고객반응성을 고려한 고객세분화 방법에 관한 연구 -농촌체험관광 만족도 영향요인을 중심으로- = The Segmentation Based on the Customer Response -with Factors Influencing the Satisfaction of Farm Tourism-

국문 초록 (Abstract)

본 연구에서는 시장세분화 방법으로써 Mixture Regression방법을 농촌체험관광에 적용하여 결과를 비교함으로써, 일반적으로 사용되는 K-means 방법에 대한 새로운 대안으로써 Mixture Regression방법에 의한 시장세분화 방법을 제시하고자 하였다. K-means과 Mixture Regression 방법을 적용하여 농촌체험관광객들을 세분하여 본 결과, 두 방법에 의해 도출된 결과는 서로 상이한 기술적, 통계적 특성을 나타내었다. 세분화된 집단의 수가 3개 이상인 경우 K-means방법에 의해 구분된 집단에서는 농촌체험관광 만족에 영향을 주는 체험품질요인들의 통계적 유의성이 낮아진 반면에, Mixture Regression에 의해 구분된 집단에서는 변수간의 통계적 유의성이 크게 낮아지지 않았다. 또한 모형의 설명력에 있어서도 수리적인 방법을 이용한 K-means 방법에 의해 형성된 집단의 모형 설명력은 상당히 저조한 반면에, Mixture Regression모형에 의해 형성된 집단의 모형 설명력은 상대적으로 높은 것으로 나타났다. 본 연구결과는, 단순히 시장을 기술적인 변수들을 이용하여 유사한 집단으로 구분하는 데는 K-means 분석방법을 이용한 세분화 방법이 비교적 용이하고, 효과적인 반면에, 세분화된 집단에서 시장반응성에 영향을 주는 요인들을 탐색하고, 이를 바탕으로 새로운 고객을 확보하는 데는 Mixture Regression을 이용한 방법이 보다 효과적임을 시사한다. 또한 실무적으로도 인구통계적, 경제사회적 기술변수들만을 이용하여 단순히 유사한 기술적 특성만을 가진 대상자들을 한데 묶음으로써 집단을 세분화 하는 경우에는 재구매도 의도나, 만족도, 구매빈도와 같은 반응성의 차이를 발견하기가 쉽지 않기 때문에, 시장세분화의 목적을 달성하기 위한 시장반응성의 차이, 즉 기준변수의 차이를 발견하기 위해서는 Mixture Regression 방법을 이용하는 것이 보다 효과적이라 할 수 있다.

다국어 초록 (Multilingual Abstract)

In this study, we suggest the mixture regression as a new method of segmenting a market. We discussed its benefits and shortcomings in comparison with typical K-means method. We applied both methods to segmenting the rural tourism market. Unlike K-means method, mixture regression method is a kind of objective oriented method. In this study, we conduct segmentation so as to classify the market into several segments in terms of the level of overall satisfaction. Variables measuring the quality of experience are used as the segmentation bases. The segmentation results of both methods are quite different. The basis variables are not significantly related with overall satisfaction in the segments made by K-means method. However, the variables in the segments made by mixture regression method are significantly related with the overall quality of experience. Since K-means method simply groups the tourists in terms of the similarity of basis variables, the variation of basis variables are not useful in predicting the level of overall satisfaction. K-means method does simply apply the Euclidean distance among variables without considering their associated relationship. However, the mixture regression method classify the market in terms of predictability of overall satisfaction, considering the association among segmentation variables. Thus, the segments done by the mixture regression method are easy to apply marketing strategy related with overall satisfaction. In sum, if there is an objective target variable, the mixture regression method is useful. Otherwise, the simple K-means method is valid for segmentation. However, since the segmentation is an marketing activity preceeding the marketing plan and strategy intended, the mixture regression method would be more appropriate than K-means method in general.

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