Customer Behavior Analysis and Classification Based on Process Mining

Author(s):  
Meijun Liu ◽  
Licheng Zhao ◽  
Fengmei Sun ◽  
Weizheng Zhao ◽  
Yi Zuo ◽  
...  
2021 ◽  
Author(s):  
Md. Golam Rabiul Alam ◽  
Sajjad Hussain ◽  
Md. Mofaqkhayrul Islam Mim ◽  
Md Tarikul Islam

Author(s):  
Rob H. Bemthuis ◽  
Martijn Koot ◽  
Martijn R. K. Mes ◽  
Faiza A. Bukhsh ◽  
Maria-Eugenia Iacob ◽  
...  

2021 ◽  
Vol 96 ◽  
pp. 107541
Author(s):  
Preeti Nagrath ◽  
Tu N. Nguyen ◽  
Shivani Aggarwal ◽  
D Jude Hemanth

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 557 ◽  
Author(s):  
Onur Dogan ◽  
Jose-Luis Bayo-Monton ◽  
Carlos Fernandez-Llatas ◽  
Basar Oztaysi

The study presents some results of customer paths’ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men’s bathroom or women’s bathroom. Since the study has a comprehensive scope, we focused on male and female customers’ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.


2017 ◽  
Vol 11 (4) ◽  
pp. 380-397 ◽  
Author(s):  
Hoda Ghavamipoor ◽  
S. Alireza Hashemi Golpayegani ◽  
Maryam Shahpasand

Purpose In this paper, a Quality of Service-sensitive customer behavior model graph (QoS-CBMG) is proposed for use in service quality adaptation in e-commerce systems. Success in achieving customer satisfaction and maximizing profit in e-commerce is highly dependent on the QoS provided. However, providing high-level QoS for all customers in all Web sessions is often deemed costly and inefficient. Therefore, a QoS-sensitive model for formulating QoS-aware offers to customers is required. The paper aims to respond to this necessity. Design/methodology/approach Process mining is adopted as the knowledge extraction technique for developing a QoS-CBMG. If it is assumed that user navigation on a website is a process, then clickstreams during one user’s navigations can be considered process steps. Findings The application of both QoS-CBMG (the new model) and CBMG (the classic version) to the same real data set demonstrated that the proposed method outperforms CBMG due to its reduction of average absolute error in the measurement scale. This finding also verifies the assumption that customer behavior is sensitive to the level of QoS. Research limitations/implications From a theoretical viewpoint, the obtained QoS-CBMG facilitates the adaption in e-commerce systems, which leads to conduct the user to the desired behavior by tuning QoS levels in different Web sessions in a dynamic manner. This implication is due to the fact that QoS-CBMG can predict the upcoming clickstream of the customer at different QoS levels. Practical implications Using the proposed model for the adaptation of service quality in e-commerce websites not only results in the efficient management of the provider’s resources but also encourages customer purchases from the website and increases profitability. It is noteworthy that with the advent of cloud computing, e-commerce websites are enabled to provide various levels of QoS for their customers by supplying their basic services (e.g. infrastructure, platform) through cloud platforms. Originality/value According to the best of our knowledge, no previous model has taken into account the QoS dimension for customer behavior modeling. The main contribution of this paper is to propose a CBMG that is sensitive to the QoS provided to customers during their navigation to formulate QoS-aware offers to them.


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