CUSTOMER CLASSIFICATION WITH MULTI-METHOD MULTIVARIATE TECHNIQUES: AN APPLICATION ON BEAUTY SERVICE INDUSTRY
Received: 19.06.2021; Revised: 28.07.2021, Accepted: 21.09.2021, Published Online: 26.10.2021
Dr. Kalim Khan
Professor, Chetana’s RK Institute of Management and Research
Assistant Professor, Chetana’s RK Institute of Management and Research
Inability to carve out specific sub-segments within a target group leads to misdirected marketing efforts which is expensive. This research aims to create a robust classification for existing customers of the beauty service industry that would help businesses create marketing strategies that are relevant, hence efficient and profitable. Exploratory Factor Analysis (EFA) is used for dimension reduction, while Cluster Analysis is used for customer classification. Finally, Discriminant Analysis is used to validate clusters. Basis cluster analysis, two customer clusters are formed – ‘believers’ and ‘sceptics’, which are found to be significantly discriminating from each other based on cluster validation conducted with ANOVA, and Discriminant Analysis. These clusters are formed on the basis of customer’s trust in service set-up, service employee, and service brand. The clusters identified would help beauty service businesses to channel relevant marketing efforts to specific clusters making marketing efforts efficient, and turning better return on marketing investment.
Keywords: Multivariate Analysis, Cluster Analysis, Discriminant Analysis, Exploratory Factor Analysis, ANOVA, Beauty Services
JEL Codes: M31