Until now, research on consumers’ purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers’ self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

I. Introduction
II. Theoretical Background
    1. Big data and consumer’s purchase pattern
    2. Association rule analysis
    3. Consumer’s brand loyalty and purchase pattern
    4. Consumer’s purchase channel pattern
III. Research Method
    1. Research questions
    2. Data collection
    3. Data analysis and process
IV. Results
    1. Comparison of sales by fashion item and distribution channel
    2. Brand loyalty categorization results
    3. Fashion item purchase pattern results in offline channel by brand loyalty group
    4. Fashion item purchase pattern result in online channel by brand loyalty group
    5. Transition pattern result of purchasing channels by brand loyalty group
V. Conclusion
References

• Ki Yong Kwon(Ph. D., Dept of Fashion and Textiles, Seoul National University, Korea) | 권기용 (서울대학교 의류학과 박사) Corresponding author