연관규칙 분석을 활용한 소비자의 농식품 구매 패턴 분석 = A Study on Consumers’ Purchasing Pattern for Agri-food Using Association Rules Analysis

다국어 초록 (Multilingual Abstract)

As the purchasing environment of agri-food changes, consumers‘ needs are also changing. Due to the development of distribution, storage technologies and improved varieties, the agri-foods that consumers encounter are becoming more diverse. Purchasing places for agri-foods are also diversified into large sized marts, SSM, convenience stores, online purchases, etc. Generally, consumers buy several other items at the same time when purchasing agri-foods. Therefore, if the relationship between the agri-food products purchased by consumers is found out, it can be utilized as a marketing strategy.
Nowadays, we can accumulate data on consumers' purchasing behavior, both online and offline. Data mining is a method of collecting and analyzing large amounts of data. Data mining applies to a variety of areas such as retail business, stocks, tourism, and banking, but it is difficult to find any research that uses this method in food demand analysis.
In this study, we used the association rule analysis, which is one of the data mining techniques. We analyzed the purchasing relevance of agri-food purchased by consumers and then we used the result as a marketing strategy. The analysis results are classified into common rules, frequently appeared rules, generated rules, and extinct rules. The results of the analysis are summarized as follows.
First, there are association rules that appear annually or frequently.
Second, the association rules appearing in the same purchasing place differ according to the age group.
Third, the association rules of the same age group are different from place to place.
Fourth, Lift of association rules can determine the change of relation of associated purchasing items.
Based on these results, marketing strategies can be established. More specifically, we could develop bundled products, organize stores, or promote sales through discount events with association rules. Furthermore, we will be able to recommend personalized products to customers based on their purchase history data.

목차 (Table of Contents)

• Ⅰ. 서 론 1
• 1. 연구 배경 1
• 2. 선행연구 고찰 3
• 3. 연구 목적 3
• Ⅱ. 연구방법 5
• 1. 연관규칙 분석의 주요 개념 5
• 2. 분석 자료와 절차 8
• 1) 분석자료 8
• 2) 분석 절차 10
• Ⅲ. 분석결과 13
• 1. 연관규칙 분석 결과의 해석 13
• 2. 연도별 연관규칙 수의 변화 15
• 3. 연령대에 따른 연도별 연관규칙 변화 16
• 1) 전체 연령 16
• 2) 50대 19
• 3) 40대 22
• 4) 30대 24
• 4. 공통 연관규칙의 향상도(Lift) 변화 26
• 1) 겨울 26
• 2) 봄 28
• 3) 여름 29
• 4) 가을 30
• Ⅳ. 요약 및 결론 32
• 참고문헌 35