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Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets

Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets

Goran Klepac
Copyright: © 2016 |Pages: 37
ISBN13: 9781466694743|ISBN10: 1466694742|EISBN13: 9781466694750
DOI: 10.4018/978-1-4666-9474-3.ch008
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MLA

Klepac, Goran. "Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets." Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., IGI Global, 2016, pp. 216-252. https://doi.org/10.4018/978-1-4666-9474-3.ch008

APA

Klepac, G. (2016). Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets. In S. Bhattacharyya, P. Banerjee, D. Majumdar, & P. Dutta (Eds.), Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications (pp. 216-252). IGI Global. https://doi.org/10.4018/978-1-4666-9474-3.ch008

Chicago

Klepac, Goran. "Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets." In Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., 216-252. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9474-3.ch008

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Abstract

Chapter represents discovering behavioural patterns within non-temporal and temporal data subsets related to customer churn. Traditional approach, based on using conventional data mining techniques, is not a guarantee for discovering valuable patterns, which could be useful for decision support. Business case, as a part of the text, illustrates such type of situation, where an additional data set has been chosen for finding useful patterns. Chosen data set with temporal characteristics was the key factor after applying REFII model on it, for finding behavioural customer patterns and for understanding causes of the increasing churn trends within observed portfolio. Text gives a methodological framework for churn problem solution, from customer value calculation, to developing predictive churn model, as well as using additional data sources in a situation where conventional approaches in churn analytics do not provide enough information for qualitative decision support. Revealed knowledge was a base for better understanding of customer needs and expectations.

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