• Journal of the Korea Society of Computer and Information
• /
• v.15 no.12
• /
• pp.227-235
• /
• 2010

This paper proposes the recommendation system which is advanced using RFM method and Association Rules in e-Commerce. Using a implicit method which is not used user's profile for rating, it is necessary for user to keep the RFM score and Association Rules about users and items based on the whole purchased data in order to recommend the items. This proposing system is possible to advance recommendation system using RFM method and Association Rules for cross-selling, and also this system can avoid the duplicated recommendation by the cross comparison with having recommended items before. And also, it's efficient for them to build the strategy for marketing and crm(customer relationship management). It can be improved and evaluated according to the criteria of logicality through the experiment with dataset collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system for one to one web marketing in e-Commerce.

• Journal of the Korean Home Economics Association
• /
• v.43 no.3 s.205
• /
• pp.1-10
• /
• 2005

The purpose of this study was to investigate the enforcement strategy for Consumer Service marketing of an e-Commerce shopping mall. An e-CRM for a Cosmetic e-Commerce shopping mall, Data Warehousing(DW) component, analysis of data mining of the DW, and web applications and strategies had to developed for marketing of consumer service satisfaction. The major findings were as follows: An RFM analysis was used for consumer classification, which is a fundamental process of e-CRM application. The components of the DW were web sales data and consumer data fields. The visual process of consumer segmentations (superior consumer class) for e-CRM solutions is presented. The association analysis algorithm of data mining to up-selling and cross-selling indicates an association rule. These e-CRM results apply web DB marketing and operating principles to a shopping mall. Therefore, the system applications of e-CRM to Consumer services indicate a marketing strategy for consumer-oriented management.

• The Korean Journal of Applied Statistics
• /
• v.17 no.2
• /
• pp.229-238
• /
• 2004

Cross-sell models are used to predict the probability or value of a current customer buying a different product or service from the same company. Selling to current customers is one of the easiest way to increase profits and allows companies to carefully manage offers to avoid over-soliciting and possibly alienating their customers. In this study, by using the real database of an insurance company in Korea, we try to explain the steps of actual data mining process. Especially, this study aims to develop cross-sell models to predict the probability which a current customer of automobile insurance buys long-term insurance product.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.249-254
• /
• 2002

기업의 입장에서 가장 중요한 이슈 중 하나는 자사에 있는 많은 고객들 중 회사에 수익을 가져다 줄 수 있는 고객이 누구인가라는 문제이다. 이러한 문제에 대한 기업의 고객 관리 전략 중 하나가 '교차판매(Cross-Selling)' 전략이다. 본 연구에서는 국내 A 손해보험사의 고객 데이터베이스를 활용하여 데이터마이닝프로세스가 어떻게 진행되고 있는지를 실제 프로젝트를 중심으로 설명하고자 한다. 특히, 본 연구에서 목표로 하고 있는 것은 기존의 자동차보험에 가입한 고객 중에서 장기보험에 추가로 가입하는 고객을 설명하기 위한 교차판매 스코어링 모델을 개발하는 것이다.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.61
• /
• pp.51-58
• /
• 2000

Discovery of association rules has been used useful in many fields, especially in the fields of the inventory display, catalog design and cross selling. In previous works, all transactions In the database are treated uniformly. In this paper, we present a method for partitioning transactions in the database using time weights. Transactions are assigned different weights as time goes on. Examples show that these method provides purchasing patterns in the database as well as finding association rules.

• Journal of Intelligence and Information Systems
• /
• v.16 no.2
• /
• pp.1-17
• /
• 2010

Association rules among product items by association analysis suggest sales effect among products. These are useful for marketing strategies such as cross-selling and product display etc. However, if we evaluate more practical product values reflecting cross-selling effects, they will be also more useful for the decisions of companies such as product item selection for product assortment and profit maximization etc. This study proposes product value evaluation models with the concept of effective value based on single-item association chain and itemset association chain. In addition to that, we performed experiments with transaction data related to clothing of an online shopping mall in Korea to show the performances of our models. In result, we confirmed that some items increased in effective values compared with their pure values while the others decreased in effective values.

• The Journal of the Korea Contents Association
• /
• v.8 no.6
• /
• pp.26-33
• /
• 2008

The customer relation marketing in which companies can utilize to control and to get the filtered information efficiently has appeared in ubiquitous commerce. It is applying data mining technique to build the management that can even predict and recommend products to customers. In this paper, we proposed the case of customer relation management application using the associative mining. The proposed method uses the associative mining composes frequent customers with occurrence of candidate customer-set creates the association rules. We analyzed the efficient the feature of purchase customers using the hypergraph partition according to the lift of creative association rules. Therefore, we discovered strategies of the cross-selling and the up-selling. To estimate the performance, the suggested method is compared with the existing methods in the questionnaire dataset. The results have shown that the proposed method significantly outperforms the accuracy than the previous methods.

• Journal of the Korean Society of Costume
• /
• v.51 no.5
• /
• pp.29-43
• /
• 2001

Database marketing is a series of marketing activities based on the customer database for increasing the customer's life-time value. In this thesis. we applied database marketing to the sales promotional strategies of Casual Korean Traditional Clothes to activate wearing of Casual Korean Traditional Clothes. To achieve this goal, we surveyed the consciousness of wearing and purchases for Casual Korean Traditional Clothes. and extract information that can be utilized in the sales promotional strategies. According to the result, the proposed sales promotional strategies for Casual Korean Traditional Clothes are summarized as follows : (1) Useful information for the customer should be stored in the database and utilized in the marketing. (2) It is necessary to shorten the cycle of repeated purchases by emphasizing daily-life clothing of Casual Korean Traditional Clothes especially for the aged 20-40. (3) Since Casual Korean Traditional Clothes are usually weared as a ceremonial clothes in the fall, direct mail, fashion show, and advertising in the mass media should be concentrated on this season. (4) Value-added marketing should be derived by cross-selling of items harmonized with Casual Korean Traditional Clothes. (5) To guarantee fixed customers and increased usage of Casual Korean Traditional Clothes, - give point score, discount, or selling on an installment basis for the customers who use credit cards or department cards. - select privileged customers by analyzing purchase history and provide multiple services for these customers. - let the customers rent Casual Korean Traditional Clothes in an appropriate cost, and make customer cards for the construction of elaborated customer database. (6) To increase the acknowledgement of Casual Korean Traditional Clothes, not only Persistent publicity, but also fashion show, visual merchandising, and advertisement in mass media should be conducted as well.

• Proceedings of the Korea Contents Association Conference
• /
• 2008.05a
• /
• pp.84-86
• /
• 2008

The customer relation marketing in which companies can utilize to control and to get the filtered information efficiently has appeared. It is applying data mining to build the management that can even predict and recommend products to customers. In this paper, we proposed the adaptive customer relation management strategies using the association rules of data mining. The proposed method uses the association rules composes frequent customers with occurrence of candidate customer set creates the rules of associative customers. We analyzed the efficient feature of purchase customers using the hyper graph partition according to the lift of creative association rules. Therefore, we discovered strategies of the cross-selling and the up-selling about customers.

• Asia pacific journal of information systems
• /
• v.16 no.4
• /
• pp.217-238
• /
• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.