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Sequential pattern mining for customer relationship management analysis

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Abstract

Customer Relationship Management (CRM) is a set of tools for managing a company’s interactions with its customers. Extracting the frequent interaction behaviors from the CRM database is useful to better understand the customers habits and to improve the company services. In this article, we propose an preliminary work on the study of interactive tool to mine frequent time-gap sequences in a CRM database. We propose a tool based on the TGSP algorithm of Yen et Lee (2013) to extract frequent sequential patterns with time-gap information. The extracted patterns are visualised in a tree view and the analyst can interact with this view to support its analysis of the patterns.
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Dates and versions

hal-01097466 , version 1 (19-12-2014)

Identifiers

  • HAL Id : hal-01097466 , version 1

Cite

Kiril Gashteovski, Thomas Guyet, René Quiniou, Alzennyr da Silva, Véronique Masson. Sequential pattern mining for customer relationship management analysis. Atelier GAST@EGC2015, Jan 2015, Luxembourg, Luxembourg. ⟨hal-01097466⟩
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