Advertisement

Analysis of Users Buying Behaviour to Improve the Coupon Marketing

  • František BabičEmail author
  • Ľudmila Pusztová
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 303)

Abstract

The paper describes a data-mining case study devoted to an analysis of the users buying behaviour with the aim to improve the effectiveness of the relevant coupon marketing campaign. A coupon represents a ticket or number in an electronic form that we can use for a financial discount when purchasing a product. We can use this type of marketing to increase the number of the new customers and to reward the current ones. In our case, we used the datasets available within DMC 2015 and implemented the analytical process in accordance to the CRISP-DM methodology. Based on initial form of data, we focused mainly on pre-processing phase to extract hidden information, potentially useful for better prediction. For this purpose, we used decision trees algorithms like C4.5, C5.0, Random forest, CART and Logistic model tree. The obtained results were plausible and in some cases more accurate as other already published.

Keywords

Transaction Coupons Data mining Decision trees 

Notes

Acknowledgments

The work presented in this paper was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant no. 1/0493/16, by the Cultural and Educational Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grants no. 025TUKE-4/2015 and no. 05TUKE-4/2017.

References

  1. 1.
    Anderson, T.W., Darling, D.A.: Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 23, 193–212 (1952)CrossRefGoogle Scholar
  2. 2.
    Bednár, P., Sarnovský, M., Demko, V.: RDF vs. NoSQL databases for the semantic web applications. In: SAMI 2014: IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Herľany, Slovakia, pp. 361–364 (2014)Google Scholar
  3. 3.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, Ch.J.: Classification and Regression Trees. CRC Press (1999)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  5. 5.
    Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59, 161 (2005)CrossRefGoogle Scholar
  6. 6.
    Butka, P., Pócs, J., Pócsová, J.: Distributed computation of generalized one-sided concept lattices on sparse data tables. Comput. Inform. 34(1), 77–98 (2015)Google Scholar
  7. 7.
    Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step-by-Step Data Mining Guide (2000)Google Scholar
  8. 8.
    Cheung, P.: Top 6% on Kaggle Project: Coupon Purchase Prediction. NYC Data Science Academy (2015)Google Scholar
  9. 9.
    Gupta, A.: Predicting Coupon Purchases on ポンパレ (Ponpare). Uhuru Data Lab (2015)Google Scholar
  10. 10.
    Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947)CrossRefGoogle Scholar
  11. 11.
    Murthy, K.S.: Automatic construction of decision tress from data: a multidisciplinary survey. Data Min. Knowl. Discov. 2, 345–389 (1997)CrossRefGoogle Scholar
  12. 12.
    Nadj, J., Lazarevic, J.: Influence of Coupons on Order Patterns Data Mining Course Project (2015)Google Scholar
  13. 13.
    Patil, N., Lathi, R., Chitre, V.: Comparison of C5.0 & CART classification algorithms using pruning technique. Int. J. Eng. Res. Technol. 1(4), 1–5 (2012)Google Scholar
  14. 14.
    Pearson, K.: On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag. 50(302), 157–175 (1900). Series 5CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Burlington (1993)Google Scholar
  17. 17.
  18. 18.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Ware-Housing 5(4), 13–22 (2000)Google Scholar
  19. 19.
  20. 20.
    Vokorokos, L., Hurtuk, J., Madoš, B., Obešter, P.: Security issues of email marketing service. Acta Electrotechnica et Informatica 15(2), 9–14 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and InformaticsTechnical University of KošiceKošiceSlovak Republic

Personalised recommendations