Uncertainties: An investigation of aleatory and epistemic errors in market segmentation analysis

Abstract

Despite the popularity of cluster analysis as a segmentation tool, its limitations continue to include the production of random solutions and the existence of uncertainties. This study aims to assist marketers in understanding the characteristics of festival goers based on music events in Malaysia. The present study investigates the existence and effect of uncertainties produced in cluster analysis results by using an artificial neural network (ANN). Four market segments are identified: the alarm hitter, the technology ticker, the plug puller, and the fuse blower. Error analysis results reveal that uncertainties may cause incorrect predictions. Academically, the limitations in existing market segmentation studies are highlighted by adding the process of ANN training and testing the segments generated from the cluster analysis. From the industry perspective, this approach introduces an important segmentation basis—technographic segmentation—to tap into the wired generation. Future research may extend this study and apply a nonprobabilistic neural network to eliminate the existence of errors in cluster analysis.

Keywords:

• Market segmentation
• cluster analysis
• uncertainties
• artificial neural network
• music event

Additional information

Funding

We would like to express the highest appreciation to Ministry of Education Malaysia (MOE) for the financial support throughout this research was conducted with grant (R. J130000.7810.5F068) funded by Universiti Teknologi Malaysia (UTM).

Notes on contributors

Nur Balqish Hassan

Nur Balqish Hassan, a PhD candidate in Management specialize in event marketing at Department of Business Administration, Azman Hashim International Business School, Universiti Teknologi Malaysia, Malaysia. Her research interest is in market segmentation in events, and also adapting artificial neural networks in predicting the classification of music event attendees through pattern recognition.

Noor Hazarina Hashim

Noor Hazarina Hashim, an Associate Professor at Department of Business Administration, Azman Hashim International Business School, Universiti Teknologi Malaysia. She teaches a range of courses at both the graduate and undergraduate level focused on electronic marketing, marketing research and tourism marketing. She researches on how technology influences destination image formation and how technology changes traveler’s behavior. Her research has been published in international and local media outlets and in leading tourism and hospitality journals.

Khairul H. Padil

Khairul Hazman Padil, a Senior lecturer at School of Civil Engineering, Faculty of Engineering. His research is focusing on the area of Structural health monitoring concentrate on vibration-based damage detection and Artificial Neural Network (ANN). He has been involved in a variety of research projects seeking new knowledge in damage identification using vibration data and ANN. He also involved in several consultancy works on structural integrity assessment, vibration monitoring, modal testing and pattern recognition using ANN.

Norhisham Bakhary

Norhisham Bakhary is an Associate Professor at School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia. His research interest is in area Artificial Neural Network and the structural health monitoring focusing on vibration based damage detection. He also involved in several research projects exploring new methods to detect damage based on vibration parameters.