Identifying consumer buying behavior differences through market basket analysis in multiple outlet types

Nurfitriana Tri Utami, Isti Surjandari Prajitno

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 International Conference on Business and Information Management, ICBIM 2017
PublisherAssociation for Computing Machinery
Pages82-86
Number of pages5
ISBN (Electronic)9781450352765
DOIs
Publication statusPublished - 23 Jul 2017
Event2017 International Conference on Business and Information Management, ICBIM 2017 - Beijing, China
Duration: 23 Jul 201725 Jul 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F131932

Conference

Conference2017 International Conference on Business and Information Management, ICBIM 2017
Country/TerritoryChina
CityBeijing
Period23/07/1725/07/17

Keywords

  • Apriori Algorithm
  • Association Rule
  • Customer Buying Behavior
  • Data Mining
  • Market Basket Analysis

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