Skip to main content
SpringerLink
Log in
Book cover

CCF Conference on Computer Supported Cooperative Work and Social Computing

ChineseCSCW 2019: Computer Supported Cooperative Work and Social Computing pp 197–211Cite as

Research on Customer Segmentation Method for Multi-value-Chain Collaboration

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1042))

Abstract

For multi-value-chain collaborative business in automotive industry, the value-based customers segmenting has become an important method to improve the synergy efficiency of automobiles. In order to accurately discover the value of potential customers, this paper proposes a customer segmentation method for multi-value-chain collaboration. Firstly, we screened evaluation index with high degree of customer value relevance, and establish a value-based customer data representation model according to customer’s information we collected on the collaborative marketing platform of the automobile marketing value chain; Then, according to the distribution characteristics of the customer information, we used improved initial centroid selection method for k-means algorithm to establish customer segmentation method. Finally, based on the customer data accumulated on the car collaborative business platform, design an experiment to verify the accuracy of customer segmentation. The result of experiment shows that the customer segmentation method effectively reduces the computational complexity. This method can guide the designing of multi-value chain coordination mechanism for customer segmentation and create more value of both automobile production value chain and sales value chain.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Parsell, R.D., Wang, J., Kapoor, C.: Customer Segmentation, WO/2014/099928 (2014)

    Google Scholar 

  2. Wang, J., Yin, L., Xu, J.: Research and Application of LNG Customer Segmentation Based on Cluster Analysis. Zhonghai Petroleum Gas and Electricity Group Co., Ltd. (2019)

    Google Scholar 

  3. Chen, Z.-Y., Fan, Z.-P., Sun, M.: Multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations. Eur. J. Oper. Res. (11), 34–37 (2016)

    Google Scholar 

  4. Ou, J., Cao, X., Zhang, J., Ding, C.: Research on power customer segmentation based on hybrid neural network. Comput. Digit. Eng. (2019)

    Google Scholar 

  5. Zhang, H.: Research and Application of Clustering ensemble Algorithm in Customer Segmentation. Anhui University (2016)

    Google Scholar 

  6. Zhang, L., Ma, Y.: Research on aviation customer churn and segmentation based on data mining technology and implementation of R language program. Math. Pract. Theory (2019)

    Google Scholar 

  7. Fan, X.: Research on Customer Segmentation and Marketing Strategy of H Bank Based on Cluster Analysis. East China University of Science and Technology (2016)

    Google Scholar 

  8. Zhang, Y., Zhou, Y.: A review of cluster algorithms. J. Comput. Appl. (2019)

    Google Scholar 

  9. Li, Z., Zhang, Y.: Analysis and evaluation of clustering analysis algorithm. Electron. Technol. Softw. Eng. (2019)

    Google Scholar 

  10. Wang, X., Wang, H., Wang, J.: Comparison of clustering methods in data mining. Comput. Technol. Dev. 16(10), 20–22 (2006)

    Google Scholar 

  11. Lai, X., Gong, X., Han, L.: Genetic algorithm based K-medoids clustering within MapReduce framework. Comput. Sci. 44(3), 23–26 (2017)

    Google Scholar 

  12. Madan, S., Dana, K.J.: Modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) for visual clustering. Formal Pattern Anal. Appl. 1–18 (2015)

    Google Scholar 

  13. Li, W.: The Study of Clustering Algorithm based on Density. Hunan University (2010)

    Google Scholar 

  14. Sharma, V.P., Sharma, S, Kumar, S., et al.: Cob web and dry bubble diseases in Lentinula edodes cultivation-a new report. International Society for Mushroom Science (2016)

    Google Scholar 

  15. Guo, D.: Research on Algorithms of Recommendation in Personalized Recommendation Systems. Beijing Jiaotong University (2017)

    Google Scholar 

  16. Wang, J.: The Application Research of Cluster Analysis in Caifupai Customer Segmentation. University of Electronic Science and Technology of China (2016)

    Google Scholar 

  17. He, L.: Customer segmentation of China telecom. Southwest Jiaotong University (2016)

    Google Scholar 

  18. Huang, Y.: Analysis of airline customer value based on k-means clustering method. Commun. World, 303–305 (2018)

    Google Scholar 

  19. Ma, C., Wu, T., Duan, M.: Clustering algorithm based on membership degree of K-nearest neighbor. Comput. Eng. Appl. 52(10), 55–58 (2016)

    Google Scholar 

  20. Mokdad, F., Haddad, B.: Improved infrared precipitation estimation approaches based on k-means clustering: application to North Algeria using MSG-SEVIRI satellite data. Adv. Space Res. (2017)

    Google Scholar 

  21. Viegas, J.L., Vieira, S.M., Melício, R., Mendes, V.M.F., Sousa, J.M.C.: Classification of new electricity customers based on surveys and smart metering data. Energy (09), 123–125 (2016)

    Google Scholar 

  22. Lu, H.: Study on value-added service for power customer segmentation. Guangdong University of Technology (2016)

    Google Scholar 

  23. Huang, M., Lin, H.: A new method of k-means clustering algorithm with events based on variable time granularity. In: Web in formation Systems and Applications Conference. IEEE (2017)

    Google Scholar 

  24. Fan, Z., Sun, Y.: Clustering of college students based on improved K-means algorithm. In: Computer Symposium. IEEE (2017)

    Google Scholar 

  25. UCI Data Set. [EB/OL]. http://archive.ics.uci.edu/ml/

  26. Wu, C., Dong, S., Li, C.: Research on the discretization algorithm of association classification based on Gaussian mixture model. J. Chin. Comput. Syst. (2018)

    Google Scholar 

  27. Liu, J., Zhang, K., Wang, G.: Comparative study on data standardization methods in comprehensive evaluation. Digit. Technol. Appl. (2018)

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. This paper is supported by The National Key Research and Development Program of China (2017YFB1400902).

Author information

Authors and Affiliations

  1. Laboratory of Parallel Software and Computational Science, Institute of Software, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Lei Duan, Wen Bo & Changyou Zhang

  2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, People’s Republic of China

    Lei Duan

  3. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, People’s Republic of China

    Wen Bo

  4. Chengdu Guolong Information Engineering Co., Ltd., Chengdu, Sichuan, People’s Republic of China

    Qing Wen

  5. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan, People’s Republic of China

    Shan Ren

Authors
  1. Lei Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen Bo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Changyou Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changyou Zhang .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Duan, L., Bo, W., Wen, Q., Ren, S., Zhang, C. (2019). Research on Customer Segmentation Method for Multi-value-Chain Collaboration. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1377-0_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1376-3

  • Online ISBN: 978-981-15-1377-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation