or does it remain?
468 00:44:46.020 ⇒ 00:44:57.680 Michael Thorson: It should remain in the geographies query, I’m thinking. I’m honestly not sure, but I’m just saying, like, I think the important thing is we’re capturing all available geographies that are sold in.
469 00:44:58.400 ⇒ 00:45:02.770 Michael Thorson: And we specifically really want, like, these…
470 00:45:03.050 ⇒ 00:45:08.360 Michael Thorson: levels. The key account and RMA are, like, the most interesting to us, I think. It’s like…
471 00:45:09.540 ⇒ 00:45:11.490 Michael Thorson: And then these appeared.
472 00:45:17.100 ⇒ 00:45:21.000 Michael Thorson: Is… Are you following me, or is this confusing?
473 00:45:21.230 ⇒ 00:45:33.359 Ashwini Sharma: Yeah, yeah, no, this is okay. Like, I’m pulling all the geographies, right? So, it should not… geography level is not even a filter, right? So, what I’m doing in the query is, you know.
474 00:45:33.690 ⇒ 00:45:37.569 Ashwini Sharma: And then the payload is including all the geographies, right?
475 00:45:38.050 ⇒ 00:45:41.189 Ashwini Sharma: And in terms of brand, it is only Magic Spoon.
476 00:45:41.610 ⇒ 00:45:44.880 Ashwini Sharma: And there is a category filter.
477 00:45:45.000 ⇒ 00:45:50.049 Ashwini Sharma: Oh, there is no category filter right now. Let me quickly look at the payload.
478 00:45:55.880 ⇒ 00:46:01.080 Michael Thorson: Yeah. And I have… I have at least the starter, like, that’s kind of where I was organizing this…
479 00:46:01.830 ⇒ 00:46:10.460 Michael Thorson: trying to mirror what’s been available on the platform. Like, Magic Spoon pays for these… like, we have access to these subcategories.
480 00:46:11.180 ⇒ 00:46:16.939 Michael Thorson: But this should be able to, like, guide your… Final filters, I guess.
481 00:46:17.270 ⇒ 00:46:17.870 Ashwini Sharma: Okay.
482 00:46:18.330 ⇒ 00:46:27.250 Michael Thorson: Because, to your point, like, as we start expanding to, like, all brands, it’s like, some brands might be selling more categories than this, which can really throw off the data.
483 00:46:29.430 ⇒ 00:46:30.599 Ashwini Sharma: Got it, okay.
484 00:46:37.130 ⇒ 00:46:44.960 Michael Thorson: Any more questions? Yeah, please hold off on the backfill for now. I don’t think we have a pressing…
485 00:46:45.100 ⇒ 00:46:47.259 Michael Thorson: Yeah, I don’t think we have a pressing…
486 00:46:47.510 ⇒ 00:46:55.300 Michael Thorson: need, but I would like to, like, before you run that, just to avoid any churn, just, like, we’re really clear on maybe what filters that you’re gonna send in.
487 00:46:55.470 ⇒ 00:46:56.690 Ashwini Sharma: Sure, yeah.
488 00:46:56.770 ⇒ 00:47:01.530 Michael Thorson: Yeah, so we can, like, run this just once and not have to think about it again.
489 00:47:01.530 ⇒ 00:47:03.519 Ashwini Sharma: Okay, cool, yeah, yeah, yeah, yeah.
490 00:47:04.770 ⇒ 00:47:05.460 Ashwini Sharma: Alright.
491 00:47:06.380 ⇒ 00:47:19.160 Michael Thorson: But yeah, if you can just toss that table in, we… like, I can reach out, take a follow-up to, like, reach out to Spins and get that meeting scheduled, or feel free to reach out to Ugo, doesn’t really matter. Sure, okay, I’ll do that, yeah.
492 00:47:19.750 ⇒ 00:47:20.690 Michael Thorson: Alright, cool.
493 00:47:20.690 ⇒ 00:47:21.220 Ashwini Sharma: Cool.
494 00:47:22.240 ⇒ 00:47:23.890 Ashwini Sharma: Alright, yeah, thanks.
495 00:47:23.890 ⇒ 00:47:24.310 Michael Thorson: photography.
496 00:47:24.310 ⇒ 00:47:24.649 Ashwini Sharma: Thank you, man.
497 00:47:24.650 ⇒ 00:47:26.220 Michael Thorson: Sorry, we’re hitting the blocker here.
498 00:47:26.220 ⇒ 00:47:27.050 Ashwini Sharma: Bye.