Advertisement

A Novel Model for Finding Critical Products with Transaction Logs

  • Ping Yu Hsu
  • Chen Wan HuangEmail author
  • Shih Hsiang Huang
  • Pei Chi Chen
  • Ming Shien Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

For the consumer market, finding valuable customers is the first priority and is assumed to assist companies in obtaining more profit. If we could discover critical products that are related with valuable customers, then it will lead to better marketing strategy to fulfill those essential customers. It will also assist companies in business development. This study selects real retail transaction data via the recency, frequency, and monetary (RFM) analysis and adopts the K-means algorithm to obtain results. Moreover, the Apriori algorithm with minimum support and skewness criteria is used to filter and find critical products. In this research, we found a novel methodology through setting the minimum support and skewness criteria and utilized the Apriori algorithm to identify 31 single critical products and 60 critical combinations (two products). This study assist companies in finding critical products and important customers, which is expected to provide an appropriate customer marketing strategy.

Keywords

RFM K-means Association rules Skewness Frequent itemsets 

References

  1. 1.
    Abirami, M., Pattabiraman, V.: Data mining approach for intelligent customer behavior analysis for a retail store. In: Vijayakumar, V., Neelanarayanan, V. (eds.) ISBCC 2016. SIST, vol. 49, pp. 283–291. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30348-2_23CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB (1994)Google Scholar
  3. 3.
    Beheshtian-Ardakani, A., Fathianb, M., Gholamian, M.: A novel model for product bundling and direct marketing in e-commerce based on market segmentation. Decis. Sci. Lett. 7, 39–54 (2018)CrossRefGoogle Scholar
  4. 4.
    Bhandari, A., Gupta, A., Das, D.: Improvised apriori algorithm using frequent pattern tree for real time applications in data mining. Procedia Comput. Sci. 46, 644–651 (2015)CrossRefGoogle Scholar
  5. 5.
    Cho, Y.S., Moon, S.C., Ryu, K.H.: Mining association rules using RFM scoring method for personalized u-Commerce recommendation system in emerging data. In: Kim, T.-H., Ramos, C., Abawajy, J., Kang, B.-H., Ślęzak, D., Adeli, H. (eds.) MAS/ASNT 2012. CCIS, vol. 341, pp. 190–198. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35248-5_27CrossRefGoogle Scholar
  6. 6.
    Hu, Y.H., Yeh, T.W.: Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowl. Based Syst. 61, 76–88 (2014)CrossRefGoogle Scholar
  7. 7.
    Hughes, A.M.: Strategic Database Marketing. McGraw-Hill Pub. Co., New York (2001)Google Scholar
  8. 8.
    Kantardzic, M.: DATA MINING: Concepts, Models, Methods and Algorithms. John Wiley & Sons, Inc., Hoboken (2001)zbMATHGoogle Scholar
  9. 9.
    Khajvand, M., Zolfaghar, K., Ashoori, S., Alizadeh, S.: Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study. Procedia Comput. Sci. 3, 57–63 (2011)CrossRefGoogle Scholar
  10. 10.
    Grami, M., Gheibi, R., Rahimi, F.: A novel association rule mining using genetic algorithm. In: 2016 Eighth International Conference on Information and Knowledge Technology (IKT), Hamedan, Iran (2016)Google Scholar
  11. 11.
    Song, M., Zhao, X., Haihong, E., Ou, Z.: Statistics-based CRM approach via time series segmenting RFM on large scale data. Knowl. Based Syst. 132, 21–29 (2017)CrossRefGoogle Scholar
  12. 12.
    Vasoya, A., Koli, N.: Mining of association rules on large database using distributed and parallel computing. Procedia Comput. Sci. 79, 221–230 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ping Yu Hsu
    • 1
  • Chen Wan Huang
    • 1
    Email author
  • Shih Hsiang Huang
    • 1
  • Pei Chi Chen
    • 1
  • Ming Shien Cheng
    • 2
  1. 1.Department of Business AdministrationNational Central UniversityTaoyuan CityTaiwan (R.O.C.)
  2. 2.Department of Industrial Engineering and ManagementMing Chi University of TechnologyNew Taipei CityTaiwan (R.O.C.)

Personalised recommendations