초록

소매유형간 경쟁이 치열해 질수록 고객이탈을 방지하려는 전략은 고객관계관리측면에서 매우 중 요시된다고 할 수 있다. 특히 기존 전통적 소매업이 위협받는 가운데 대형슈퍼마켓의 진입이 이루 어질 경우 기존고객의 이탈 등 대형슈퍼마켓의 진입에 따른 영향은 지역내 중소상공인 뿐만 아니라 지역사회의 중요한 쟁점사항이 된다. 또 진입하는 대형슈퍼마켓의 입장에서도 신규시장에서 새로운 고객층의 확대 여부는 진입타당성 검토시 매우 중요한 요소가 된다. 본논문에서는 이런 경우 소비 자의 소매유형 선택에 대한 정확한 예측이 매우 중요해진다고 할 수 있다. 본 논문에서는 최근 등장하고 있는 기계학습방식(Machine Learning Method)중 로지스틱 (Logistic) 회귀모형, 의사결정나무(Decision Tree), 랜덤포레스트(Random Forest), 부스팅(Boosting), 신경망(Neural Network) 등의 예측력을 비교하여 소비자의 소매유형(전통시장과 대형슈퍼마켓)의 선택 및 교차쇼핑(Cross Shopping)의 정도를 예측하는 모형을 설정하고자 하였다. 훈련자료를 통 해 각 방식별 최적모형을 도출한 후 테스트자료의 부트스트랩을 통해 각각의 예측력을 비교한 결과 랜덤포레스트와 부스팅과 같은 앙상블(Ensemble)방식은 예측력의 우월성 측면에서 다른 기계학습 방식들은 물론 기존 선형모형들과의 비교할 때 유의적인 차이를 보이고 있다.

키워드

소매유형선택, 교차쇼핑, 기계학습

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