Skip to main content

Visual Mining for Customer Targeting

  • Conference paper
Web Technologies Research and Development - APWeb 2005 (APWeb 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3399))

Included in the following conference series:

  • 416 Accesses

Abstract

In this paper, we propose the customer map – the information visualization method for customer targeting. To develop the customer map, we classify customer data into customer needs, customer characteristics, and customer value. We suggest an analysis framework to derive key dimensions of the customer map by data mining techniques and a network mapping method to detect meaningful combinations of key dimensions. The customer map is built visually in terms of these key dimensions. The proposed visual targeting model helps a decision maker to build customer-oriented strategies and offers them the ability to monitor and perceive the real time state of customer value distribution based on their information without preconception. We apply the visual targeting model to a credit card company, and acquire managerial implications from this study.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Weinstein, A.: Market Segmentation Revised Edition. Probus Publishing Company (1994)

    Google Scholar 

  2. Park, C.-H., Kim, Y.-G.: A Framework of Dynamic CRM: Linking Marketing with Information Strategy. Business Process Management Journal 9(5), 652–671 (2003)

    Article  Google Scholar 

  3. Westphal, C., Blaxton, T.: Data Mining Solution, pp. 123–147. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  4. Mulhern, F.J.: Customer Profitability and Diagnosing a Customer Portfolio. In: Kellogg on Integrated Marketing. John Wiley &Sons, Inc, Chichester (2003)

    Google Scholar 

  5. Kohonen, T., Deboeck, G.: Visual Exploration in Finance with Self-Organizing Maps. Springer, Heidelberg (1998)

    Google Scholar 

  6. Mitchell, V.-W.: How to Identify Psychographic Segments: Part1, Part2. Marketing Intelligence & Planning 12(7) (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Woo, J.Y., Bae, S.M., Pyon, C.U., Choi, M.S., Park, S.C. (2005). Visual Mining for Customer Targeting. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds) Web Technologies Research and Development - APWeb 2005. APWeb 2005. Lecture Notes in Computer Science, vol 3399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31849-1_94

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-31849-1_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25207-8

  • Online ISBN: 978-3-540-31849-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics