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Examining consumer favorite preferences on credit cards in the Greek market: a conjoint analysis approach
(2025-03-31) Maniatopoulos, Harilaos; Μανιατόπουλος, Χαρίλαος; Indounas, Kostis; Baltas, George; Saridakis, Charalampos
Purpose: To investigate which credit card attributes are valued higher by Greek credit card holders and to identify who these card holders are. To examine the appeal of loyalty programs compared to other main features such as fees and interest rates. To determine the impact of attributes such as insurance services & commercial initiatives, and uncover usage patterns. In total, to decipher how Greeks choose and use their credit cards. Design / Methodology / Approach: A convenience sample of 127 credit card holders was surveyed using an online questionnaire. Participants evaluated 16 credit card profiles generated through an orthogonal design process, reflecting real-market attributes. Conjoint analysis was applied to determine the most valued features, followed by k-means cluster analysis to segment respondents based on their preferences. Findings: Annual fees emerged as the most important factor, with loyalty programs ranking second. Cluster analysis identified four consumer segments: i. Fee Sensitive // ii. Loyalty & Security Lovers // iii. Loyalty & Web Users // iv. Tier Seekers. Income and education were found to significantly influence credit card usage. Additional insights, including co-branded card preferences, are discussed, along with limitations and directions for future research. Practical Implications: Banks already use card behavioral segmentation models, however this study offers direct consumer insights, allowing for enhanced segmentation and targeting initiatives. Findings can help optimize all aspects of the marketing mix and refine credit card offerings to better align with user preferences. Originality / Value: The first academic study on credit cards in Greece that employs conjoint analysis. Probably among the first globally to explore the relative importance of loyalty programs, annual fees, interest rates, and insurance services together using conjoint analysis. The additional use of k-means cluster analysis in order to segment users based on stated preferences, can also be considered a novelty compared to similar studies.