Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models

Segmenting consumers based on how they spend a tax rebate: An analysis of the Australian stimulus payment

“…Note that, like investing, paying off debts can be considered a form of saving (Katona, 1975). Acquisition patterns reflecting consumer priorities for financial products and services have already been reported in the literature (e.g., Kamakura et al, 1991;Paas, Bijmolt, & Vermunt, 2007;Prinzie & Van den Poel, 2006).…”

Application of data mining techniques in customer relationship management: A literature review and classification

“…Larivie'Re and , Poel and Larivière (2004) Clustering Attribute oriented induction Li et al (2006) Regression Logistic regression Cassab and Maclachlan (2006), Poel and Buckinx (2005) Sequence discovery Goal oriented sequential pattern Chiang et al (2003) One to one marketing Association Association rules Adomavicius and Tuzhilin (2001), , Chen, Chiu, and Chang (2005), Demiriz (2004) Association rules Aggarval and Yu (2002), Brijs et al (2004), Jukic and Nestorov (2006) Markov chain model Giudici and Passerone (2002) Sequence discovery Association rules Chen, Tang, Shen, and Hu (2005), Kubat et al (2003) Up/cross selling Association Neural network and association rule Changchien et al (2004) Sequence discovery Mixture transition distribution Prinzie and Poel (2006) Of the 54 articles related to customer retention, only two of them discuss complaints management. Complaints management is a crucial requirement for successful businesses when managing customers' needs and changes in behavior.…”

“…Customer lifetime value analysis is defined as the prediction of the total net income a company can expect from a customer (Drew, Mani, Betz, & Datta, 2001;Etzion, Fisher, & Wasserkrug, 2005;Rosset, Neumann, Eick, & Vatnik, 2003). Up/Cross selling refers to promotion activities which aim at augmenting the number of associated or closely related services that a customer uses within a firm (Prinzie & Poel, 2006). Market basket analysis aims at maximizing the customer transaction intensity and value by revealing regularities in the purchase behaviour of customers (Aggarval & Yu, 2002;Brijs, Swinnen, Vanhoof, & Wets, 2004;Carrier & Povel, 2003;Chen, Tang, Shen, & Hu, 2005;Giudici & Passerone, 2002;Kubat, Hafez, Raghavan, Lekkala, & Chen, 2003).…”

A Comprehensive Study of CRM through Data Mining Techniques

“…This phase involves targeting the population who are most likely to become customers or most profitable to the company. Moreover, it involves analyzing customers who are being lost to the competition and how they can be won back [5] Customer attraction: This is the phase following customer identification. After identifying the segments of potential customers, organizations can direct effort and resources into attracting the target customer segments [6] Customer retention: This is the central concern for CRM.…”