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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, Ch.J.: Classification and Regression Trees. CRC Press (1999)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59, 161 (2005)
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)
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)
Cheung, P.: Top 6% on Kaggle Project: Coupon Purchase Prediction. NYC Data Science Academy (2015)
Gupta, A.: Predicting Coupon Purchases on ポンパレ (Ponpare). Uhuru Data Lab (2015)
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)
Murthy, K.S.: Automatic construction of decision tress from data: a multidisciplinary survey. Data Min. Knowl. Discov. 2, 345–389 (1997)
Nadj, J., Lazarevic, J.: Influence of Coupons on Order Patterns Data Mining Course Project (2015)
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)
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 5
PRRI-US 2017 Coupon and promo code use study. http://www.opportunityhealthcenter.org/spotlight/2017-coupon-promo-code-study/
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Burlington (1993)
Recruit Coupon Purchase Winner’s Interview: 2nd Place. http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/
Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Ware-Housing 5(4), 13–22 (2000)
The Shoppers Trend Report (2014). https://www.retailmenot.com/blog/2014-consumer-insights.html
Vokorokos, L., Hurtuk, J., Madoš, B., Obešter, P.: Security issues of email marketing service. Acta Electrotechnica et Informatica 15(2), 9–14 (2015)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Babič, F., Pusztová, Ľ. (2017). Analysis of Users Buying Behaviour to Improve the Coupon Marketing. In: Abramowicz, W. (eds) Business Information Systems Workshops. BIS 2017. Lecture Notes in Business Information Processing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-69023-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-69023-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69022-3
Online ISBN: 978-3-319-69023-0
eBook Packages: Business and ManagementBusiness and Management (R0)