Exploring Customer Impulse Buying Behavior from Credit Card Transaction Records
Date Issued
2015
Date
2015
Author(s)
Wang, Wen-Cheng
Abstract
Impulse buying behavior is an unexpected and irresistible purchasing behavior. From all the past research on impulse buying, most researchers focused on psychological factors like purchasing environment, personality traits, marketing stimuli and behavior parameters etc. by using traditional questionnaire analysis to study the factors that lead to impulse buying. Because of the characteristic of being unexpected and sudden, researchers couldn’t identify the exact reason immediately. We even couldn’t know the actual outcomes when measuring the difference of purchasing attitudes before buying and after buying. That leads to the restrictions of previous research. Impulse buying is a very normal behavior of consumer behavior and transactions that could happen anytime and anywhere. According to past research, the percentage of impulse buying behavior would rise when some specific stores and particular industries were involved. Furthermore, the more impulse buying characteristics consumers have, the more customer value enterprises pursue. Therefore, no matter on academic research or on practices, impulse buying researches has already been a developed trend. The developments of database mining, information technology, and ubiquitous network have changed the society. Countless transaction behavior means countless transaction data are being recorded during the big data era. With the statistical methods on database and the collocation of marketing strategies, we can try to identify the motivations behind the consumer behavior. But there are few research studying impulse buying by using data mining methods on consumer transaction records. Therefore, I tried to use some data mining methods to analyze the credit cards transaction database that targeted customers with impulse buying characteristics by utilizing two-step clustering analysis, demographic variables analysis, and the dynamic segments stability analysis. According to the first outcome, the variables we chose and dealt with used in two-step clustering analysis from the credit cards database are able to differentiate the impulse buying and non-impulse buying customers. But the percentage of the impulse buying customer was too low. So, I kept tring another method to identify impulse buying customers by using only one classification index that was made from database. The second time’s outcome was siginificant and the number of those impulse buying customers we identified was much more than the first time. Finally, we can use these results, as well as the demographic variables analysis and the transaction items of industries analysis from records to make customer management programs, one-to-one customized marketing strategies, recommendation systems and to develop the collaboration relationships with related industries and enterprises. With the objective to create bigger and much more value for the market and companies.
Subjects
Impulse Buying
Database Marketing
Data Mining
Cluster Analysis
Two-Step Cluster Analysis
Dynamic Segments Stability
Chi-Square Test
Cross-Table Analysis
SDGs
Type
thesis
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