유통업 Big Data를 이용한 고객 행태 분석에 대한 연구 = study on the customer behavior analysis using Big data from Distribution industry

국문 초록 (Abstract)

IT 산업과 데이터 저장 기술의 발전은 대규모 데이터에 대한 생성과 수집을 가능하게 하였고 이를 활용한 다양한 통계 분석은 관심 현상에 대한 실시간 의사 결정을 가능하게 하였다. 이러한 추세는 사회, 경제, 정치, 문화 등의 분야로 확대되고 있다. 최근 유통업에서도 역시 다양한 시장과 채널에서 수집되는 고객 관련 데이터를 이용하여 고객 중심의 제품/서비스를 제공하기 위하여 노력하고 있다. 이러한 노력 중의 하나로 가맹점의 고객 멤버십 그리고/또는 다양한 상품 군에 대한 거래관련 Big Data를 기업 경영에 적용하고자 시도 하고 있다. 본 논문은 기존의 고객관계 경영(Customer Relationship Management)과 이러한 Big Data 분석의 기초가 되는 새로운 고객 세분화 기준과 고객 정보 활용 프로세스에 대하여 고찰해보고자 한다. 이를 위하여 소매 유통업의 고객/거래 관련 데이터의 수집 구조를 파악하고 단일 고객 관점(Single Customer View)에서의 고객 지표의 생성, 이를 이용한 고객 세분화 방법과 적용 가능한 통계 분석을 제안해 보고자 한다.

다국어 초록 (Multilingual Abstract)

The advancement of diverse industries and improvement of data storage technology enabled creation and collection of data on large scale. The trend presents new standard of real time decision making centered on statistical analysis. The trend is applied extensively in order to properly explain and predict multifaceted phenomena in areas of politics, social studies, economics, and culture studies. Today’s distribution industry spends significant efforts in collecting customer data through various channels in order to identify customer preferences and provide customized products and services. Understanding and analyzing membership and/or transaction data of individual customer will yield meaningful customer data that can be applied to management practices of organizations. This thesis studies the utility of the Big Data, innovative tool based on Customer Relationship Management, in order to maintain proper collection and analysis of customer data which is fundamental to substantiality of organizations. For the purpose listed above, the thesis prepares steps to comprehend customer/transaction related data collection structure in distribution industry in order to create customer index focused on Single Customer View. New customer index will be used in systemic statistical analysis and customer segmentation. Suggested method is applied to the 30TB of G distribution industry data collected from multiple channels within specific period of 2016. The result is presented alongside the method.

목차 (Table of Contents)

• I. 서론 II. 고객 유형의 분류 III. 고객 분류별 고객 발전 단계 IV. Big Data를 활용한 고객 세분화 V. 사례 VI. 결론 및 향후 연구과제 참고문헌

참고문헌 (Reference)

1 최정환, "죽은 CRM 살아있는 CRM" 한언 2001

2 이병엽, "자동차산업의 고객분류 및 타겟 마케팅 모델" 한국콘텐츠학회 9 (9): 313-322, 2009

3 서현지, "빅 데이터를 이용한 고객 행태 분석에 대한 연구" 가천대학교 일반대학원 2017

4 채서일, "마케팅" 학현사 1993

5 Hodgson, D., "“Know your customer”: marketing, governmentality and the “new consumer” of financial services" 2002

6 Akman, P., "‘Consumer’versus ‘Customer’: the Devil in the Detail" 37 (37): 315-344, 2010

7 Vij, A., "When is big data big enough? Implications of using GPS-based surveys for travel demand analysis" 56 : 446-462, 2015

8 Bell, S. J., "When customers disappoint : A model of relational internal marketing and customer complaints" 32 (32): 112-126, 2004

9 Lindgreen, A., "Value in business and industrial marketing : Past, present, and future" 41 (41): 207-214, 2012

10 Pan, S. L., "Using e-CRM for a unified view of the customer" 46 (46): 95-99, 2003

11 Ziafat, H., "Using data mining techniques in customer segmentation" 4 (4): 70-79, 2014

12 Gallego, G., "Upgrades, upsells and pricing in revenue management"

13 Rothman, M. J., "U.S. Patent No. 6,505,168"

14 Jaworski, B., "The service-dominant logic of marketing" Routledge 127-135, 2014

15 Zeithaml, V. A., "The customer pyramid : creating and serving profitable customers" 43 (43): 118-142, 2001

16 Hunter, M., "The 5 types of customers. About money"

17 Espinoza, M., "Short-term load forecasting, profile identification, and customer segmentation : a methodology based on periodic time series" 20 (20): 1622-1630, 2005

18 Lin, C. F., "Segmenting customer brand preference: demographic or psychographic" 2002

19 McCarty, J. A., "Segmentation approaches in data-mining : A comparison of RFM, CHAID, and logistic regression" 60 (60): 656-662, 2007

20 Levitt, T., "Relationship management" 61 (61): 87-93, 1983

21 Hu, M., "Mining and summarizing customer reviews" 168-177, 2004

22 Anderson, M. W., "Method and system for gathering and analyzing consumer purchasing information based on product and consumer clustering relationships. U.S. Patent No. 5,974,396"

23 Kara, A., "Markets of a single customer: exploiting conceptual developments in market segmentation" 1997

24 Dibb, S., "Marketing theory: A student text" 251-279, 2016

25 Wedel, M., "Marketing analytics for data-rich environments" 80 (80): 97-121, 2016

26 Shah, D., "Managing customer profits : The power of habits" 51 (51): 726-741, 2014

27 Fletcher, K., "Issues in customer information management" 38 (38): 1-15, 1996

28 Raffel, Keith, "Graphical user interface for customer information management. U.S. Patent No. 6,169,534"

29 Fotaki, G., "Exploring big data opportunities for Online Customer Segmentation" 5 (5): 58-75, 2014

30 Bolton, R. N., "Expanding business-to-business customer relationships : Modeling the customer's upgrade decision" 72 (72): 46-64, 2008

31 Jun, M., "Examining the relationships between internal service quality and its dimensions, and internal customer satisfaction" 21 (21): 205-223, 2010

32 Stahl, M. J, "Encyclopedia of health care management" Sage Publications 2003

33 Xu, Z., "Effects of big data analytics and traditional marketing analytics on new product success : A knowledge fusion perspective" 69 (69): 1562-1566, 2016

34 Payne, A., "Diagnosing customer value : integrating the value process and relationship marketing" 12 (12): 159-182, 2001

35 Tsiptsis, K. K., "Data mining techniques in CRM: inside customer segmentation" John Wiley & Sons 2011

36 Chen, D., "Data mining for the online retail industry : A case study of RFM model-based customer segmentation using data mining" 19 (19): 197-208, 2012

37 Kang, J., "Customer–company identification and the effectiveness of loyalty programs" 68 (68): 464-471, 2015

38 Keaveney, S. M., "Customer switching behavior in online services : An exploratory study of the role of selected attitudinal, behavioral, and demographic factors" 29 (29): 374-390, 2001

39 Kendall, S. D., "Customer service delivery: Research and best practices" 3-21, 2006

40 Weinstein, A., "Customer retention: A usage segmentation and customer value approach" 10 (10): 259-268, 2002

41 Symons, M. J., "Customer relationship management (crm) systems. U.S. Patent Application No. 14/192,692"

42 Lengnick-Hall, C. A., "Customer contributions to quality: A different view of the customer-oriented firm" 21 (21): 791-824, 1996

43 Schmittlein, D. C., "Customer base analysis : An industrial purchase process application" 13 (13): 41-67, 1994

44 Radcliffe, J., "Creating the single customer view with customer data integration" Gartner Research 2006

45 Hosseini, S. M. S., "Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty" 37 (37): 5259-5264, 2010

46 Anderson, H., "Can you buy a business relationship? : On the importance of customer and supplier relationships in acquisitions" 30 (30): 575-586, 2001

47 Shelton, T., "Business models for the social mobile cloud:Transform your business using social media, mobile internet, and cloud computing" John Wiley & Sons 2013

48 Wang, Y., "Big data analytics : Understanding its capabilities and potential benefits for healthcare organizations" 126 : 3-13, 2018

49 Erevelles, S., "Big Data consumer analytics and the transformation of marketing" 69 (69): 897-904, 2016

50 Erevelles, S., "Big Data consumer analytics and the transformation of marketing" 69 (69): 897-904, 2016

51 Conduit, J., "Balancing the act:the implications of jointly pursuing internal customer orientation and external customer orientation" 30 (30): 1320-1352, 2014

52 Ikeda, H., "Additional facilities of a concentional DBMS to support interactive statistical analysis" 25-36, 1981

53 Winer, R. S., "A framework for customer relationship management" 43 (43): 89-105, 2001

54 Ester, M., "A density-based algorithm for discovering clusters in large spatial databases with noise" 96 (96): 226-231, 1996

55 고승곤, "(프로젝트 기반의) 통계적 문제 해결"