• DocumentCode
    3438184
  • Title

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

  • Author

    Holmbom, Annika H. ; Ronnqvist, Samuel ; Sarlin, Peter ; Eklund, Tomas ; Back, Barbro

  • Author_Institution
    Dept. of IT, Abo Akademi Univ., Turku, Finland
  • fYear
    2013
  • fDate
    7-10 Dec. 2013
  • Firstpage
    529
  • Lastpage
    535
  • 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.
  • Keywords
    behavioural sciences; customer relationship management; self-organising feature maps; SOTM; behavioral customer data; green customer behavior; nongreen customers; self-organizing time map; Customer relationship management; Data visualization; Footwear; Green products; Image color analysis; Standards; Training; Clustering; Customer Relationship Management (CRM); Green customer behavior; Self-Organizing Time Map (SOTM); Visual Analytics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
  • Conference_Location
    Dallas, TX
  • Print_ISBN
    978-1-4799-3143-9
  • Type

    conf

  • DOI
    10.1109/ICDMW.2013.103
  • Filename
    6753966