WEBVTT 00:00:00.001 --> 00:00:03.520 Welcome everyone to the Location Insights Podcast. 00:00:03.520 --> 00:00:08.960 I am Ryan Imaizumi, in charge of global marketing at unerry. 00:00:08.500 --> 00:00:13.300 Today, I would like to talk about marketing utilizing location data. 00:00:13.300 --> 00:00:14.900 Our guest today is Mr. 00:00:14.900 --> 00:00:18.980 Takeshi Yamamoto, the founder of Macagua. 00:00:18.760 --> 00:00:19.160 Mr. 00:00:19.160 --> 00:00:22.040 Yamamoto, thank you for being here today. 00:00:22.040 --> 00:00:25.080 I am Takeshi Yamamoto from Macagua. 00:00:25.080 --> 00:00:26.360 Thank you for having me. 00:00:26.360 --> 00:00:27.400 Well then, first, Mr. 00:00:27.400 --> 00:00:28.760 Yamamoto. 00:00:28.560 --> 00:00:33.760 Could you please tell us what kind of company Macagua is, as well as your own background? 00:00:33.760 --> 00:00:34.880 Yes, certainly. 00:00:34.880 --> 00:00:39.280 Macagua is still a very new company, only one year old. 00:00:39.060 --> 00:00:42.820 It's a company that has finally reached its first anniversary. 00:00:42.820 --> 00:00:50.100 To introduce the company, I'll need to talk a bit about my background and the flow of events, so I'd like to guide you through that first 00:00:50.200 --> 00:00:59.240 Regarding Macagua, the services we primarily handle are foot traffic data and marketing solutions that utilize foot traffic data 00:00:59.240 --> 00:01:08.600 Back in 2019, there was a company using this foot traffic data, and at the time, that company operated under the name Near. 00:01:08.620 --> 00:01:14.700 I joined them as the person responsible for opening up the Japanese market and market development. 00:01:14.700 --> 00:01:18.620 Over time, that company, Near, changed its name 00:01:18.540 --> 00:01:23.580 From near to near intelligence and from near intelligence to azira. 00:01:23.580 --> 00:01:26.140 The names changed in this way 00:01:26.340 --> 00:01:35.540 Among those, the final company, Azira, is currently headquartered in the United States, and I was representing their Japanese operations. 00:01:35.540 --> 00:01:42.020 Azira decided to stop its operations in Japan to concentrate on America and other countries. 00:01:42.020 --> 00:01:45.380 So, last year my contract ended. 00:01:44.900 --> 00:01:55.940 But in order to continue supporting the Japanese market and to further grow the Japanese market, I started the company Macagua and took over the sales rights for Azira in Japan. 00:01:55.820 --> 00:02:02.860 Therefore, although Macagua is a recently established company, we mainly deal with foot traffic data. 00:02:02.860 --> 00:02:08.780 Since around 2000, I have been involved in marketing solutions in the internet sphere 00:02:08.660 --> 00:02:19.220 At that time I was mainly doing affiliate marketing, but since then, search engines like SEM and SEO, and what we now call word-of-mouth or buzz marketing 00:02:19.460 --> 00:02:23.220 I have incorporated and supported various marketing methods. 00:02:23.220 --> 00:02:32.180 Because I can support customers in the area of digital marketing by combining these, I have made that the scope of business for Macagwa. 00:02:31.960 --> 00:02:33.240 Thank you very much. 00:02:33.240 --> 00:02:34.600 That was very clear. 00:02:34.600 --> 00:02:41.160 I understand that Macagua develops services such as location marketing, utilizing foot traffic data. 00:02:41.160 --> 00:02:45.320 For example, how are companies utilizing or using location marketing? 00:02:45.320 --> 00:02:48.360 And what kind of merits are there for the companies? 00:02:48.360 --> 00:02:50.280 Yes, let's see. 00:02:50.160 --> 00:02:57.040 What we primarily handle is what is called foot traffic data, or some people say human movement data. 00:02:57.040 --> 00:02:59.120 What is human movement data? 00:02:58.800 --> 00:03:06.400 Well, it means that because people are moving, it actually means that everyone is walking around with a smartphone these days. 00:03:06.400 --> 00:03:10.720 We collect movement data from smartphones all over the world. 00:03:10.620 --> 00:03:16.620 We stock, classify, and organize a vast amount of data to make it usable. 00:03:16.620 --> 00:03:19.100 To imagine what can be done with this 00:03:19.240 --> 00:03:22.840 For example, let's say you are running a shop yourself. 00:03:22.840 --> 00:03:31.480 You know the faces of the people who come to your shop well, but from where, what distance, and via what route did they reach your shop? 00:03:31.420 --> 00:03:35.180 Or they come to your shop often and are a good customer. 00:03:35.180 --> 00:03:39.260 But are they also a good customer of the shop across the street? 00:03:39.340 --> 00:03:43.260 If you only look at what's right in front of you, you don't really know. 00:03:43.260 --> 00:03:49.100 If you observe customers' movement, this isn't very useful if it's just a single ramen shop. 00:03:48.800 --> 00:04:01.120 But if you're running a chain with nationwide expansion, or if you hold multiple real estate properties, when you try to do those things to strengthen your business, you naturally want to know and analyze 00:04:01.140 --> 00:04:04.420 What kind of people are currently coming to my shop or tenant? 00:04:04.420 --> 00:04:05.860 Where are they coming from? 00:04:05.860 --> 00:04:08.019 What other places are they visiting? 00:04:08.019 --> 00:04:12.980 Based on that analysis, you build and execute a strategy for the next move 00:04:12.860 --> 00:04:18.060 The clues for that, or perhaps the foothold, is human movement data. 00:04:18.060 --> 00:04:23.180 Since it's only movement data, we don't know what they bought or how much they spent 00:04:23.139 --> 00:04:27.620 However, by looking at that movement, you can gradually understand that person. 00:04:27.620 --> 00:04:30.100 That's how it's often utilized. 00:04:30.100 --> 00:04:36.180 Once you understand this, for example, let's say there is someone who goes to Starbucks often 00:04:36.139 --> 00:04:40.220 There is also a coffee chain in Japan called Tully's Coffee. 00:04:40.220 --> 00:04:44.540 For example, if I were a marketer for a certain Tully's coffee, I'd think. 00:04:44.740 --> 00:04:46.660 Man, Starbucks is great. 00:04:46.660 --> 00:04:49.060 It's so nice they have so many people coming. 00:04:49.060 --> 00:04:53.780 If even 10% of them came to us, our sales would go up significantly 00:04:53.760 --> 00:05:00.000 So, you'd want to run ads or promotions targeting people who visit Starbucks to lure them to your shop. 00:05:00.000 --> 00:05:05.920 However, you can't just watch the front of the store all day and you can't keep handing out flyers forever. 00:05:05.940 --> 00:05:17.140 In such cases, while looking at human movement data, you can see how many such people there are, what percentage of them come to us, and see people's foot traffic 00:05:17.340 --> 00:05:27.260 Moreover, because we are looking at customer movement on a mobile phone basis, it's recently become possible to run ads on those smartphones. 00:05:27.139 --> 00:05:32.979 So you could try running ads, and by doing so, entice people to come here. 00:05:32.979 --> 00:05:36.900 What I just mentioned at the end is the general gist of it. 00:05:36.740 --> 00:05:40.500 You think of the next move after performing an analysis. 00:05:40.500 --> 00:05:46.500 Among those moves, there are various options, like handing out flyers or putting up signs. 00:05:46.160 --> 00:05:53.200 But since everyone has a smartphone in hand now, you run ads inside the smartphone to encourage behavior change. 00:05:53.200 --> 00:05:55.600 That is how it can be utilized. 00:05:55.520 --> 00:05:58.160 Sorry, that got a bit long. 00:05:58.160 --> 00:05:59.440 Thank you. 00:05:59.440 --> 00:06:00.480 I see. 00:06:00.480 --> 00:06:07.040 So from traditional paper flyers, you are now already looking at customer movement, deciding on a target 00:06:06.979 --> 00:06:11.699 and delivering ads to targeted people within their mobile phones and smartphones. 00:06:11.699 --> 00:06:18.419 In that sense, it means you can perform specialized targeting and marketing towards people who are interested in your business. 00:06:18.360 --> 00:06:24.360 So there are merits like the accuracy becoming very high and efficiency also improving, right? 00:06:24.600 --> 00:06:26.360 That is exactly right. 00:06:26.360 --> 00:06:27.240 Yes. 00:06:27.240 --> 00:06:28.280 Thank you. 00:06:28.420 --> 00:06:29.380 I see. 00:06:29.380 --> 00:06:35.540 So this target marketing, this location marketing, probably started earlier globally compared to Japan. 00:06:35.540 --> 00:06:39.140 For example, you mentioned the American company you were previously at. 00:06:39.340 --> 00:06:41.100 Near became a zero. 00:06:41.100 --> 00:06:50.860 When you were working at Near, for example, where there are representative cases of global utilization, you've done marketing utilizing foot traffic data in various forms. 00:06:50.760 --> 00:06:55.640 But among those, are there any particularly impressive cases you could share with our listeners? 00:06:55.640 --> 00:06:56.520 Yes. 00:06:56.520 --> 00:07:01.400 Roughly speaking, within marketing solutions, there is the ad delivery business. 00:07:01.260 --> 00:07:10.300 On the other hand, if we have the ad delivery solution in the right hand, in the left hand, we have the foot traffic data analysis solution. 00:07:10.039 --> 00:07:17.560 The order is to analyze human movement and then deliver ads to the optimal target at the optimal location. 00:07:17.560 --> 00:07:18.680 For example, 00:07:18.400 --> 00:07:28.560 Considering just the ad delivery business, we have a database of what kind of buildings or shops are at various locations based on past visit points. 00:07:28.340 --> 00:07:41.060 We track customer movement continuously, and by combining those visit points and movement trends, combining location data with human movement data, we perform so-called user profiling 00:07:41.220 --> 00:07:47.620 Near and Azra, currently Azira, do not hold any personal information at all. 00:07:47.620 --> 00:07:52.820 However, while looking at the history of how smartphone location information visits and moves 00:07:52.840 --> 00:08:00.520 We look at the visit points, what kind of brand shops are there, whether it's for men or women, and what age group the shop targets. 00:08:00.520 --> 00:08:06.840 We have that as separate information and have AI and machine learning analyzing that data. 00:08:06.539 --> 00:08:20.780 By doing so, because a single user ID has a certain visit tendency, we can identify, for example, a company employee or someone who often goes to shops for young people and regularly visits to a college. 00:08:20.340 --> 00:08:22.340 So they are likely a student. 00:08:22.340 --> 00:08:29.300 Based on the brands of shops visited, they often go to places for men, so they are likely a male student. 00:08:29.300 --> 00:08:31.220 We do such profiling 00:08:31.420 --> 00:08:38.539 Based on this, we run targeted ads, which is what Near and AZIRA have been doing for a long time. 00:08:38.539 --> 00:08:42.780 While profiling and targeting are common in the world of advertising, 00:08:42.400 --> 00:08:51.600 The characteristic part is looking at foot traffic and seeing visit tendencies, looking at behavior in the real world, which is very useful. 00:08:51.540 --> 00:09:06.100 Based on such data, for example, what is happening now is that when companies perform marketing, they usually look at behavioral logs left on digital platforms or what content is frequently read when running targeted ads. 00:09:05.740 --> 00:09:12.300 They perform so-called contextual targeting while seeing what content is frequently engaged with. 00:09:12.300 --> 00:09:16.540 That refers to digital footprints when running targeting ads 00:09:16.440 --> 00:09:28.040 In the case of Near and AZiRA, we look at the foot traffic of where customers visit in their real lives, not on digital platforms, and perform so-called real behavioral targeting 00:09:28.220 --> 00:09:34.940 By combining these two types of data, it becomes possible to run high-precision, accurate targeting. 00:09:34.940 --> 00:09:37.100 This kind of thing, for example 00:09:36.900 --> 00:09:49.460 Companies know data for people who came to their shop, people who came to a physical shop, or people who visited an e-commerce shop, and they know the data of people who bought things or registered as members. 00:09:49.240 --> 00:09:54.520 There is a database called CRM for maintaining relationships with customers. 00:09:54.520 --> 00:10:00.280 By looking at purchase data and such, they have data for those who came or bought things. 00:09:59.940 --> 00:10:06.260 However, they have no way to capture those who came to the shop but left without buying anything. 00:10:06.260 --> 00:10:11.140 Online, if it's e-commerce, you know someone clicked an ad and entered. 00:10:10.959 --> 00:10:14.000 But you don't know the person who left without buying. 00:10:14.000 --> 00:10:21.200 At physical shops like department stores, people who came but left without buying anything don't stay in the database. 00:10:21.200 --> 00:10:22.880 You don't know them well. 00:10:22.660 --> 00:10:26.740 And people who came to your shop surely go to competing stores as well. 00:10:26.740 --> 00:10:32.500 If you wonder where they go, you say welcome and thank you to people who come to your shop. 00:10:32.459 --> 00:10:40.060 But you don't even know if they turned right, turned left, or entered the competing department store across the street afterward 00:10:40.000 --> 00:10:50.640 By combining real-world data and digital data in these areas, you can better understand what kind of customers came to your shop and how to reach such customers. 00:10:50.339 --> 00:10:54.180 By combining data, you can refine your customer image. 00:10:54.180 --> 00:11:02.100 We've been providing services to extend CRM data using data near holds for over five years overseas 00:11:02.240 --> 00:11:05.680 We do so-called first-party data extension. 00:11:05.680 --> 00:11:09.600 However, in Japan, it's been difficult to get this accepted. 00:11:09.600 --> 00:11:13.040 That's been a challenge in my five years of activity. 00:11:13.040 --> 00:11:25.760 However, something similar to this, running campaigns by combining online and offline data, has been proven to yield very high performance overseas, and we've done a few trials in Japan as well 00:11:25.759 --> 00:11:36.319 Since it results in quite high conversions and high click-through rates, marketing that combines the real world and the digital world must be done in a more targeted way. 00:11:36.319 --> 00:11:37.920 And I think it will increase 00:11:38.339 --> 00:11:40.500 There is room to grow it. 00:11:40.500 --> 00:11:47.060 So this would be a good case of starting overseas first and then seeing how it went when trying it in Japan. 00:11:47.220 --> 00:11:48.740 Yes, thank you. 00:11:48.740 --> 00:11:57.540 Especially combining real and online, I can really imagine how customer understanding deepens and targeting precision improves. 00:11:57.360 --> 00:12:04.000 Specifically, for example, in America, it's probably a service or movement that particularly started there. 00:12:04.000 --> 00:12:09.280 But based on your experience, what are some other markets where this is being utilized? 00:12:09.280 --> 00:12:10.160 Yes. 00:12:10.339 --> 00:12:17.380 Regarding the first-party data extension I just mentioned, it's actually being done quite actively in Australia. 00:12:17.380 --> 00:12:21.380 Major publishers have been doing this with us for a long time. 00:12:21.100 --> 00:12:31.899 It's highly effective, and for publishers, in Japan, they are exhausting themselves by undercutting each other on media fees, but in Australia, it's the opposite 00:12:32.040 --> 00:12:37.800 The media performance improves because it leads to an understanding of the customer image. 00:12:37.800 --> 00:12:40.760 They can make great proposals to clients. 00:12:40.720 --> 00:12:45.200 This allows them to further increase their value as premium media. 00:12:45.200 --> 00:12:48.640 There are various other use cases in other countries 00:12:48.540 --> 00:12:55.500 There is the aspect of improving results by using audience profiles, and for an example, from another country, Europe. 00:12:55.500 --> 00:13:01.660 Azira has a base in France as well, and what they are doing there is, I think it was Paris. 00:13:01.640 --> 00:13:05.880 It seems that distributing paper flyers is already prohibited. 00:13:05.880 --> 00:13:08.200 So, what is happening there? 00:13:08.200 --> 00:13:11.080 Paper flyers were likely used to say. 00:13:10.959 --> 00:13:17.279 Please come to the shop at key points and guide people, but that has been entirely replaced by digital. 00:13:17.279 --> 00:13:22.240 Instead of digital flyers, they run audience targeting ads tailored to the location 00:13:22.440 --> 00:13:32.600 This eliminates wasted effort and resource drain, and based on past foot traffic, which leads into location marketing, location marketing is becoming more digitalized 00:13:32.300 --> 00:13:37.820 It feels like it's operating as a major success case of a transition from paper to digital. 00:13:37.820 --> 00:13:39.100 Thank you. 00:13:39.100 --> 00:13:46.220 Yes, from paper to digital, plus the ability to target, I feel there are various possibilities there. 00:13:46.040 --> 00:13:51.560 You mentioned earlier that there are still various challenges for location marketing in Japan. 00:13:51.560 --> 00:13:55.800 Specifically, how do you think location marketing will spread in Japan? 00:13:55.400 --> 00:14:01.960 For example, what kind of companies could utilize location marketing and what kind of merits would those companies have? 00:14:01.960 --> 00:14:04.280 Please let us know your views. 00:14:04.280 --> 00:14:05.240 Yes. 00:14:05.440 --> 00:14:10.480 What I've mentioned so far were mostly stories leaning towards marketing campaigns. 00:14:10.480 --> 00:14:18.560 Of course, I think there is room for improvement and more utilization on the marketing campaign side, but from a different perspective. 00:14:18.440 --> 00:14:24.520 Data on how what kind of people are moving around the globe is the basis of foot traffic data. 00:14:24.520 --> 00:14:28.600 It's becoming clear that this can be used in various industries 00:14:28.540 --> 00:14:39.740 unerry is also active in this industry, so you know well, but commercial facilities, restaurant chains, and where people are and what routes they use to enter. 00:14:39.740 --> 00:14:43.260 Store opening plans, closing plans. 00:14:43.220 --> 00:14:46.180 So-called store opening strategies. 00:14:46.180 --> 00:14:52.340 When you move large amounts of capital and cannot afford to fail, a lot of preparation is necessary. 00:14:52.340 --> 00:14:57.540 You can derive where to open a store more precisely based on human movement. 00:14:56.860 --> 00:14:58.700 You can do those things. 00:14:58.700 --> 00:15:02.860 Real estate, restaurant chains, and shops inside them. 00:15:02.860 --> 00:15:05.900 Retail stores in large shopping malls. 00:15:05.600 --> 00:15:11.839 If it's an industry that moves people in the real world, it can be utilized in almost any industry. 00:15:11.839 --> 00:15:18.240 This isn't recent, but money flows to where people shop was the main driver of economic activity 00:15:18.440 --> 00:15:24.040 While people say it is less expensive in the digital world with e-commerce. 00:15:24.040 --> 00:15:28.680 Actually, the Japan Ministry of Economy, Trade and Industry said a few years ago 00:15:28.839 --> 00:15:33.319 90% of the economy consists of the actual movement of people. 00:15:33.319 --> 00:15:38.519 In that sense, we have to look at this 90% of the economy properly. 00:15:38.240 --> 00:15:46.000 Since almost everything is managed by people moving, if you look around outside, money flows to where people are moving. 00:15:46.000 --> 00:15:50.800 I believe it can be utilized in various industries right before our eyes 00:15:50.820 --> 00:15:55.140 Dining, real estate, and the retail shops inside. 00:15:55.140 --> 00:16:00.260 By performing proper analysis and comparing with competitors and other shops, 00:16:00.060 --> 00:16:06.940 We can continue to develop not just existing marketing methods, but marketing methods that utilize such data. 00:16:06.940 --> 00:16:14.620 Another interesting trend from the last few years is in the financial industry, what is called alternative data. 00:16:13.740 --> 00:16:17.340 Finance combines various data for things like quants. 00:16:17.340 --> 00:16:22.700 Previously, they used artificial satellites to look at human movement, trucks. 00:16:22.440 --> 00:16:30.360 And the movement of materials entering factories to predict that something will be shipped from the factory months later and lined up in stores. 00:16:30.000 --> 00:16:38.880 They looked at how much economic effect there would be when those products sold, but now human movement data is gradually becoming a support material. 00:16:38.759 --> 00:16:43.079 Since these things are happening, it's not just visible objects moving. 00:16:43.079 --> 00:16:49.959 Even in invisible areas, which might mean money moving, utilization will progress in such industries 00:16:50.120 --> 00:16:51.240 Thank you. 00:16:51.240 --> 00:17:02.839 Yes, alternative data, utilizing foot traffic data in the financial industry specifically, is a very interesting initiative, and I can really imagine utilization progressing further and further. 00:17:03.020 --> 00:17:03.900 Thank you. 00:17:03.900 --> 00:17:08.940 Another thing I wanted to ask is that AI has been attracting a lot of attention lately. 00:17:08.940 --> 00:17:10.540 For example, as you mentioned 00:17:10.560 --> 00:17:18.000 Predicting store openings or signs and predictions of where to open stores are already being used in our location data business. 00:17:18.000 --> 00:17:21.520 But going forward by combining AI and location data. 00:17:21.760 --> 00:17:27.039 What kind of new products, services, or markets do you feel or think will be born? 00:17:27.039 --> 00:17:28.160 Yes. 00:17:28.160 --> 00:17:32.960 We still need to think about what various things it can be used for from now on. 00:17:32.940 --> 00:17:36.860 So I haven't reached a specific this is it yet. 00:17:36.860 --> 00:17:39.419 But for example, tourism. 00:17:39.419 --> 00:17:43.019 Lately, there are many stories about tourism. 00:17:42.740 --> 00:17:46.980 In tourism, you can grasp trends from people's movement. 00:17:46.980 --> 00:17:55.860 People often worry about what should the next move be, because they could not analyze the situation so they don't know what hand to play next. 00:17:55.640 --> 00:18:00.200 There are companies working on using AI to make proposals. 00:18:00.200 --> 00:18:04.680 This might just be an example from tourism right now, but it's not just tourism. 00:18:04.680 --> 00:18:10.120 It includes store opening plans where you feed past data and analyze it. 00:18:09.840 --> 00:18:16.720 Based on those results, you try executing, and successes and failures naturally emerge. 00:18:16.720 --> 00:18:23.280 By ruminating on those, I believe more precise proposals and strategic decisions can be made 00:18:23.240 --> 00:18:29.240 In terms of AI utilization, I think such things will continue to progress in the future. 00:18:29.240 --> 00:18:30.120 Thank you. 00:18:30.120 --> 00:18:36.280 Yes, we often hear that data can be seen, but it's unclear what specifically to do next. 00:18:36.140 --> 00:18:45.420 So AI being able to propose or suggest specific next actions or what options exist makes it very easy to imagine as a form of utilization. 00:18:45.420 --> 00:18:50.780 Indeed, this is the last thing, but regarding location marketing. 00:18:49.860 --> 00:18:55.700 While its utilization progresses globally and in Japan, I'm sure there are various challenges. 00:18:55.700 --> 00:19:02.659 If there are challenges you see now or things that might become challenges in the future, even if not right now, 00:19:02.560 --> 00:19:05.120 Could you please share them with our listeners? 00:19:05.120 --> 00:19:06.080 Yes. 00:19:06.080 --> 00:19:14.080 After all, if you observe movement information continuously, as I said earlier, you become able to do things like profiling. 00:19:13.760 --> 00:19:24.400 While you can't strictly identify individuals like their gender, age, or email address, as things progress, you can somehow smoke out things that are really close to that 00:19:24.540 --> 00:19:29.020 So I think concerns regarding privacy will continue to rise in the future. 00:19:29.020 --> 00:19:34.540 In Azira's case, we tackle service and development with a privacy-first approach. 00:19:34.240 --> 00:19:40.080 But depending on the country, Europe is particularly strict with things like GDPR. 00:19:40.080 --> 00:19:48.000 Depending on the country, or not just the country, but when everyone's interest in privacy regarding digital data rises further. 00:19:47.500 --> 00:19:55.340 We must continue to watch how location information changes and how its handling changes and respond appropriately. 00:19:55.340 --> 00:19:57.820 I think that is a future challenge 00:19:57.940 --> 00:20:02.740 I believe it is a part where we need to deal with it successfully in advance. 00:20:02.740 --> 00:20:03.860 Thank you. 00:20:03.860 --> 00:20:05.139 Yes. 00:20:05.139 --> 00:20:10.980 In North America, specifically America, rules change or differ by state. 00:20:10.840 --> 00:20:17.480 And regarding using or acquiring people's location information, rules change frequently in each state. 00:20:17.480 --> 00:20:20.600 So it's about getting along with that successfully. 00:20:20.260 --> 00:20:26.500 It might not be that it can't be used or done, but we must follow each rule to acquire and utilize it. 00:20:26.500 --> 00:20:30.500 So that is a challenge each company must follow properly, right? 00:20:30.919 --> 00:20:33.960 Yes, that's exactly right. 00:20:33.960 --> 00:20:35.080 Thank you. 00:20:35.080 --> 00:20:36.520 Today we heard from Mr. 00:20:36.520 --> 00:20:41.799 Takeshi Yamamoto of Macagua about marketing using various location data 00:20:41.860 --> 00:20:49.460 From global to domestic and how this will be used in the future, including AI and how it will be utilized in location marketing. 00:20:49.460 --> 00:20:51.860 And finally, we talked about challenges 00:20:51.840 --> 00:20:56.559 You shared various knowledge and experiences regarding location marketing in many forms. 00:20:56.559 --> 00:20:57.039 Mr. 00:20:57.039 --> 00:21:00.400 Yamamoto, thank you very much for your time today. 00:21:00.400 --> 00:21:01.440 Thank you.