Companies that sell experiences instead of products are surviving in the era. I hope you will learn practical methodologies for experience design through the DCX framework in the course Data-Based Customer Experience Design by Professor Cha Kyung-jin at Hanyang University. Our students are not engineering students. When they first encounter Python in their first or second year, they excel at it. If you wonder why students majoring in liberal arts are so good at it, it's because they have excellent creativity and planning skills. So, by leaping to connect certain data and realizing, Ah, this is how we can solve this problem, they bring the data together. It's hopeful. I was struck by a few things you just mentioned. Firstly, attention to detail is necessary. To understand the context of the customers, you need to look at the data and also have real experiential knowledge. That's right. Why do you need to look at it? Because every customer is different. In fact, if all customers were the same, you could just use qualitative methodologies.

When conducting surveys or using them to understand popularity and targeting, we need to realize that our customers are more diverse than we think. They have different personas, and sometimes even one customer may exhibit different personas. It feels like one person has multiple personalities. Depending on whether they are playing the role of a daughter-in-law, a professor, a mother, or staying in a space like a school, home, or outside, the context varies greatly. Therefore, even with the same TV, people have different experiences based on the context. To deliver the right message at the right timing regardless of the context, efforts are being made to re-sense the data to provide experiences tailored to each context. In my case, even though it's not exactly the same when watching TV with my child compared to watching alone, the channel to tune in while having a meal or at other times varies, and what needs to be watched is different.

If you have it, it will provide a different experience. If a journalist who invests in U.S. stocks appears in front of you, briefing you on stocks, the contextual experiences will be different. If you don't focus on that, people won't know why they should buy a TV. They might think, Why should I buy a TV? So, we need to provide experiences that attract people to buy a display. But the more important fact is that once they experience it, they can't buy another TV. Why is that? The answer is that data is dependent, so it knows the words best. It knows what I've received, what my tendencies are, and that I don't trade stocks on Saturday mornings, so it knows what I will watch. It is more tailored to me. To build that up again, that's why our customers, in order to order our products, need a customer experience based on data.

For example, if you can't switch from Android to iPhone, it's because of the apps I bought. Due to the health data accumulated there, it's not easy to transfer to Android. So, I feel like I'm becoming more and more dependent on the Apple ecosystem. Initially, I only bought an iPhone, but when I bought a tablet next time, since it's all connected, I felt the need to buy an iPad, and because they are connected, I had to buy a MacBook. Within that ecosystem, it inevitably leads me to keep consuming. Right, there are various values ​​that can be provided through experiences. Some people buy it for functional value, some for quality, some for economic benefits or premium value. So, why do we buy? We buy now for experiences and meanings. According to the theory of the consent of consumption, people invest money for connections, and then happiness follows. Happiness comes from connections.

To be honest, when I buy daily necessities, I am quite sensitive to expensive things. For happiness, it's not 1,000 won for toilet paper, but rather 100,000 won. When I travel and spend money, I hardly check the price tag and just spend. In Korea, when living daily life, I keep searching for what's cheaper and buy it. However, the experience of connection is important. Not long ago, I had an Apple iPad, and there was a Magic Keyboard that connects to it, which costs over 500,000 won. Normally, you can buy a Bluetooth keyboard for around 100,000 won, so I hesitated a lot. It can work with Bluetooth, but still, I see Apple users who end up buying it because it enhances the value of their Apple phone, Mac, and other products connected to it. It seems like they tend to spend without feeling the price is wasted because they consider the value of the connection. The key point is that you need to consider the value of the connection.

We connect the TV and the refrigerator, connect the TV and the water purifier, and even think about connecting the TV to other ecosystems. If we continue to think about these connections, the value of connection comes as a huge experience to customers. So, instead of looking at cost-effectiveness, we will look at emotional value. Therefore, wouldn't it lead to not going to that store anymore? So, because we think about these things, at this CES as well, I think the importance of connection connectors has emerged as a hot topic. The value of this connection, once everything is connected, people will never want to go back to the situation before it was connected. In my case, these days, people don't want to drive cars that don't support Android Auto or Apple CarPlay. Since I changed my phone and now Android Auto doesn't work in my car, I've been thinking about all sorts of things. The phenomenon of preferring apartments could also increase, and the value of remodeling companies could rise.

It could be possible. Moving forward, rather than just making remodeling clean, we will put in all digital devices to live comfortably. The remodeling company might join us, and eventually, Samsung or LG could even become the remodeling company. We will connect everything and take care of it. In reality, construction companies are already looking into creating adaptable spaces, not just to attract residents but to connect with data and design experiences within that space. I am currently collaborating with another company that is working on senior residences. We are planning to build 40 in Korea. We are already working on projects in Busan and Magok. The experience design for active seniors will be tailored to their data, providing all services accordingly. We will design experiences within that context, considering their lives and preferences for digital experiences, which are different from the younger generation. Their interests are also diverse.

So, they study things like that through PersonaTIS, connect it to the appearance of a senior residence that suits it, connect household appliances, connect health data, and even connect life data outside. They are considering designing an experience where once you live here, you can't live anywhere else. When you interview them, they say it's not easy to burden their children, and they really want to make themselves happy with the money they have earned so hard at their age. So, they also wanted cultural experiences in the community, which was another reason they wanted to enter Silver Residences. They also wanted clear experiences. After entering this Silver Residence, there were also things to clearly see how much my health has improved. Some people think much more professionally than other active seniors, so to provide such personalized experiences, they also...

Data is also needed, and the connection with hospitals, connecting the devices they are using. Now, there are concerns about connecting these things inside the residence. You shouldn't just look at it. After the connection, what becomes important is that before smartphones existed, there were originally separate telephones, then separate cameras, and separate voice recorders. But after smartphones came into existence, the iPhone just took over everything. It devoured the entire app ecosystem and such, so everything is connected. Who will take the lead in this, it's uncertain. It could be the construction company, or it could be a battle for leadership. The battle for leadership has already begun. Alexa could take the lead, and they expressed their desire to take the lead in CS this time. So, BMW also decided to use their own thing, they also said that. Especially in cases like smart homes, whether electronic companies will take the lead.

Are you going to take it with you, or are you going to take Smart Things? No, it's not going to be Google or Amazon taking it. It seems like they will take the initiative this time in CES. And that initiative is not just about homes now; it's about connecting with cars. If you have a car and a home, of course, the company will have to use that too. Samsung, for example, also announced that they will connect everything up to the workspace, and other companies also made similar announcements. Even though one person will have everything, in the stage of taking the lead, attention to detail and customer experience is important, right? Because consumers will choose products from companies that delve deep into that area and know the pain points well. So, by creating a platform that has a lot of products that are widely chosen, and having many products themselves, in the end, who will eat it all up? Eventually, it's the one with active users.

Many people may be attracted to various experiences, so they can either get into smart dances or into live cues. What has created such an opportunity is that in the past, only people with Samsung products installed SmartThings. But now, it's not the case. Even connecting Huawei or other products, you can use SmartThings. That's why manufacturers used to think they would take the lead, but I thought it would never happen. They keep trying to keep that possibility open, so they make standards, they make meta, and they are in that state. Currently, what's more uncertain is the AI speaker market. Alexa or Amazon agents that started it might still be in the lead, but it gave a lot of disappointment because you never know how things will turn out there. It didn't feel so useful, so can eat everything on that platform.

I understand, but since it wasn't actually like that, what's important from now on. I think it's more important who creates the actual appealing contextual experience first. Just laying down the infrastructure, creating a platform, and leaving it open like this won't automatically connect everything. Now, no one comes. You mentioned a similar nuance before. It's true that analyzing data well and personalizing it in a contextual way is correct, but it's not just about collecting and using data recklessly. In the context of the DCX framework, what's called data-driven is not just about using data as before. What's different about analyzing customers based on data is that there have been efforts before. We did a lot. The problem is, if we think that the customer data we fight over will be very similar and analyze it diligently, then something...

I thought it would come out, but more than I thought, it's not coming out. And when signing up for membership, they ask for your age, ask for your address, and slowly, in a uniform and not very helpful way, they ask for things. That was helpful in the past because there were quite similar patterns and contexts depending on where people lived. So, by segmenting based on that and targeting and communicating, it worked. But now, in this era, it's different. In our school, everyone used to carry the same sports bag, and in a certain generation, Louis Vuitton bags were all the rage. But these days, those trends are not as common. In reality, if you try to create personas for digital generations, they are all very different. There are various patterns, behaviors, and contexts within them. So, what I emphasize at GCX is that when looking at big data, we need to consider things that we hadn't thought of before.

We talk about ways to utilize big data to find customer groups and discover contexts of customers that were not previously considered. In the past, those who dealt with big data only focused on volume. This side is huge. Targeting this color is enough. However, looking at that large segment, it's too general. Even without analyzing big data, you could have known it out of habit. It has been disappointing to see such results. So, even when analyzing, I make our students look at extreme contexts. Even though the world is not that big, recently, the segments that are growing have very unique contexts. Uniqueness is a way to solve problems for those customers. From there, we get hints to use as ingredients when designing innovative customer experiences. There were various traditional marketing methods for different customers in the past.

Surveys were good for confirming hypotheses we could think of. However, now when trying to find various hypotheses, there used to be traditional ways of confirming hypotheses that were a bit off. To try designing a truly innovative and appealing experience, we need to change our data sensing strategy to communicate experiences through line messages at the right timing for those customers by finding various contexts and diverse customers. Therefore, recently, companies attempting this ask very unique questions. In the past, they used to ask, What ability did you come to buy a refrigerator for? or Did you come to buy a double-door refrigerator? They used to ask product-centered questions, but salespeople now ask different questions. How many people live in your house? Do you mainly eat vegetarian food or regular food? Do you have children? The way of buying a refrigerator that gives contextually relevant experiences is now customer-centric.

Changing efforts, I initially ask good questions to customers for feedback, and by inputting that, I think of it as a sensing strategy for money data. Accumulating IoT sensor data in the cloud, without even considering what to do with it, as a data engineer, I simply looked at it from a data engineering perspective, such as how many times a button is pressed and how long it is used. I couldn't sense anything other than that, but that sensing strategy is also changing. Because, since 2018, for 45 years, I've been diligently collecting data, but I couldn't innovate based on that ancient experience. I couldn't innovate on the customer experience I wanted to provide because I just piled it up without a clear purpose. No matter how much I look at it, even if someday a brilliant data analyst appears and it seems like this will lead to something innovative, such a miracle does not happen. It hasn't happened yet, no, it can't happen in the future.

I mentioned that it's a waste of cloud costs and now is the time to collect data that is not economically meaningful from this product to establish a data-driven strategy for the experience. Context seems really important, and in the past, there were a lot of segments gathered, and later when I looked at it, it was the point we have always been at. But now, what we need to find forward was not there, suddenly a few things gathered, and when we analyze this, it can grow. Use the data for the purpose of discovering that later. We have become accustomed to finding contexts of customers that we have not thought of and may not even feel uncomfortable, but finding it as a problem through data and connecting it to the experience. I now think that innovation is finding problems.

I think it comes from the ability to find the customer's problem. There are so many problems in the world that can be solved just by finding the problem. There are too many methods as well. But there are not as many problems as you might think. That's why the ability to accurately identify the core issue is crucial. Data is definitely helpful in that. If I may ask a bit more, could you please talk about the DCX framework? From what you've mentioned about understanding the customer, finding the customer's context, and designing a comprehensive customer experience, it seems like the process of creating a sustainable process after designing a three-dimensional customer experience. I learned it this way. Can you briefly introduce what this is fundamentally? So far, I have mentioned that data is useful in finding unexpected customers and contexts. Could you explain briefly the role of data and artificial intelligence up to this point? That's all about using this material for experiences.

Who creates it? It's done by people. But in my experience with projects so far, I've only focused on the data and handed it over. If you can't design an engaging experience as expected, all the hard work you've done before becomes meaningless. So, I pondered on how to create an immersive digital experience and found some hints in integrated theory. I explain research results and project cases on how to provide our customers with a mental world, a clear physical experience, a cultural inner experience, and a connected experience to the external systematic world. This is the stage of comprehensive customer experience design. Lastly, conducting such comprehensive customer experience design does not end in one go because our customers are constantly evolving.

We need to re-collect how our customers feel and continuously improve their experience based on the collected results. Sometimes, we may need to conduct experimental designs like AB testing. Sustainable hospital design involves collecting data, improving experiences, and having processes within the business organization. However, a bigger issue that I have encountered is that the organization needs to change a bit. In order to work in an environment like this, not necessarily in large corporations, nor in organizations like Ajai, where finding and addressing problems is praised, we need to change to a culture where real innovation can occur centered around customers. Finally, I talked about how in organizations that have met a lot of these well-performing organizations, they have made data-driven decisions and achieved customer experience innovation, even if they are not large corporations.

No, especially startups are growing. I asked why here, where there is not a lot of data talent, even large companies struggle, how can online companies do better? The leaders said that way. Professor, we don't have many experienced and intuitive leaders. They are not confident in the decisions they make, so they are very scared. Therefore, we thoroughly continue to check and experiment based on data. Even Coupang, which has become a huge success now, did a lot of experiments with data. When I met them in the first half of the year, they said we did 30 million experiments. They often do experiments like comparing the actual click rate when the payment button is on the left versus on the right. And they are considering investing in rocket delivery.

In Gangnam area, there is an apartment complex divided into A region and B region. In the A region, the existing delivery system is in place, while in the B region, although not yet completed, most daily necessities are delivered directly. Observing that prices steadily rise after experiencing direct delivery of daily necessities, many people make investment decisions based on such observations. These aspects are deeply ingrained. I remember being very good at this, but it doesn't work well in large companies. Decision-making processes are not systematic, data silos are much more pronounced in large companies, and interdepartmental connections are not well established. Connecting all these takes a lot of time or processing time. However, this is also changing a lot in reality. As data organizations enter business units, they design pipelines for data processing and connections well, enabling people at the customer interface to make decisions and plan based on data.

It's starting to emerge. In the past, data organizations tried to achieve results on their own. We will try to solve all the problems of the organization, so let's interview and see what problems there are. Shall we try to solve the data? Let's prioritize and pick things we can achieve within this year. We worked hard and achieved results, but an organization like this cannot fundamentally succeed. Recently, creativity and problem-solving skills have become important. Connecting this with A and the ability to find problems is more hopeful for liberal arts majors who have read many books. So, even for professionals in the field, handling data is not as difficult as before. And when you actually try Python, it works well. You don't have to do everything from scratch. You can bring well-made things and adapt and use data that suits us. Don't be afraid. You can do it.

It can be done more easily like using a program tool. You gave us words that resonate with us. We are from the liberal arts [laughter]. Don't lose hope. Now that we have many students from the science department, our channel is more read by science students, right? It might be a strange question, but when you talked with Professor Choi before, you mentioned that data is ultimately from the past, right? In fact, the experiences of our elders or many experienced individuals are also from the past. So, when predicting the future based on the past or the experience of our elders, which one is more advantageous? When we have past data, we analyze it to create an AI. This is not just statistics, but AI. Therefore, even though it's based on the past, we can tune it to avoid overfitting. Depending on how we plan, the uncertainty of the future based on the past can be somewhat.

There are some ways to create a generalized model that can be predicted, and human intuition and experience may have a lot of things that we have never seen in the past. Therefore, rather than intuition and experience, it is difficult to say that it is good to have a problem that has not been in the past, but if you detect the ability to solve past problems and real-time incoming data together, it is much more difficult. I think that the quality is much higher when someone with rich intuition plans the data and makes decisions based on that data. You shouldn't leave it to the people who do the data because they haven't tried to solve that problem and don't know it well. Although experience and data are in the past, data has real-time data, so there is some on-site, and experience is.

Because it's something that people do, it involves people's judgment, creativity, and such, so I think those who are well versed in both data and human experience can find context well and design well. [MUSIC] Those who come up with difficult questions bring creativity. Sometimes they are quirky. Hey, there are those who think so creatively about making AI, to the extent of telling you to think really creatively. Friends like that seem to solve really difficult problems better. By tracking gaze with data in unexpected ways, comparing human attention and AI attention, and discussing things that eliminate AI bias, I came here today. Hey, this is something that engineering students really can't think of. Indeed, creativity is important for future talents. This is also connected. Originally, liberal arts were liberal arts, and natural sciences were natural sciences, but because of AI.

Liberal arts students are connected with science and engineering, and science and engineering students are also connected with liberal arts, so who knows who will dominate this, so is it okay to have healthy competition and promotion? We learn about artificial intelligence about 70%. We learn artificial intelligence technology and the remaining 30% is where we learn how to connect this AI to what type of problems in business. Our graduate school accepts liberal arts students, who must diligently and intensively learn AI. We need talents who can connect AI with business strategies, marketing strategies, risk management predictions, financial predictions, and such, because we need people who have hands-on experience in the field. Places that nurture such talents have not yet reached my business. Those who have not yet entered university or graduate school, please look into it once. And you may have heard today how important data is.

I hope you have felt how important it is to design customer experiences based on that foundation. It would be great if you could watch the lecture for more details. Thank you for giving us great teachings for a long time today, guiding us to find a ray of light in the midst of this complexity. I appreciate it. Will you come again as an MC next time? Yes, I think being an MC is more comfortable. Thank you so much for your kind words, and I will see you again. Thank you.

“뻔히 아는 걸 왜 빅데이터분석 하죠?” (차경진 교수)

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“뻔히 아는 걸 왜 빅데이터분석 하죠?” (차경진 교수)

“뻔히 아는 걸 왜 빅데이터분석 하죠?” (차경진 교수)

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