Please enter verification code
Extending the Queue Question to Customer Purchase Rate to Predict the Probability of Customer Inactive
American Journal of Data Mining and Knowledge Discovery
Volume 4, Issue 1, June 2019, Pages: 53-56
Received: Apr. 17, 2019; Accepted: May 31, 2019; Published: Jun. 24, 2019
Views 900      Downloads 132
Hui-Hsin Huang, Department of Business Administration, Aletheia University, New Taipei City, Taiwan
Article Tools
Follow on us
In the rapidly changing market, there is a never-ending sequence of marketing actions and competitive reactions. Customer may consume much volume to stockpile more than they need when the price promotion or some benefit that retail gives. Thus this paper explores the customer purchase rate, the speed of product consumption and the inactive probability with extending queue model. A real data set from the customer purchase behavior in two electronic business retailers website includes purchase volume, the duration of customer consumption (which is the interpurchase time between purchase behavior with the same product in the same brand) and the duration of variety-seeking (which is defined as customer purchase the same categories but different brand of product) to estimate the parameters and calculate the expectation value of product consumption and product stockpile volume. This result can make application for other industries such as e-commerce.
Queue, Purchase Rate, The Speed of Product Consumption, Inactive Probability
To cite this article
Hui-Hsin Huang, Extending the Queue Question to Customer Purchase Rate to Predict the Probability of Customer Inactive, American Journal of Data Mining and Knowledge Discovery. Vol. 4, No. 1, 2019, pp. 53-56. doi: 10.11648/j.ajdmkd.20190401.18
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Pappas, O. I., Kourouthanassis, E. P., Giannakos, N. M. and Chrissikopoulos, V. (2016) Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions. Journal of Business Research 69 (2) 794-803.
Grewal, D., Ahlbom, C. P., Beitelspacher, L., Noble, M. S. and Nordfält, J. (2018) In-Store Mobile Phone Use and Customer Shopping Behavior: Evidence from the Field. Journal of Marketing 82 (4) 102-126.
D. Dong, and M. H. Kaiser, “Investigating Coupon Effects on Household Interpurchase Behavior for Cheese,” American Agricultural Economics Assosiation Annual Meeting, Long Beach, California, 23-26, July 2006.
Kumar, A. and Kumar, S. (2017) An Empirical Study on the Factors Affecting Online Shopping Behavior of Millennial Consumers. Journal of Internet Commerce 16 (3) 219-230.
Lismont, J., Ram, S., Vanthienen, J., Lemahieu, W. and Baesens, B. (2018) Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation. Expert Systems with Applications104 (15) 22-32.
Wang, J. H., Malthouse, C. E and Krishnamurthi, L. (2015) On the Go: How Mobile Shopping Affects Customer Purchase Behavior. Journal of Retailing, 91 (2) 217-234.
Igari, R. amd Hoshino, T. (2018) A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing. Computational Statistics & Data Analysis 126, 150-166.
Wysocki, A. F. (2005) A Frictionless Marketplace Operating in a World of Extremes. Choices, 20 (4) 263-268.
Schweidel, A. D., Park, Y. H. and Zainab, J. (2014) A Multiactivity Latent Attrition Model for Customer Base Analysis. Marketing Science 33 (2) 273-286.
Kalyanaraman, R. and Suvitha, V. (2012). A Single Server Compulsory Vacation Queue with Two Type of Services and with Restricted Admissibility. International Journal of Information and Management Sciences 23 (3) 287-304.
Rubio, N, Villaseñor, N. and Yagüe, J. M. (2019) Customer’s loyalty and trial intentions within the retailer: the moderating role of variety-seeking tendency. Journal of Consumer Marketing 36 (1) 235-274.
Tian, J., Zhang, Y. and Zhang, C. (2018) Predicting consumer variety-seeking through weather data analytics. Electronic Commerce Research and Applications 28, 194-207.
Baltas, G., Kokkinaki, F. and Loukopoulou, A. (2017) Does variety seeking vary between hedonic and utilitarian products? The role of attribute type. Journal of Consumer Behaviour 16 (1) 1-12.
Hyewook, G. J. and Aimee, D. (2016) Variety-seeking as an emotional coping strategy for chronically indecisive consumers. Marketing Letters 27 (1) 55-62.
Huang, Z., Liang, Y., Weinberg, B. C. and Gorn, J. G. (2019) The Sleepy Consumer and Variety Seeking. Journal of Marketing Research 56 (2) 1-18.
Niu, B., Chen, L., Liu, Y. and Jin, Y. (2019) Joint price and quality decisions considering Chinese customers' variety seeking behavior. International Journal of Production Economics 213 97-107.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186