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Visualized Benefit Segmentation Using Supervised Self-organizing Maps: Support Tools for Persona Design and Market Analysis

  • Fumiaki SaitohEmail author
Conference paper
  • 276 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)

Abstract

This study provides a visualization technique for market segmentation by benefits using supervised self-organizing maps (SOMs). Recent use of customer personas has attracted attention in the marketing field, and is important in the development of products and services that customers want. Market segmentation tools based on clustering methods, such as SOM, k-means and neural networks, have been widely used in recent years. By associating customer benefits and demographics to classes and attributes, respectively, supervised SOM can be a useful tool to support persona design. Market segmentation is important in order to utilize personas effectively for decision-making in businesses that have accumulated large amounts of customer data. We use a real case of customer data from a hotel in Tokyo, Japan to illustrate our approach for market segmentation and customer analysis.

Keywords

Benefit segmentation Supervised self-organizing map Market segmentation Persona Customer evaluation Data visualization 

Notes

Acknowledgements

This work was supported by JSPS KAKENHI Grant-in-Aid for Scientific Research C 19K04887.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chiba Institute of TechnologyNarashinoJapan

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