콘텐츠 본문
논문 국내 국내전문학술지(KCI급) 자동차 재구매 증진을 위한 데이터 마이닝 기반의 맞춤형 전략 개발
- 학술지 구분 국내전문학술지(KCI급)
- 게재년월 2017
- 저자명 이동욱, 최근호, 유동희
- 학술지명 정보시스템연구
- 발행처명 한국정보시스템학
- 발행국가 국내
- 논문언어 한국어
- 전체저자수 3
논문 초록 (Abstract)
Purpose Although automobile production has increased since the development of the Korean automobile industry, the number of customers who can purchase automobiles decreases relatively. Therefore, automobile companies need to develop strategies to attract customers and promote their repurchase behaviors. To this end, this paper analyzed customer data from a Korean automobile company using data mining techniques to derive repurchase strategies. Design/methodology/approach We conducted under-sampling to balance the collected data and generated 10 datasets. We then implemented prediction models by applying a decision tree, naive Bayesian, and artificial neural network algorithms to each of the datasets. As a result, we derived 10 patterns consisting of 11 variables affecting customers’ decisions about repurchases from the decision tree algorithm, which yielded the best accuracy. Using the derived patterns, we proposed helpful strategies for improving repurchase rates. Findings From the top 10 repurchase patterns, we found that 1) repurchases in January are associated with a specific residential region, 2) repurchases in spring or autumn are associated with whether it is a weekend or not, 3) repurchases in summer are associated with whether the automobile is equipped with a sunroof or not, and 4) a customized promotion for a specific occupation increases the number of repurchases.
