• Journal of Distribution Science
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• v.10 no.5
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• pp.37-44
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• 2012

In this paper, we investigate several factors that affect the locational decision of discount stores by using previous studies on the marketing area and the location of commercial facilities. We selected 21 primary variables that are expected to influence the decision of store location and, by factor analysis, grouped them into five underlying factors. Among these, the demographic factor, which shows the potential purchasing power level, had the greatest impact on the locational decision for the store. However, we found individual stores positioned according to unique locational characteristics in addition to the demographic factor. It means that we have to additionally consider if the vicinity of the market is based on any physical properties. Many previous studies proposed four decision factors for store location: the economic factor, the demographic factor, the land utilization factor, and traffic factor. However, the fivefold factors-our distinctive contribution-are more concrete and persuasive according to Korean reality. We show that location preference is based on the following criteria: (1) the area is densely populated, (2) houses stand close together, (3) residents have a high income level, (4) road traffic is developed and easy to access, and (5) public transportation is well developed. The demographic factor has the greatest impact on the location of a discount store. The number of households has a greater relevance to the demographic factor than does the individual consumer. Second, discount stores relatively prefer places where houses are located close together because such places offer easy access to the market. Third, a place whose residents have a high income level will be preferred, with its large cars and excellent traffic conditions. Fourth, a location would be highly rated if the roads around commercial facilities are well developed and their accessibility is good. Finally, discount stores must be located close to bus stops because female consumers, including housewives-the most important customers-evaluate stores based on distance. In this research, the variable of consumer attitude and preference was excluded, and the location factors of discount stores were analyzed according to a microscopic view through physical spatial data. In the future, the opening of new discount stores based on the five factors indicated above will require a comparatively shorter time from the first project feasibility analysis. In addition, the result of our study can be applied to the field of public policy for constructing and attracting large-scale distribution facilities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.19-38
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• 2019

This study is a study on the effect of patent management on the performance of new business development, focusing on fusion expert collaboration. In the past, most studies on patent management have been influenced by the quantitative patent index on the business performance. Therefore, research on the effect of patent management on the performance of new business development through the cooperation of fusion experts was very insufficient. Therefore, this study examined the influence of existing patent management on the performance of new business development and the causal relationship between the influence of patent management on new business development performance, focusing on fusion expert collaboration. The results of the hypothesis empirical analysis are as follows. First, patent management showed positive (+) influence on convergence expert cooperation. Patents management has a positive effect on fostering convergence specialists and utilizing convergence experts. Second, patent management has a positive effect on new business development performance. Patent management has a positive effect on the success of the business, the achievement of target sales, the development of new markets, the development of new technologies, and the degree of reflection of customer requirements. Third, patent management mediated by convergence expert cooperation has a negative effect on financial aptitude among new business development outcomes. The results of this study are as follows. First, it is concluded that patent management through mediation of convergence expert cooperation has a positive effect on non - financial performance of new business development performance. Financial performance includes business success and achievement of target sales. Non-financial performance includes new technology development and new market development. Therefore, in order to continuously generate business performance of domestic convergence new business development companies, it suggests that we should make efforts to be linked with new business development performance through revitalization of patent management centered on convergence expert cooperation that has positive (+) influence.

• Korea Science and Art Forum
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• v.25
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• pp.103-118
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• 2016

Due to the rapid development of ICT, corporate business models quickly changed and because of the radical growth of IT technology, sequential or gradual survival has become difficult. Internet-based new businesses such as IT service companies are seeking for new convergence business models that have not existed before to create business models that are more competitive, but the economic efficiency of business models that were successful in the past is wearing off. Yet, as reaching the critical point where the platform value becomes extremely high for platforms via the Internet is happening at a much higher speed than before, platform-ization has become a very important condition for rapid business expansion for all kinds of businesses. This study analyzes the necessity of establishing platform business models in IT service planning and identifies their characteristics through case analyses of platform business models. The results derived features First, there is a need to ensure sufficient buyers and sellers, and second, platform business model should provide customers with distinctive value of the only platforms are generating. third, the common interests between platform-driven company and a partner, participants Should be existing. Fourthly, by expanding base of participants and upgrades, expansion of adjacent areas we must have a continuous scalability and evolution must be sustainable. While it is expected that the identified characteristics will cause tremendous impacts to the establishment of platform business models and to the graphing of service planning, we also look forward to this study serving as the starting point for the establishment of theories of profit models for platform businesses, which were not mentioned in the study, so that planners responsible for platform-based IT service planning will spend less time and draw bigger schemes in building planning drafts.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.1-31
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• 2008

Trust is central to human relationships, at all times and places. The importance of trust is fundamental in all areas of human life, not only in the area of business administration. 2,500 years ago in China, Confucius taught that the foundation of politics was the trust of the people, more important even than military strength or the supply of food. Shakespeare's play, "Much Ado about Nothing' is about trust and deception. These days, trust and transparency in a commercial organization's business culture form the basis of the 'social capital' by which that organization increases its productivity. A successful company raises productivity by the accumulation of social capital, derived from a trust relationship between business partners, and between the company and consumers. Trust is the crucial factor. At the national level, building trust determines a nation's competitiveness. For a company, long term trust relationships with customers are essential for its survival in a business environment of rapid change. Such relationships, based on trust, are important assets to ensure a company's competitive advantage, and need to be organic to that company's business culture. Because of this importance, trust relationships have been studied in diverse areas within business administration, and especially within marketing, where they form the basis of a successful relationship between producer and consumer. However, what has been lacking is a unified definition of trust. Research has been conducted on the basis of various definitions and models. The majority of researchers have not considered the multidimensional character of the concept of trust until now. Approaches based on a one dimensional model have undermined the value of research results. Furthermore, researchers have only considered trust and trustworthiness as a single component. The majority of research has explored the consequences of perceived trust for outcomes such as loyalty or cooperation, but has neglected the effects of trustworthiness upon the mechanisms of consumer trust. This study focuses on the dimension of trust from such a perspective. It seeks to verify the effect of trust on customer intentions by breaking it down into three separate components: 1) the salesperson, 2) the product/service, and 3) the company. The purposes of this paper are as follows: Firstly, we review the multidimensional nature of trust objects: the salesperson, the product/service, and the company. Secondly, we analyze the relationship between multidimensional trust and trustworthiness. Thirdly, we analyze the connection between trust and repeat purchase intentions for the maintenance of long term relationships. For these purposes the author has developed several hypotheses as follows: H1-1: The competence of a salesperson is positively associated with the trust given by the consumer to the salesperson. H1-2: The benevolence of a salesperson is positively associated with the trust given by the consumer to the salesperson. H2-1: The competence of product/service is positively associated with the trust given by the consumer to the product/service. H2-2: The benevolence of product/service is positively associated with the trust given by the consumer to the product/service. H3-1: The reputation of a company is positively associated with the trust given by the consumer to the company. H3-2: The physical environment of a company is positively associated with the trust given by the consumer to the company. H4-1: Trust in a salesperson is positively associated with repeat purchase intentions. H4-2: Trust in a product/service is positively associated with repeat purchase intentions. H4-3: Trust in a company is positively associated with repeat purchase intentions. The data was compiled from 366 questionnaires. 500 questionnaires were collected, but some of the data was considered unsuitable and inappropriate. The subjects of the survey were male and female customers purchasing products at department stores in Seoul, Daegu and Gyeongbuk. It was carried out between Oct. 25 and 29, 2007. The data was analyzed by frequency analysis using SPSS 12.0 and structural equation modeling using LISREL 8.7. The result of the overall model analysis is as follows: Chi-Square=445.497, d.f.=185, p-value=0.0, GFI=.901, RMSEA=.0617, NNFI=.986, NFI=.981, CFI=.989, AGFI=.864, RMR=.0872. The results of the overall model analysis were coherent. It was found that trust is a multi-dimensional construct, that each of the dimensions of trust are meaningful influences on customer's repurchase intention. Trust in a company may be the most relevant, while trust in a product/service and a salesperson may be less relevant to repurchase intentions. The effective factors in determining trust in a salesperson and a company's product/service were found to be competence and benevolence. Factors in determining trust in a company were its reputation and physical environment, and the relationship of each effective trust factor has been verified in this research. As a result, it was found that competence and benevolence have a meaningful influence on trust in a salesperson and in product/service. It was also found that a company's reputation influences the overall trust in the company significantly but a company's physical environment does not have much effect.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.45-67
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• 2017

According to the Internet Usage Research performed in 2016, the number of internet users and the internet usage have been increasing. Smartphone, compared to the computer, is taking a more dominant role as an internet access device. As the number of smart devices have been increasing, some views that the demand on high-speed internet will decrease; however, Despite the increase in smart devices, the high-speed Internet market is expected to slightly increase for a while due to the speedup of Giga Internet and the growth of the IoT market. As the broadband Internet market saturates, telecom operators are over-competing to win new customers, but if they know the cause of customer exit, it is expected to reduce marketing costs by more effective marketing. In this study, we analyzed the relationship between the cancellation rates of telecommunication products and the factors affecting them by combining the data of 3 cities, Anyang, Gunpo, and Uiwang owned by a telecommunication company with the regional data from KOSIS(Korean Statistical Information Service). Especially, we focused on the assumption that the neighboring areas affect the distribution of the cancellation rates by coupling type, so we conducted spatial cluster analysis on the 3 types of cancellation rates of each region using the spatial analysis tool, SatScan, and analyzed the various relationships between the cancellation rates and the regional data. In the analysis phase, we first summarized the characteristics of the clusters derived by combining spatial information and the cancellation data. Next, based on the results of the cluster analysis, Variance analysis, Correlation analysis, and regression analysis were used to analyze the relationship between the cancellation rates data and regional data. Based on the results of analysis, we proposed appropriate marketing methods according to the region. Unlike previous studies on regional characteristics analysis, In this study has academic differentiation in that it performs clustering based on spatial information so that the regions with similar cancellation types on adjacent regions. In addition, there have been few studies considering the regional characteristics in the previous study on the determinants of subscription to high-speed Internet services, In this study, we tried to analyze the relationship between the clusters and the regional characteristics data, assuming that there are different factors depending on the region. In this study, we tried to get more efficient marketing method considering the characteristics of each region in the new subscription and customer management in high-speed internet. As a result of analysis of variance, it was confirmed that there were significant differences in regional characteristics among the clusters, Correlation analysis shows that there is a stronger correlation the clusters than all region. and Regression analysis was used to analyze the relationship between the cancellation rate and the regional characteristics. As a result, we found that there is a difference in the cancellation rate depending on the regional characteristics, and it is possible to target differentiated marketing each region. As the biggest limitation of this study and it was difficult to obtain enough data to carry out the analyze. In particular, it is difficult to find the variables that represent the regional characteristics in the Dong unit. In other words, most of the data was disclosed to the city rather than the Dong unit, so it was limited to analyze it in detail. The data such as income, card usage information and telecommunications company policies or characteristics that could affect its cause are not available at that time. The most urgent part for a more sophisticated analysis is to obtain the Dong unit data for the regional characteristics. Direction of the next studies be target marketing based on the results. It is also meaningful to analyze the effect of marketing by comparing and analyzing the difference of results before and after target marketing. It is also effective to use clusters based on new subscription data as well as cancellation data.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.35-55
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• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.