Skip to main content

Study of Customer Value and Supplier Dependence with the RFM Model

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 331))

Abstract

In this study, data mining is applied to the use of an Enterprise Resource Planning (ERP) database to explore the core value of business operations by revising the RFM model into an RFGP model through “customer value analysis”. This model is used as a tool by enterprises to make proper adjustment to business strategies in a timely manner. By using an RFM model to “analyze dependence on suppliers”, enterprises can properly plan to reduce internal procurement costs and suppliers can properly manage their risks. With this two-prong focus on “profit maximization” and “cost reduction”, enterprises can maximize their profits. The findings of this study indicate that high-value customers are stable customers and accounted for 31 % of total customers. The statistics also show that customers of LED backlight modules constituted 18 % of the total and were the primary source of profit for the enterprises. However, there remains much uncertainty in the LED lighting market and the rate of return was low. With limited operational resources, enterprises should explore technologies related to backlight products, purchase automation equipment, train professionals in related disciplines and seek to enhance their overall competitive power. Suppliers of key materials are suppliers of high dependence, accounting for 11 % of the total and including suppliers of heavy reliance and exclusive suppliers. Enterprises are strongly advised to establish new sources of supplies to ensure a proper procurement balance and reduce materials supply related risks. In addition, enterprises should take the top 10 key materials suppliers as targets for price reductions in order to cut costs and maximize profits. Enterprises are also strongly advised to invest in the suppliers of key materials. The recycled use of enterprise resources could feedback to the enterprises themselves and helps to maximize enterprise profits.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Huang H-H (2012) The application of data mining to customer value analysis in the food industry. Thesis, Southern Taiwan University of science and Technology, Department of Business Administration

    Google Scholar 

  2. Cheng C-H, Chen Y-S (2009) Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst Appl 36(3):4176–4184

    Article  Google Scholar 

  3. Lo C-C (2011) Research on the application of data mining of bookstore customer relationship management. Thesis, National Chin-Yi University of Technology, Department of Distribution Management

    Google Scholar 

  4. Huang J, Zhou C, Han W (2013) Assessing competitive advantage based on customer satisfaction and customer value, pp 12–17

    Google Scholar 

  5. Atmani B, Beldjilali B (2012) Knowledge discovery in database: induction graph and cellular automaton. Comput Inf 26(2):171–197

    Google Scholar 

  6. Kaas Q, Yu R, Jin A-H, Dutertre S, Craik DJ (2012) ConoServer: updated content, knowledge, and discovery tools in the conopeptide database. Nucleic Acids Res 40(D1):D325–D330

    Article  Google Scholar 

  7. Ting I-H (2005) Data mining

    Google Scholar 

  8. Frawley WJ, Piatetsky-Shapiro G, Matheus CJ (1992) Knowledge discovery in databases: an overview. AI Mag 13(3):57

    Google Scholar 

  9. Grupe FH, Mehdi Owrang M (1995) DATA BASE MINING discovering new knowledge and competitive advantage. Inf Syst Manag 12(4):26–31

    Article  Google Scholar 

  10. Fayyad UM, Piatetsky-Shapiro G, and Smyth P “Knowledge discovery and data mining: towards a unifying framework.” pp. 82-88

    Google Scholar 

  11. Cabena P (1998) Discovering data mining: from concept to implementation. Prentice Hall PTR

    Google Scholar 

  12. Linoff GS, Berry MJ (2011) Data mining techniques: for marketing, sales, and customer relationship management. Wiley, Chichester

    Google Scholar 

  13. Hughes AM (2005) Strategic database marketing. McGraw-Hill Publishing Co., New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jui-Hung Kao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media Dordrecht

About this paper

Cite this paper

Kao, JH., Lai, F., Liaw, HT., Hsieh, Ph. (2015). Study of Customer Value and Supplier Dependence with the RFM Model. In: Park, J., Pan, Y., Chao, HC., Yi, G. (eds) Ubiquitous Computing Application and Wireless Sensor. Lecture Notes in Electrical Engineering, vol 331. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9618-7_27

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-9618-7_27

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-9617-0

  • Online ISBN: 978-94-017-9618-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics