WEBVTT 00:00:00.000 --> 00:00:04.219 How does a concert in a tiny Texas town of just 00:00:04.219 --> 00:00:08.199 over 5 ,000 people generate roughly $100 ,000 00:00:08.199 --> 00:00:11.060 more in revenue than a massive arena show in 00:00:11.060 --> 00:00:13.400 Vancouver? Right, with more than double the audience. 00:00:13.560 --> 00:00:16.710 Exactly. Or... Better yet, how does a slightly 00:00:16.710 --> 00:00:19.570 smaller crowd in Dallas, Texas out earn a packed 00:00:19.570 --> 00:00:22.210 house at Madison Square Garden? I mean, the world's 00:00:22.210 --> 00:00:25.089 most famous arena. It sounds impossible on paper. 00:00:25.250 --> 00:00:28.050 It really does. But today, we're opening up a 00:00:28.050 --> 00:00:30.649 data set sent in by you, the listener, and we're 00:00:30.649 --> 00:00:33.109 going to answer exactly those questions. So welcome 00:00:33.109 --> 00:00:35.200 to the Deep Dive. Glad to be here. Our source 00:00:35.200 --> 00:00:37.560 material today is the Wikipedia data for Carol 00:00:37.560 --> 00:00:42.179 G's 2022 Strip Love Tour. And our mission here 00:00:42.179 --> 00:00:44.159 is pretty simple. We want to reverse engineer 00:00:44.159 --> 00:00:47.060 the anatomy of a blockbuster concert run. It's 00:00:47.060 --> 00:00:49.479 a fascinating data set. Oh, totally. We're going 00:00:49.479 --> 00:00:51.439 to look at the math, the map, and the music to 00:00:51.439 --> 00:00:53.979 understand how an artist transforms a 33 -day 00:00:53.979 --> 00:00:58.619 tour into a flawless, sold -out, $72 .2 million 00:00:58.619 --> 00:01:01.460 blueprint for the music industry. And, you know, 00:01:01.920 --> 00:01:04.620 To really grasp the magnitude of what she accomplished 00:01:04.620 --> 00:01:06.920 here, we have to establish exactly where this 00:01:06.920 --> 00:01:09.560 tour sits in her overall career timeline. Right. 00:01:09.680 --> 00:01:11.920 Context is everything. Exactly. If we look at 00:01:11.920 --> 00:01:14.180 the chronology and the source material, this 00:01:14.180 --> 00:01:17.060 2022 tour, which, by the way, was sponsored by 00:01:17.060 --> 00:01:20.140 AEG Live, served as a very specific high -stace 00:01:20.140 --> 00:01:23.299 bridge. A bridge between her other tours, right? 00:01:23.299 --> 00:01:25.540 Yeah. It happened right after her Bichota tour, 00:01:25.620 --> 00:01:28.400 which wrapped up earlier in 2022, and then directly 00:01:28.400 --> 00:01:31.620 before the massive Manana Sarabunido tour that 00:01:31.620 --> 00:01:34.480 kicked off in 2023. Wow. So back to back to back. 00:01:34.640 --> 00:01:36.519 Pretty much. It essentially functioned as a victory 00:01:36.519 --> 00:01:41.000 lap for her 2021 album, KG051stein. It was a 00:01:41.000 --> 00:01:43.359 moment to cement her status before moving on 00:01:43.359 --> 00:01:47.099 to the next era. So whether you are a diehard 00:01:47.099 --> 00:01:50.099 live music fanatic who lives for the front row 00:01:50.099 --> 00:01:52.859 or you're just fascinated by incredible business 00:01:52.879 --> 00:01:55.459 stick with us. Because understanding the mechanics 00:01:55.459 --> 00:01:57.159 of a tour like this one, where nothing is left 00:01:57.159 --> 00:01:59.180 to chance, it's a total revelation. It really 00:01:59.180 --> 00:02:02.159 is a master class. Okay, let's unpack this. Before 00:02:02.159 --> 00:02:04.579 we even look at what she played on stage, we 00:02:04.579 --> 00:02:06.700 need to look at the staggering scope of where 00:02:06.700 --> 00:02:09.400 and when she played. Because the timeline here 00:02:09.400 --> 00:02:12.560 is, honestly, it's brutal. Oh, absolutely grueling. 00:02:12.699 --> 00:02:15.500 The tour kicks off on September 6th, 3 -3 -22 00:02:15.500 --> 00:02:18.400 in Rosemont, Illinois. And it wraps up on November 00:02:18.400 --> 00:02:21.879 2nd, 2022 in Boston, Massachusetts. Which is, 00:02:21.879 --> 00:02:24.199 what, a window of less than 60 days. Exactly. 00:02:24.379 --> 00:02:27.740 We are talking about 33 total shows across the 00:02:27.740 --> 00:02:30.939 US and Canada in under two months. And the total 00:02:30.939 --> 00:02:35.379 attendance across the run, 412 ,840 people. That's 00:02:35.379 --> 00:02:38.560 a massive footprint. Huge. But here is the critical 00:02:38.560 --> 00:02:41.520 data point that just jumped off the page at me. 00:02:41.919 --> 00:02:44.419 The attendance isn't just a big impressive number. 00:02:44.840 --> 00:02:47.020 Next to every single date on this Wikipedia list, 00:02:47.159 --> 00:02:49.939 the ticket sold number exactly matches the available 00:02:49.939 --> 00:02:52.449 tickets number. Right. Every single available 00:02:52.449 --> 00:02:54.409 ticket across the entire tour was purchased. 00:02:54.729 --> 00:02:56.990 It is a 100 percent sellout rate across the board. 00:02:57.129 --> 00:02:58.710 Which is just an operational triumph. I mean, 00:02:58.750 --> 00:03:01.129 to put that in perspective, seeing a 100 percent 00:03:01.129 --> 00:03:03.050 sellout rate across three dozen dates isn't just 00:03:03.050 --> 00:03:05.169 about popularity. What else is it then? It's 00:03:05.169 --> 00:03:07.530 about algorithmic precision from the promoters. 00:03:07.530 --> 00:03:11.530 Right. AEG live perfectly matching venue capacities 00:03:11.530 --> 00:03:13.750 to regional demand. OK, wait, let me challenge 00:03:13.750 --> 00:03:16.409 that. Because I hear 100 percent sellout rate 00:03:16.409 --> 00:03:20.409 and. little voice in my head gets, you know, 00:03:20.689 --> 00:03:22.449 skeptical. Sure, that's fair. Like, if every 00:03:22.449 --> 00:03:25.770 single ticket sold out across 33 dates, couldn't 00:03:25.770 --> 00:03:27.830 a cynic argue that they actually just priced 00:03:27.830 --> 00:03:30.849 the tickets too low? Or, I don't know, that scalper 00:03:30.849 --> 00:03:33.150 bots just bought up large chunks of the inventory. 00:03:33.270 --> 00:03:35.189 That's the immediate assumption a lot of people 00:03:35.189 --> 00:03:38.090 make. Right. So how do we know from just looking 00:03:38.090 --> 00:03:40.810 at a Wikipedia data table that this is pure, 00:03:41.150 --> 00:03:43.729 authentic fan demand? That is a completely fair 00:03:43.729 --> 00:03:46.129 question. And the answer is actually hidden in 00:03:46.129 --> 00:03:49.050 the revenue variance, which we will dig into 00:03:49.050 --> 00:03:51.509 later. OK, pizza for now. Yeah. But to briefly 00:03:51.509 --> 00:03:53.909 answer your question now, if the tickets were 00:03:53.909 --> 00:03:57.330 uniformly underpriced or swept by bots, you would 00:03:57.330 --> 00:04:00.020 see a relatively flat average ticket price across 00:04:00.020 --> 00:04:02.120 the board. Oh, because the bots don't care about 00:04:02.120 --> 00:04:05.159 the local market nuances. Exactly. The fact that 00:04:05.159 --> 00:04:07.740 the revenue wildly fluctuates from city to city, 00:04:07.879 --> 00:04:10.580 even in cities with similar venue sizes, tells 00:04:10.580 --> 00:04:13.849 us the pricing was highly elastic. I see. The 00:04:13.849 --> 00:04:16.069 promoters captured the true ceiling of what real 00:04:16.069 --> 00:04:18.269 fans in each specific local market were willing 00:04:18.269 --> 00:04:21.230 to pay. But to understand how they pulled that 00:04:21.230 --> 00:04:24.110 off, you really have to look at the sheer logistical 00:04:24.110 --> 00:04:26.569 endurance required. Look at the calendar in late 00:04:26.569 --> 00:04:28.790 September. OK, I have it right here. Let me just 00:04:28.790 --> 00:04:30.829 read this stretch aloud so everyone listening 00:04:30.829 --> 00:04:34.230 can feel the exhaustion. Go for it. She plays 00:04:34.230 --> 00:04:38.350 Miami on September 22nd and 23rd. Orlando on 00:04:38.350 --> 00:04:42.889 the 24th, Tampa on the 26th, Atlanta on the 27th, 00:04:43.129 --> 00:04:45.490 Houston on the 29th, and Hidalgo on the 30th. 00:04:45.550 --> 00:04:48.430 That is a relentless pace. Right. Performing 00:04:48.430 --> 00:04:50.769 a highly choreographed, emotionally draining 00:04:50.769 --> 00:04:53.649 arena show almost every single night, while, 00:04:53.670 --> 00:04:56.209 by the way, moving an entire city's worth of 00:04:56.209 --> 00:04:57.970 production gear down the highway. Yeah, routing 00:04:57.970 --> 00:05:00.930 a 33 -date tour in 60 days isn't just drawing 00:05:00.930 --> 00:05:03.509 lines on a map. It's like solving a massive 3D 00:05:03.509 --> 00:05:05.350 Tetris puzzle. That's a great way to put it. 00:05:05.370 --> 00:05:07.389 Thanks. I mean, the blocks are... the convoys 00:05:07.389 --> 00:05:10.410 of 18 -wheelers, local union labor rules, and 00:05:10.410 --> 00:05:12.910 arena availabilities. If one block falls out 00:05:12.910 --> 00:05:15.430 of place, say a truck breaks down between Tampa 00:05:15.430 --> 00:05:18.009 and Atlanta, the entire multi -million dollar 00:05:18.009 --> 00:05:21.060 structure basically collapses. Precisely. And 00:05:21.060 --> 00:05:23.779 that physical toll on the artist is massive. 00:05:24.279 --> 00:05:27.120 To survive that brutal schedule without burning 00:05:27.120 --> 00:05:30.180 her voice out or, frankly, physically collapsing, 00:05:30.699 --> 00:05:33.060 the show itself cannot just rely on spontaneous 00:05:33.060 --> 00:05:35.220 energy. So it has to be structured carefully. 00:05:35.579 --> 00:05:38.259 Exactly. It has to be scientifically paced. And 00:05:38.259 --> 00:05:40.420 this is where the logistics of the show's structure 00:05:40.420 --> 00:05:43.040 become vital. For instance, the source notes 00:05:43.040 --> 00:05:45.120 the presence of a supporting act, Aguadillo, 00:05:45.180 --> 00:05:47.980 for the entire run. You know, I always just assume 00:05:47.980 --> 00:05:50.560 an opening act is the to get some exposure or 00:05:50.560 --> 00:05:52.699 just to, like, warm up the crowd a bit? Sure. 00:05:52.800 --> 00:05:55.060 That's part of it. But in a schedule this tight, 00:05:55.220 --> 00:05:57.560 your supporting act is a crucial logistical tool. 00:05:58.199 --> 00:06:00.459 They aren't just there to play music. They manage 00:06:00.459 --> 00:06:03.379 the room's energy. How so? They act as a buffer. 00:06:03.519 --> 00:06:05.839 They set the baseline tone for the evening. And 00:06:05.839 --> 00:06:08.459 most importantly, they buy the production manager 00:06:08.459 --> 00:06:11.079 and the main artists the precise amount of time 00:06:11.079 --> 00:06:14.220 needed to prepare for a flawlessly timed entrance. 00:06:14.420 --> 00:06:16.579 Oh, wow. I never thought about it like that. 00:06:16.759 --> 00:06:19.100 Yeah, when you are doing 33 shows in 60 days, 00:06:19.240 --> 00:06:21.439 you rely heavily on that opener to ensure the 00:06:21.439 --> 00:06:23.060 crowd is at the exact right temperature when 00:06:23.060 --> 00:06:25.139 you step onto the stage. That makes a lot of 00:06:25.139 --> 00:06:28.639 sense. But obviously, logistics and a good opener 00:06:28.639 --> 00:06:31.620 alone do not sell 400 ,000 tickets. No, definitely 00:06:31.620 --> 00:06:34.060 not. The product itself, the actual performance 00:06:34.060 --> 00:06:37.300 she's putting on, has to deliver. And doing 33 00:06:37.300 --> 00:06:40.079 shows in 60 days means her set list has to be 00:06:40.079 --> 00:06:42.560 engineered with built -in recovery moments. Which 00:06:42.560 --> 00:06:45.259 leads us perfectly into examining the architecture 00:06:45.259 --> 00:06:48.040 of the set list. The Wikipedia article provides 00:06:48.040 --> 00:06:50.040 a representative set list from her September 00:06:50.040 --> 00:06:52.579 20th show at the Spectrum Center in Charlotte, 00:06:52.579 --> 00:06:56.639 and it reveals a, well, highly deliberate structure. 00:06:56.839 --> 00:06:58.439 Here's where it gets really interesting. I was 00:06:58.439 --> 00:07:00.720 looking at the set list, and it isn't just a 00:07:00.720 --> 00:07:03.160 random shuffle of her Spotify top 10. Not at 00:07:03.160 --> 00:07:06.160 all. It is structurally divided into three distinct 00:07:06.160 --> 00:07:08.959 acts, followed by an encore. Act one, act two, 00:07:09.019 --> 00:07:11.689 act three. It's formatted exactly like a three 00:07:11.689 --> 00:07:13.790 -act movie script. It is entirely theatrical. 00:07:14.029 --> 00:07:16.050 It's designed to take the audience on a specific 00:07:16.050 --> 00:07:18.889 narrative and emotional arc, while simultaneously 00:07:18.889 --> 00:07:21.269 managing the performer's stamina. Let's walk 00:07:21.269 --> 00:07:24.209 through it. So Act One opens the show with Catubla, 00:07:24.449 --> 00:07:27.410 features a remix of poblado, fricky, pineapple, 00:07:27.910 --> 00:07:30.310 and ends with oormi llama. Right, setting the 00:07:30.310 --> 00:07:32.610 stage. Yeah, it feels very much like an introduction, 00:07:32.910 --> 00:07:35.300 setting the baseline energy for the room. But 00:07:35.300 --> 00:07:37.639 then we hit Act Two, and if you are listening 00:07:37.639 --> 00:07:39.959 to this and you know her catalog, Act Two looks 00:07:39.959 --> 00:07:42.180 like an absolute gauntlet of heavy hitters. That's 00:07:42.180 --> 00:07:44.399 heavy artillery for sure. We've got Bichota, 00:07:44.579 --> 00:07:47.379 El Machinon, Leyendas, Ay Dios Mio, Sejo Dio, 00:07:47.459 --> 00:07:50.259 Do DVD, Ay Ella, and it closes with one of her 00:07:50.259 --> 00:07:53.360 biggest hits, Provenza. Right. Act Two is the 00:07:53.360 --> 00:07:55.620 peak physical exertion of the show. It is the 00:07:55.620 --> 00:07:58.000 high -energy core. But then Act Three happens, 00:07:58.300 --> 00:08:02.480 and the shift is, like, dramatic. We get... Ocean, 00:08:02.920 --> 00:08:07.180 El Barco, Tusa, 200 Copas, and Mommy? To me, 00:08:07.319 --> 00:08:09.319 Looking at the tracks, this seems fundamentally 00:08:09.319 --> 00:08:12.000 different from the club bangers in Act 2. It 00:08:12.000 --> 00:08:14.439 is. Act 3 is where the pacing we talked about 00:08:14.439 --> 00:08:16.680 comes into play. She shifts from high tempo, 00:08:16.879 --> 00:08:19.779 heavy production dance tracks to songs like Ocean 00:08:19.779 --> 00:08:22.240 and 200 Copas. Which are totally different vibes. 00:08:22.500 --> 00:08:24.920 Exactly. These are deeply emotional, often more 00:08:24.920 --> 00:08:27.259 acoustic or anthemic songs. If you're looking 00:08:27.259 --> 00:08:29.420 at the show as a mechanism, Act 3 is designed 00:08:29.420 --> 00:08:31.639 to bring the audience's heart rate down while 00:08:31.639 --> 00:08:33.820 simultaneously raising the emotional stakes. 00:08:33.980 --> 00:08:37.039 Okay, I see. It shifts the room from a massive 00:08:37.039 --> 00:08:40.899 dance party into a communal, intimate sing -along. 00:08:41.039 --> 00:08:43.399 It's the emotional climax. And it probably gives 00:08:43.399 --> 00:08:45.220 her a slight physical breather before the end 00:08:45.220 --> 00:08:47.340 of the night, too. Oh, absolutely. But then we 00:08:47.340 --> 00:08:50.980 reach the encore. And this is the part of the 00:08:50.980 --> 00:08:54.000 data that genuinely confused me. The sole song 00:08:54.000 --> 00:08:56.820 in the encore is Provenza, but it's the remix. 00:08:57.220 --> 00:08:58.860 Right. I see the contradiction you're pointing 00:08:58.860 --> 00:09:00.879 out. She already performed the original version 00:09:00.879 --> 00:09:04.080 of Provenza to close out act two. Why end the 00:09:04.080 --> 00:09:06.320 entire night with a remix of a song you just 00:09:06.320 --> 00:09:08.960 played an hour ago? It seems redundant at first 00:09:08.960 --> 00:09:11.399 glance. Right. I mean, looking at her discography 00:09:11.399 --> 00:09:13.779 and the source, she clearly has plenty of other 00:09:13.779 --> 00:09:16.580 songs she could have chosen. Is repeating a track 00:09:16.580 --> 00:09:19.139 a sign of a limited catalog for an arena show? 00:09:19.370 --> 00:09:21.769 The Wikipedia article doesn't explicitly tell 00:09:21.769 --> 00:09:24.169 us her psychological motive for ending on a remix, 00:09:24.370 --> 00:09:26.509 but if we look at the data shifting from the 00:09:26.509 --> 00:09:30.250 emotional ballads of Act 3 to a high BPM remix, 00:09:30.690 --> 00:09:33.309 we can deduce the strategy. Okay, let's hear 00:09:33.309 --> 00:09:35.990 it. It's a highly deliberate psychological maneuver. 00:09:36.210 --> 00:09:38.169 How so? Walk me through the psychology of that. 00:09:38.350 --> 00:09:40.309 Think about the emotional journey you just described. 00:09:40.929 --> 00:09:43.940 By the end of Act 3, The audience has been through 00:09:43.940 --> 00:09:47.820 this entire cathartic communal experience. Yeah, 00:09:47.820 --> 00:09:50.100 they've been singing, maybe crying a bit. Exactly. 00:09:50.159 --> 00:09:52.059 If she just ended the show right there, people 00:09:52.059 --> 00:09:54.840 would leave feeling deeply moved, but maybe a 00:09:54.840 --> 00:09:58.059 little drained. By deploying a high tempo dance 00:09:58.059 --> 00:10:01.379 remix of Provenza as the encore, she completely 00:10:01.379 --> 00:10:03.980 shifts the context. OK, I'm following you. In 00:10:03.980 --> 00:10:06.240 Act 2, the original Provenza is just another 00:10:06.240 --> 00:10:09.240 massive hit in a string of hits. It's mid show 00:10:09.240 --> 00:10:13.059 momentum. But the remix and the encore. That's 00:10:13.059 --> 00:10:16.179 a strategic deployment of pure adrenaline. Wow. 00:10:16.659 --> 00:10:18.919 It is designed to abruptly spike the audience's 00:10:18.919 --> 00:10:20.940 heart rate one last time. She is essentially 00:10:20.940 --> 00:10:23.159 sending them out of the arena into the parking 00:10:23.159 --> 00:10:25.480 lots, into the streets, completely energized. 00:10:25.639 --> 00:10:27.700 That's fascinating. It reframes the song from 00:10:27.700 --> 00:10:30.179 a standard performance into a grand finale celebration. 00:10:30.820 --> 00:10:33.120 It ensures the last memory the listener has isn't 00:10:33.120 --> 00:10:35.620 the heavy emotional weight of act three, but 00:10:35.620 --> 00:10:38.980 an absolute euphoric high. So she is actively 00:10:38.980 --> 00:10:41.519 manipulating the audience's physical energy as 00:10:41.519 --> 00:10:44.240 they leave the building to ensure they associate 00:10:44.240 --> 00:10:47.000 the entire concert experience with a feeling 00:10:47.000 --> 00:10:49.840 of total exhilaration. Exactly. It's brilliant 00:10:49.840 --> 00:10:52.580 show architecture. And here is where the narrative 00:10:52.580 --> 00:10:55.299 connects, because that emotional high, that feeling 00:10:55.299 --> 00:10:57.879 of immense perceived value as fans leave the 00:10:57.879 --> 00:11:00.879 building translates directly into the financial 00:11:00.879 --> 00:11:03.620 reality of the tour. Which brings us to the box 00:11:03.620 --> 00:11:05.240 office numbers, because if you want to talk about 00:11:05.240 --> 00:11:07.879 the economics of modern fandom, the numbers here 00:11:07.879 --> 00:11:10.320 are jaw dropping. They really are. The total 00:11:10.320 --> 00:11:15.159 gross revenue for these 33 dates is $72 ,243 00:11:15.159 --> 00:11:19.179 ,333. A staggering figure for a two -month run. 00:11:19.480 --> 00:11:21.759 Let's do the math on that. If you take that $72 00:11:21.759 --> 00:11:25.259 .2 million and divide it by the $412 ,840 total 00:11:25.259 --> 00:11:27.720 attendees, you are looking at an average ticket 00:11:27.720 --> 00:11:31.320 price of around $175 across the entire continent. 00:11:31.500 --> 00:11:33.919 Which is a premium price point. And the source 00:11:33.919 --> 00:11:36.700 breaks down the revenue show by show, revealing 00:11:36.700 --> 00:11:40.460 some, well, Massive peaks. The LA show, for example. 00:11:40.860 --> 00:11:43.580 Yeah. The highest -grossing single night was 00:11:43.580 --> 00:11:45.899 toward the end of the tour, October 22nd, in 00:11:45.899 --> 00:11:49.279 Los Angeles at the Crypto .com arena. That single 00:11:49.279 --> 00:11:52.580 night pulled in just shy of $3 million. Almost 00:11:52.580 --> 00:11:57.960 $3 million one night. Specifically? 2 ,998 ,273 00:11:57.960 --> 00:12:01.600 olders lies with 13 ,067 attendees. Which makes 00:12:01.600 --> 00:12:04.820 geographical and demographic sense. LA is a massive 00:12:04.820 --> 00:12:07.539 primary market with a huge Latin music fan base. 00:12:07.720 --> 00:12:10.200 Sure, LA makes sense. But here's the anomaly 00:12:10.200 --> 00:12:11.970 I teased at the beginning of the show. Let's 00:12:11.970 --> 00:12:14.490 look at Dallas, Texas on October 5th at the American 00:12:14.490 --> 00:12:16.509 Airlines Center and compare it to New York City 00:12:16.509 --> 00:12:18.769 on September 13th at Madison Square Garden. OK, 00:12:18.769 --> 00:12:20.610 let's compare them. Madison Square Garden and 00:12:20.610 --> 00:12:25.070 NYC had 13 ,575 fans in attendance and generated 00:12:25.070 --> 00:12:29.850 $2 ,748 ,506. Right, solid numbers. But Dallas, 00:12:29.870 --> 00:12:31.570 which actually had a slightly smaller crowd of 00:12:31.570 --> 00:12:38.129 13 ,484 fans, generated $2 ,847 ,119. So what 00:12:38.129 --> 00:12:39.929 does this all mean? How does a slightly smaller 00:12:39.929 --> 00:12:42.720 crowd in Texas generate roughly $100 ,000 more 00:12:42.720 --> 00:12:45.100 in revenue than a larger crowd at the most famous 00:12:45.100 --> 00:12:47.379 arena in New York. If we connect this back to 00:12:47.379 --> 00:12:50.080 your earlier question about whether the 100 % 00:12:50.080 --> 00:12:52.659 sellout was just cheap tickets, this data proves 00:12:52.659 --> 00:12:55.909 otherwise. What you are seeing here, deduced 00:12:55.909 --> 00:12:59.009 from these numbers, is the sheer power of localized 00:12:59.009 --> 00:13:02.070 demand. OK. The Wikipedia data doesn't use the 00:13:02.070 --> 00:13:04.210 industry jargon, but what this illustrates is 00:13:04.210 --> 00:13:07.409 highly elastic pricing. Can you break down elasticity 00:13:07.409 --> 00:13:09.850 for me? Because it sounds like an economics textbook. 00:13:10.210 --> 00:13:13.129 Absolutely. Think of elasticity like the surge 00:13:13.129 --> 00:13:16.230 pricing on an Uber app. OK. New York City is 00:13:16.230 --> 00:13:18.789 a massive market, yes. But on any given Tuesday 00:13:18.789 --> 00:13:21.970 night in September, there are 100 other concerts, 00:13:22.330 --> 00:13:25.009 Broadway shows, and a Events happening. The entertainment 00:13:25.009 --> 00:13:27.190 dollar is fractured. Right. Lots of competition 00:13:27.190 --> 00:13:29.889 for attention. But in Dallas, Texas, a Carol 00:13:29.889 --> 00:13:32.610 G Arena show might be the single biggest cultural 00:13:32.610 --> 00:13:35.429 event in the city that month. The demand is fiercely 00:13:35.429 --> 00:13:38.090 concentrated. So the ticketing systems recognize 00:13:38.090 --> 00:13:40.549 that concentrated demand. Right. The algorithms 00:13:40.549 --> 00:13:43.809 see 50 ,000 people fighting for 13 ,000 seats 00:13:43.809 --> 00:13:46.789 in Dallas and the ticket prices organically surge 00:13:46.789 --> 00:13:49.490 to match what that specific local market is willing 00:13:49.490 --> 00:13:51.169 to bear. Let me do the math on those numbers 00:13:51.169 --> 00:13:53.759 I just read. Go for it. The average ticket in 00:13:53.759 --> 00:13:55.820 New York was about two hundred and two dollars, 00:13:56.419 --> 00:13:58.759 but the average ticket in Dallas was over two 00:13:58.759 --> 00:14:01.059 hundred and eleven dollars. Exactly. Even though 00:14:01.059 --> 00:14:04.179 New York is theoretically the bigger global market, 00:14:04.740 --> 00:14:07.500 the specific density of her fandom in Texas drove 00:14:07.500 --> 00:14:10.090 the per ticket value higher. That makes total 00:14:10.090 --> 00:14:12.490 sense when you frame it like surge pricing. And 00:14:12.490 --> 00:14:14.470 honestly, it gets even wilder when we look at 00:14:14.470 --> 00:14:17.129 the floor of the tour, the smaller regional markets. 00:14:17.330 --> 00:14:19.889 Hidalgo, right? Yeah, Hidalgo. On September 30th, 00:14:19.929 --> 00:14:22.769 she played Paine Arena in Hidalgo, Texas. The 00:14:22.769 --> 00:14:26.590 attendance was only 5 ,343. It's by far the smallest 00:14:26.590 --> 00:14:29.169 crowd on the entire tour. Barely over 5 ,000 00:14:29.169 --> 00:14:32.240 people. But the revenue? Hidalgo still grossed 00:14:32.240 --> 00:14:37.720 over $1 .2 million. $12 ,207 ,507 to be exact. 00:14:37.860 --> 00:14:39.620 They compare that to a major coastal city. Look 00:14:39.620 --> 00:14:42.059 at Vancouver on October 29th. Okay, pulling up 00:14:42.059 --> 00:14:44.539 Vancouver, wow. Vancouver had more than double 00:14:44.539 --> 00:14:47.740 the attendance of Hidalgo, 11 ,201 people, but 00:14:47.740 --> 00:14:50.120 grossed significantly less money. It came in 00:14:50.120 --> 00:14:53.600 at $934 ,064. Let's do the math on that disparity. 00:14:53.740 --> 00:14:57.399 I'm calculating now. In Hidalgo, Texas, 5 ,343 00:14:57.399 --> 00:15:00.100 fans generating $1 .2 million means the average 00:15:00.100 --> 00:15:03.120 ticket was roughly $226. Huge number. But in 00:15:03.120 --> 00:15:08.779 Vancouver, 11 ,201 fans generating 934 ,000 means 00:15:08.779 --> 00:15:11.740 the average ticket was only about $83. This is 00:15:11.740 --> 00:15:14.659 exactly what we mean by elasticity. She has successfully 00:15:14.659 --> 00:15:17.899 played massive coastal hubs like L .A. and NYC, 00:15:17.899 --> 00:15:20.679 but she also targeted highly concentrated regional 00:15:20.679 --> 00:15:23.899 markets like Hidalgo, Texas and Fresno, California. 00:15:24.179 --> 00:15:25.740 Oh, so they really knew their audience. They 00:15:25.740 --> 00:15:28.320 did. They dynamically scaled the revenue in these 00:15:28.320 --> 00:15:30.820 smaller markets, capturing incredibly high per 00:15:30.820 --> 00:15:32.980 ticket yields where the fandom was most dense 00:15:32.980 --> 00:15:35.360 while still moving high volumes of tickets at 00:15:35.360 --> 00:15:37.679 lower prices in newer markets like Vancouver. 00:15:37.940 --> 00:15:39.519 Right. Expanding the footprint. And they did 00:15:39.519 --> 00:15:42.600 all of this while... losing that 100 % sellout 00:15:42.600 --> 00:15:45.100 metric. It really highlights how flawless the 00:15:45.100 --> 00:15:48.299 business execution was. AEG and Carol G.'s team 00:15:48.299 --> 00:15:51.039 knew exactly what each specific market could 00:15:51.039 --> 00:15:53.100 bear, and they priced it perfectly. You can't 00:15:53.100 --> 00:15:55.200 fake those margins. Which is precisely why this 00:15:55.200 --> 00:15:58.159 tour earned deep critical and industry respect. 00:15:58.559 --> 00:16:00.519 The source notes that the Strip Love Tour won 00:16:00.519 --> 00:16:03.220 Tour of the Year at the 2023 Latin American Music 00:16:03.220 --> 00:16:05.960 Awards and secured a nomination for the same 00:16:05.960 --> 00:16:08.919 title at the Billboard Latin Music Awards. Winning 00:16:08.919 --> 00:16:11.679 Tour of the Year against every other Latin artist 00:16:11.679 --> 00:16:14.299 touring that entire year. Yes. And in the live 00:16:14.299 --> 00:16:16.529 music industry, an award like that isn't just 00:16:16.529 --> 00:16:19.529 a popularity contest, it's a recognition by industry 00:16:19.529 --> 00:16:21.690 peers of exactly what we've been unpacking. The 00:16:21.690 --> 00:16:24.450 logistics. Flawless, highly elastic business 00:16:24.450 --> 00:16:27.809 execution paired with a meticulously paced product 00:16:27.809 --> 00:16:30.549 that leaves audiences completely exhilarated. 00:16:30.830 --> 00:16:33.860 It is the ultimate case study. So, to synthesize 00:16:33.860 --> 00:16:36.159 all of this for you listening, the Strip Love 00:16:36.159 --> 00:16:38.679 Tour wasn't just a series of fun concerts, it 00:16:38.679 --> 00:16:42.940 was a $72 .2 million strategic triumph. Absolutely. 00:16:43.200 --> 00:16:45.899 We saw how the brutal logistics of 33 dates in 00:16:45.899 --> 00:16:48.649 60 days... required a scientifically paced set 00:16:48.649 --> 00:16:51.549 list. We saw how that set list manipulated the 00:16:51.549 --> 00:16:53.710 audience's heart rate, bringing them down with 00:16:53.710 --> 00:16:56.169 the emotional ballads of Act Three, only to blast 00:16:56.169 --> 00:16:58.950 them out the doors with a high BPM remix encore. 00:16:59.029 --> 00:17:01.450 A brilliant move. And we saw how that emotional 00:17:01.450 --> 00:17:03.850 high fueled an economic engine, allowing her 00:17:03.850 --> 00:17:06.069 team to squeeze maximum revenue out of both Los 00:17:06.069 --> 00:17:09.349 Angeles arenas and tiny Texas border towns. Above 00:17:09.349 --> 00:17:12.069 all, it was about giving a fiercely loyal fan 00:17:12.069 --> 00:17:14.569 base exactly what they wanted, down to the very 00:17:14.569 --> 00:17:17.400 last ticket. It is an undeniable achievement, 00:17:17.720 --> 00:17:20.099 but you know, looking at the broader chronology 00:17:20.099 --> 00:17:22.059 provided in the source material, this raises 00:17:22.059 --> 00:17:24.460 an important question. One that I think is worth 00:17:24.460 --> 00:17:27.039 dwelling on after the applause fades. What's 00:17:27.039 --> 00:17:29.160 that? We established right at the beginning that 00:17:29.160 --> 00:17:31.960 the 2022 Strip Love Tour was sandwiched directly 00:17:31.960 --> 00:17:34.339 between the Bishoda Tour, which ended earlier 00:17:34.339 --> 00:17:36.980 that same year, and the Manana Sarabanido Tour, 00:17:37.059 --> 00:17:39.880 which immediately fired up in 2023. Right. It's 00:17:39.880 --> 00:17:41.940 just back to back to back touring, three straight 00:17:41.940 --> 00:17:44.819 years on the road. Exactly. So here's the lingering 00:17:44.819 --> 00:17:48.099 thought. When an artist is caught in a seemingly 00:17:48.099 --> 00:17:52.019 endless multi -year cycle of these 100 % sold 00:17:52.019 --> 00:17:56.079 out, highly engineered arena tours, at what point 00:17:56.079 --> 00:17:58.759 does the human toll of that relentless logistical 00:17:58.759 --> 00:18:01.420 machine begin to outweigh the financial rewards? 00:18:01.640 --> 00:18:04.619 That's a heavy question. It is. Can a human being 00:18:04.619 --> 00:18:07.519 sustain that kind of precisely timed, emotionally 00:18:07.519 --> 00:18:10.440 draining momentum for three straight years without 00:18:10.440 --> 00:18:12.980 losing the raw, spontaneous inspiration that 00:18:12.980 --> 00:18:15.480 built the fan base in the very first place? The 00:18:15.480 --> 00:18:18.319 logistics machine demands daily perfection, but 00:18:18.319 --> 00:18:20.339 art usually thrives on a little bit of chaos 00:18:20.339 --> 00:18:23.279 and breathing room. Wow. That is a fascinating 00:18:23.279 --> 00:18:25.839 tension to consider. We talked at the beginning 00:18:25.839 --> 00:18:27.839 about how a tour like this captures lightning 00:18:27.839 --> 00:18:30.799 in a bottle and turns it into a perfectly engineered 00:18:30.799 --> 00:18:33.559 electrical grid. Right. But you really have to 00:18:33.559 --> 00:18:36.119 wonder what happens to the artist who has to 00:18:36.119 --> 00:18:39.299 function as the generator for that grid night 00:18:39.299 --> 00:18:42.019 after night, year after year. It's an incredible 00:18:42.019 --> 00:18:44.480 thought to leave on. Thank you so much for sending 00:18:44.480 --> 00:18:46.680 in this data. It really allowed us to peel back 00:18:46.680 --> 00:18:49.140 the curtain and look at the math behind the magic. 00:18:49.319 --> 00:18:51.220 My pleasure. Keep digging into the things that 00:18:51.220 --> 00:18:53.799 make you curious, and we will catch you on the 00:18:53.799 --> 00:18:54.660 next Deep Dive.