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Development of Beauty Experience Pattern Map Based on Consumer Emotions: Focusing on Cosmetics

소비자 감성 기반 뷰티 경험 패턴 맵 개발: 화장품을 중심으로

  • Seo, Bong-Goon (Graduate School of Business IT, Kookmin University) ;
  • Kim, Keon-Woo (Graduate School of Business IT, Kookmin University) ;
  • Park, Do-Hyung (School of Management Information Systems, Kookmin University)
  • 서봉군 (국민대학교 비즈니스 IT 전문대학원) ;
  • 김건우 (국민대학교 비즈니스 IT 전문대학원) ;
  • 박도형 (국민대학교 경영정보학부/비즈니스 IT 전문대학원)
  • Received : 2019.03.11
  • Accepted : 2019.03.28
  • Published : 2019.03.31

Abstract

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.

최근의 '똑똑한 소비자(Smart Consumer)'라 불리는 소비자가 많아지고 있는데, 이들은 제조사나 광고를 통해 전달되는 정보에 의존하지 않고, 기존 사용자나 전문가들의 후기, 여러 과학 지식을 획득하여 제품에 대한 이해를 높이고, 본인 스스로가 직접 판단하여 구매하고 있다. 특히나 화장품 분야는 인체 유해성과 같은 부정적인 요소에 대한 민감도가 높고, 자신의 고유한 피부 특성과의 조화도 고려되어야 하기 때문에, 전문적인 지식과 타인의 경험, 본인의 과거 경험 등을 종합적으로 생각하여 구매 의사결정을 내려야 하고, 이에 대해서 적극적인 소비자가 많아지고 있다. 이러한 움직임은 '셀프 뷰티' 와 같은 '셀프' 문화의 열풍과 함께, 문화 현상인 '그루밍족'의 등장, 사회적 트렌드인 'K-뷰티' 와도 동행한다고 할 수 있다. 맞춤형 화장품에 대한 관심의 급부상도 이러한 현상 중 하나라 볼 수 있다. 소비자들의 맞춤형 화장품의 니즈를 충족시키기 위해, 화장품 제조사나 관련 기업들은 ICT기술과의 융합을 통하여 프리미엄 서비스를 중심으로 소비자의 니즈에 대응하고 있다. 그러나 기업 및 시장 현황이 맞춤형 화장품을 향해 진화하고 있지만, 소비자의 피부 상태, 추구하는 감성, 실제 제품이나 서비스까지 소비자 경험을 전체적으로 완전하게 다루는 지능형 데이터 플랫폼은 부재한다. 본 연구에서는 소비자 경험에 대한 지능형 데이터 플랫폼 구축을 위한 첫 단계로 소비자 언어 기반의 화장품 감성 분석을 수행하였다. 소비자들 개인의 선호나 취향이 분명한 앰플/세럼 카테고리를 중심으로 매출 순위 1위에서 99위까지의 99개 제품을 선정하여, 블로그와 트위터 등의 SNS 상에 언급되는 후기 내에 화장품 경험에 대한 소비자 감성을 수집하였다. 총 357개의 감성 형용사를 수집하였고, 고객 여정 워크샵을 통해 유사 감성을 합치고, 중복 감성을 통합하는 작업을 수행하였으며, 최종 76개 형용사를 구축했다. 구축한 형용사에 대한 SOM 분석을 통해 화장품에 대한 소비자 감성에 대한 클러스터링을 실시했다. 분석 결과, 총 8개의 클러스터를 도출했고, 클러스터 별 각 노드의 벡터 값을 기준으로 소비자 감성 Top 10을 도출했다. 소비자 감성을 기준으로 클러스터별 소비자 감성에 서로 다른 특징이 발견됐으며, 소비자에 따라 다른 소비자의 감성을 선호, 기존과는 다른 소비자 감성을 고려한 추천 및 분류 체계가 필요함을 확인했다. 연구 결과를 통해 감성 분석의 활용 도메인이 화장품만이 아닌 다양한 영역으로 확장될 수 있음 확인했으며, 감성 분석을 통한 소비자 인사이트를 도출할 수 있다는 점을 시사했다. 또한, 본 연구에서 활용한 디자인 씽킹(Design Thinking)의 방법론의 적용하여 화장품 특화된 감성 사전을 과학적인 프로세스로 구축했으며, 화장품에 대한 소비자의 인지 및 심리에 대한 이해를 도울 수 있을 것으로 기대한다.

Keywords

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맞춤형 화장품에 대한 기존의 제품 및 서비스

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Consumer Experience

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Research Process

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Boundary Depending on Number of Clusters

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Consumer Sentiment Pattern Map (Left: Counts on Nodes, Right: Node Characteristics)

Characteristics of Final Clusters

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TOP10 Consumer Sentiment by Cluster

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References

  1. Bagozzi, R. P., M. Gopinath, and P. U. Nyer, "The Role of Emotions in Marketing," Journal of the Academy of Marketing Science, Vol. 27, No. 2(1999), 184-206. https://doi.org/10.1177/0092070399272005
  2. Berardesca, E., and H. Maibach, "Racial Differences in Skin Pathophysiology," Journal of the American Academy of Dermatology, Vol. 34, No. 4(1996), 667-672. https://doi.org/10.1016/S0190-9622(96)80070-3
  3. Chaudhuri, A., Emotion and Reason in Consumer Behaviour, Amsterdam, the Netherlands: Elsevier Verlag, 2006
  4. Curry, B., F. Davies, , M. Evans, , L. Moutinho, and P. Phillips, "The Kohonen Self-Organising Map as an Alternative to Cluster Analysis: An Application to Direct Marketing," International Journal of Market Research, Vol. 45, No. 2(2003), 1-20.
  5. Han, J., J. Pei, and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2011.
  6. Hwang, Y. "A Hybrid Forecasting Framework based on Case-based Reasoning and Artificial Neural Network," Journal of Intelligence and Information Systems, Vol. 18, No. 4(2012), 43-57. https://doi.org/10.13088/JIIS.2012.18.4.043
  7. Holbrook, M. B., and J. O'Shaughnessy, "The Role of Emotion in Advertising," Psychology and Marketing, Vol. 1, No. 2(1984), 45-64. https://doi.org/10.1002/mar.4220010206
  8. Johnson, L. C., and N. L. Corah, "Racial Differences in Skin Resistance," Science, Vol. 139, No. 3556(1963), 766-767. https://doi.org/10.1126/science.139.3556.766
  9. Kang T., and D.-H. Park, "The Effect of Expert Reviews on Consumer Product Evaluations: A Text Mining Approach," Journal of Intelligence and Information Systems, Vol. 22, No. 1(2016) 63-82. https://doi.org/10.13088/jiis.2016.22.1.063
  10. Kohonen, T. Self-Organizing Maps, Springer, New York, 1995.
  11. Kim, K.-W., and D.-H. Park, "Emoticon by Emotions: The Development of an Emoticon Recommendation System Based on Consumer Emotions," Journal of Intelligence and Information Systems, Vol. 24, No. 1(2018), 227-252. https://doi.org/10.13088/JIIS.2018.24.1.227
  12. Kim, Y., and D.-H. Park, "A Study on the Consumers' Knowledge Structure of Innovative Products through Product Category Concept Map: Focusing on 3D and Smart TV," Entrue Journal of Information Technology, Vol. 12, No. 3(2013), 181-197.
  13. Lee S. J., B.-G. Seo, and D.-H. Park, "Development of Music Recommendation System based on Customer Sentiment Analysis," Journal of Intelligence and Information Systems, Vol. 24, No. 4(2018), 197-217. https://doi.org/10.13088/JIIS.2018.24.4.197
  14. Lemon, K. N., and P. C. Verhoef, "Understanding customer experience throughout the customer journey," Journal of Marketing, Vol. 80, No. 6(2016), 69-96. https://doi.org/10.1509/jm.15.0420
  15. Na, J., H. Jun, Y. Chen, H. Choi, and D.-H. Park, "The Development and Practice of Design Thinking Methodology Based on Gamification: Focusing on University Loyalty Program," Journal of Information Technology Services, Vol. 15, No. 2(2016), 65-80. https://doi.org/10.9716/KITS.2016.15.2.065
  16. Park, D.-H., J. Chung, Y.-J. Chung, and D. Lee, "Development of Market Growth Pattern Map Based on Growth Model and Self-organizing Map Algorithm: Focusing on ICT products," Journal of Intelligence and Information Systems, Vol. 20, No. 4(2014), 1-23. https://doi.org/10.13088/JIIS.2014.20.4.01
  17. Shaw, M. J., C. Subramaniam, G. W. Tan, and M. E. Welge "Knowledge Management and Data Mining for Marketing," Decision Support Systems, Vol. 31, No. 1(2001), 127-137. https://doi.org/10.1016/S0167-9236(00)00123-8
  18. Sheth, J. N., B. Mittal, and B. I. Newman, B. I., Consumer Behavior and Beyond, NY: Harcourt Brace, 1999.
  19. Wesley, N. O., and H. I. Maibach, "Racial (ethnic) Differences in Skin Properties," American Journal of Clinical Dermatology, Vol. 4, No. 12(2003), 843-860. https://doi.org/10.2165/00128071-200304120-00004
  20. Westbrook, R. A., and R. L. Oliver, "The Dimensionality of Consumption Emotion Patterns and Consumer Satisfaction," Journal of Consumer Research, Vol. 18, No. 1(1991), 84-91. https://doi.org/10.1086/209243
  21. Yoo, I.-J., B.-G. Seo, and D.-H. Park, "Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments," Journal of Intelligence and Information Systems, Vol. 24, No. 1(2018), 25-52. https://doi.org/10.13088/JIIS.2018.24.1.025

Cited by

  1. 웹툰 콘텐츠 추천을 위한 소비자 감성 패턴 맵 개발 vol.25, pp.4, 2019, https://doi.org/10.13088/jiis.2019.25.4.067