TY - GEN
T1 - Identifying consumer buying behavior differences through market basket analysis in multiple outlet types
AU - Utami, Nurfitriana Tri
AU - Prajitno, Isti Surjandari
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/23
Y1 - 2017/7/23
N2 - Retail business in Indonesia is growing rapidly and it causes a sales competition conducted by retailers. Therefore, a strategy is needed to expand the consumer market. In order to expand the current market, companies need to pay attention to customer satisfaction that will affect the sustainability of product purchasing activities. Market basket analysis is done to extract consumer buying behavior by identifying the associations of various products that consumers put on the shopping basket. This study was conducted to observe whether outlet type affects consumer buying behavior. There are two types of outlets based on their placement locations, which are outlets located within shopping centers and stand-alone outlets. The data used in this study was taken from customer purchase transactions database. Since the purchase transactions made by consumers were large, then data was processed using data mining techniques. Apriori algorithm was applied for finding frequent item-set. The minimum support used in data processing was 12%, while the minimum confidence used was 70%. Based on these results, it can be concluded that the pattern of consumer purchases in outlets within the shopping center was making a purchase with a few number of product category combinations, while in stand-alone outlets were engaged in transactions with the number of combinations of more product categories. The difference can be observed from the association pattern generated from each type of outlet. By knowing the pattern of consumer purchases at each outlet viewed from the product association, the company can determine the right strategy to increase the market for each type of outlet.
AB - Retail business in Indonesia is growing rapidly and it causes a sales competition conducted by retailers. Therefore, a strategy is needed to expand the consumer market. In order to expand the current market, companies need to pay attention to customer satisfaction that will affect the sustainability of product purchasing activities. Market basket analysis is done to extract consumer buying behavior by identifying the associations of various products that consumers put on the shopping basket. This study was conducted to observe whether outlet type affects consumer buying behavior. There are two types of outlets based on their placement locations, which are outlets located within shopping centers and stand-alone outlets. The data used in this study was taken from customer purchase transactions database. Since the purchase transactions made by consumers were large, then data was processed using data mining techniques. Apriori algorithm was applied for finding frequent item-set. The minimum support used in data processing was 12%, while the minimum confidence used was 70%. Based on these results, it can be concluded that the pattern of consumer purchases in outlets within the shopping center was making a purchase with a few number of product category combinations, while in stand-alone outlets were engaged in transactions with the number of combinations of more product categories. The difference can be observed from the association pattern generated from each type of outlet. By knowing the pattern of consumer purchases at each outlet viewed from the product association, the company can determine the right strategy to increase the market for each type of outlet.
KW - Apriori Algorithm
KW - Association Rule
KW - Customer Buying Behavior
KW - Data Mining
KW - Market Basket Analysis
UR - http://www.scopus.com/inward/record.url?scp=85038356837&partnerID=8YFLogxK
U2 - 10.1145/3134271.3134290
DO - 10.1145/3134271.3134290
M3 - Conference contribution
AN - SCOPUS:85038356837
T3 - ACM International Conference Proceeding Series
SP - 82
EP - 86
BT - 2017 International Conference on Business and Information Management, ICBIM 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Business and Information Management, ICBIM 2017
Y2 - 23 July 2017 through 25 July 2017
ER -