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Green vs. Non-Green Customer Behavior: A Self-Organizing Time Map Over Greenness

Annika Holmbom, Samuel Rönnqvist, Peter Sarlin, Tomas Eklund, Barbro Back, Green vs. Non-Green Customer Behavior: A Self-Organizing Time Map Over Greenness. In: Wei Ding, Takashi Washio (Eds.), IEEE 13th International Conference on Data Mining Workshops, IEEE International Conference on Data Mining, 1–7, IEEE, 2013.

Abstract:

Companies have traditionally used segmentation approaches to study and learn more about their customer base. One area that has attracted considerable amounts of research in recent years is that of green customer behavior. However, the approaches used have often been static clustering approaches and have focused on identifying green vs. non-green customers. In fact, results have been non-unanimous and not seldom contradictory. An alternative approach is to study customers according to degrees of green purchases. Recently, a Self-Organizing Time Map (SOTM) over any variable of cardinal, ordinal or higher level of measurement has been proposed. The key idea is to enable the exploration of changes in cluster structures over not only the time dimension, but also any other variable. This paper presents an application of the SOTM to demographic and behavioral customer data, in which the key focus is on assessing how customer behavior varies over customers' degree of greenness.

BibTeX entry:

@INPROCEEDINGS{inpHoRxSaEkBa13a,
  title = {Green vs. Non-Green Customer Behavior: A Self-Organizing Time Map Over Greenness},
  booktitle = {IEEE 13th International Conference on Data Mining Workshops},
  author = {Holmbom, Annika and Rönnqvist, Samuel and Sarlin, Peter and Eklund, Tomas and Back, Barbro},
  series = {IEEE International Conference on Data Mining},
  editor = {Ding, Wei and Washio, Takashi},
  publisher = {IEEE},
  pages = {1–7},
  year = {2013},
  keywords = {Self-Organizing Time Map (SOTM); Green customer behavior; Clustering; Customer Relationship Management (CRM), Visual Analytics},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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