Skip to main content

The Optimal Stopping Criteria for a Customer Contact Strategy in Targeted Marketing

  • Conference paper
  • First Online:
Operations Research and Enterprise Systems (ICORES 2020, ICORES 2021)

Abstract

Many companies and institutions, such as banks, usually have a wide range of products which must be marketed to their customers. Multiple contact channels such as phone calls (the most common but also most costly), emails, postal mail and Social Media are used for marketing these products to specific customers. The more contacts (and hence cost to the company) made to a customer the higher the chance that the customer will subscribe but beyond a certain limit this customer may in fact become irritated by such calls if they are not really interested in the product (which is another potential cost to the company if they lose the customer). Previous work has shown that one can use historical data on customer contacts together with demographic information of those customers to significantly increase the average number of subscriptions achieved, or products bought, per phone call (or contact) made when considering new customers. We demonstrate an improved approach to this problem and illustrate with data obtained from a bank.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

References

  1. Bertsimas, D., Mersereau, A.J.: A learning approach for interactive marketing to a customer segment. Oper. Res. 55(6), 1120–1135 (2007)

    Article  MathSciNet  Google Scholar 

  2. Dwyer, F.R.: Customer lifetime valuation to support marketing decision making. J. Direct Mark. 11(4), 6–13 (1997)

    Article  Google Scholar 

  3. Pearson, K.: Liii. on lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901). https://doi.org/10.1080/14786440109462720

    Article  MATH  Google Scholar 

  4. Gu, J., Na, J., Park, J., Kim, H.: Predicting success of outbound telemarketing in insurance policy loans using an explainable multiple-filter convolutional neural network. Appl. Sci. 11(15), 7147 (2021)

    Article  Google Scholar 

  5. Hosein, P., Ramoudith, S., Rahaman, I.: On the optimal allocation of resources for a marketing campaign. In: ICORES, pp. 169–176 (2021)

    Google Scholar 

  6. Karim, M., Rahman, R.M.: Decision tree and Naive Bayes algorithm for classification and generation of actionable knowledge for direct marketing. J. Softw. Eng. Appl. 6(4), 196–206 (2013)

    Article  Google Scholar 

  7. Kotler, P., Keller, K.: A Framework for Marketing Management, 5th edn. Prentice Hall, Upper Saddle River (2011)

    Google Scholar 

  8. Kozak, J., Juszczuk, P.: The ACDF algorithm in the stream data analysis for the bank telemarketing campaign. In: 2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 49–53. IEEE (2018)

    Google Scholar 

  9. Kumar, N., Pauwels, K.: Don’t cut your marketing budget in a recession. Harvard Business Review (2020)

    Google Scholar 

  10. Lawi, A., Velayaty, A.A., Zainuddin, Z.: On identifying potential direct marketing consumers using adaptive boosted support vector machine. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), pp. 1–4. IEEE, Kuta Bali, Indonesia (2017)

    Google Scholar 

  11. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  Google Scholar 

  12. Loshin, D., Reifer, A.: Using Information to Develop a Culture of Customer Centricity: Customer Centricity, Analytics, and Information Utilization. Morgan Kaufmann Publishers Inc., San Francisco (2013)

    Google Scholar 

  13. Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014)

    Article  Google Scholar 

  14. Moro, S., Cortez, P., Rita, P.: Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns. Neural Comput. Appl. 26(1), 131–139 (2014). https://doi.org/10.1007/s00521-014-1703-0

    Article  Google Scholar 

  15. Mylonakis, J.: The influence of banking advertising on bank customers: an examination of Greek bank customers’ choices. Banks Bank Syst. 3(4), 44–49 (2008)

    Google Scholar 

  16. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(10), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  17. Puteri, A.N., Tahir, Z., et al.: Comparison of potential telemarketing customers predictions with a data mining approach using the MLPNN and RBFNN methods. In: 2019 International Conference on Information and Communications Technology (ICOIACT), pp. 383–387. IEEE, IEEE, Yogyakarta, Indonesia (2019)

    Google Scholar 

  18. Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Amsterdam (2014)

    Google Scholar 

  19. Ramoudith, S., Rahaman, I., Hosein, P.: Implementation of improved customer segmentation approach. GitHub (2021). https://github.com/shiv1994/BankMarketingExtended

  20. Roach, G.: Consumer perceptions of mobile phone marketing: a direct marketing innovation. Direct Market. Int. J. 3(2), 124–138 (2009)

    Article  Google Scholar 

  21. Team, R.D.: RAPIDS: collection of libraries for end to end GPU data science (2018). https://rapids.ai

  22. Turkmen, E.: Deep learning based methods for processing data in telemarketing-success prediction. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 1161–1166. IEEE (2021)

    Google Scholar 

  23. Virtanen, P., et al.: Scipy 10: fundamental algorithms for scientific computing in python. Nat. Methods 17(3), 261–272 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shiva Ramoudith .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ramoudith, S., Hosein, P., Rahaman, I. (2022). The Optimal Stopping Criteria for a Customer Contact Strategy in Targeted Marketing. In: Parlier, G.H., Liberatore, F., Demange, M. (eds) Operations Research and Enterprise Systems. ICORES ICORES 2020 2021. Communications in Computer and Information Science, vol 1623. Springer, Cham. https://doi.org/10.1007/978-3-031-10725-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10725-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10724-5

  • Online ISBN: 978-3-031-10725-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics