Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Customer Relationship Management Method Using Real-Time IoT Data

실시간 IoT 데이터를 활용한 고객 관계 관리 방안에 관한 연구

  • Bae, Ji Won (Graduate School of Management Consulting, Hanyang University) ;
  • Baek, Dong Hyun (Department of Business Administration, Hanyang University ERICA)
  • 배지원 (한양대학교 일반대학원 경영컨설팅학과) ;
  • 백동현 (한양대학교 경상대학 경영학부)
  • Received : 2019.05.16
  • Accepted : 2019.06.19
  • Published : 2019.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

As information technology advances, the penetration of smart devices connected to the Internet, such as smart phone and tablet PC, has rapidly expanded, and as sensor prices have fallen the Internet of Things has begun to be introduced in the industry. Today's industry is rapidly changing and evolving, requiring companies to respond to the new paradigm of business. In this situation, companies need to actively manage and maintain customer relationships in order to acquire loyal customers who bring them a high return. The purpose of this study is to suggest a method to manage customer relationship using real time IoT data including IoT product usage data, customer characteristics and transaction data. This study proposes a method of segmenting customers through RFM analysis and transition index analysis. In addition, a real-time monitoring through control charts is used to identify abnormalities in product use and suggest ways of differentiating marketing for each group. In the study, 44 samples were classified as 9 churn customers, 10 potential customers, and 25 active customers. This study suggested ways to induce active customers by providing after-sales benefit for product reuse to a group of churn customers and to promote the advantages or necessity of using the product by setting the goal of increasing the frequency of use to a group of potential customers. Finally, since the active customer group is a loyal customer, this study proposed an one-on-one marketing to improve product satisfaction.

Keywords

References

  1. Ban, J.O., Kim, K.H., and Kim, K.S., Architecture for Real-time Prediction Service of Time Series Sensor Data Utilizing Deep Learning In AWS IoT Environment, Journal of the Korean Entertainment Industry Association, 2017, Vol. 11, No. 8, pp. 347-353.
  2. Cheng, C.H. and Chen, Y.S., Classifying the Segmentation of Customer Value via RFM Model and RS Theory, Expert Systems with Applications, 2009, Vol. 36, No. 3, pp. 4176-4184. https://doi.org/10.1016/j.eswa.2008.04.003
  3. Chun, J.R., A Study of the Factors affecting the Implementation of HCRM, Journal of the Korea Academia-Industrial Cooperation Society, 2009, Vol. 10, No. 1, pp. 209-214. https://doi.org/10.5762/KAIS.2009.10.1.209
  4. Doishita, K., Muramoto, E., and Kouda, T., Application of ICT to Construction Machinery, Komatsu Technical Report, 2010, Vol. 56, No. 163, pp. 33-49.
  5. Hughes, A.M., Strategic Database-Marketing, second edition, McGraw-Hill, 2000-2004.
  6. Im, H., Implementation of IoT Sensor Data Flow Control System [Master], [Daejeon, Korea] : BaeJae University, 2016.
  7. Jeong, Y.J., Choi, I.Y., Kim, J.K., and Choi, J.C., Strategy for Store Management using SOM Based on RFM, Journal of Intelligence and Information Systems, 2015, Vol. 21, No. 2, pp. 93-112. https://doi.org/10.13088/jiis.2015.21.2.93
  8. Kim, J.M., eCRM Construction and Implementation Guide for e-Business Models, Fertilizer, 2000, pp. 124-126.
  9. Kim, S.Y., Song, J.Y., and Lee, G.S., A Study of Customer Churn by Analysing CRM Customer Data, Asia Marketing Journal, 2005, Vol. 7, No. 1, pp. 21-42.
  10. Lee, G.W., A Study on Improving the Quality of Express Delivery Service with Control Chart Technique [Master], [Busan, Seoul] : KyungSung University, 2012.
  11. Lee, Y.A., Analysis of Influencing Factors on Purchase Intention of IoT Products; Focusing the Moderating effect of Emotional Consumption value, [Master], [Asan, Seoul] : Hoseo University, 2017.
  12. Liu, R.Y., Control Charts for Multivariate Processes, Journal of the American Statistical Association, 1995, Vol. 90, No. 432, pp. 1380-1387. https://doi.org/10.1080/01621459.1995.10476643
  13. Park, K.H., Baek, D.H., Han, D.S., and Kim, H.S., A Study on Model for the Evaluation of Customer Composition in Internet Shopping Malls, Journal of Society of Korea Industrial and Systems Engineering, 2006, Vol. 29, No. 2, pp. 83-91.
  14. Park, K.H., Web services Framework for Loyal Customer Management based on RFM Models in Internet Retailing, Korea Intelligent Information System Society, 2002, Vol. 8, No. 1, pp. 41-63.
  15. Reichheld, F.F. and Sasser, W.E., Zero Defections : Quality Comes to Services, Harvard Business Review, 1990, Vol. 68, No. 5, pp. 105-111.
  16. Robert, S.W., Control Chart Tests Based on Geometric Moving Averages, Technometrics, 2000, Vol. 42, No. 1, pp. 97-101. https://doi.org/10.1080/00401706.2000.10485986
  17. Son, Y.S., Yeon, S.J., Lee, K.G., Lee, J.W., and Ha, J.A., Home IoT Market Analysis and Implication Point, National Information Society Agency, 2016.
  18. Szadziul, R. and Slowinski, B., Telematic System for Monitoring the Operation of Machines and Vehicles in a Transport-Equipment Enterprise, Diagnostyka, 2008, Vol. 4, No. 48, pp. 21-24.