• Asia pacific journal of information systems
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• 제26권1호
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• 지능정보연구
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• 제11권3호
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• pp.69-81
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• 2005

고객관계관리는 치열한 경쟁환경에서 각 기업이 생존하기 위해 반드시 필요한 하나의 기업전략이 되었다. 고객관계관리의 방법은 다양하지만 가장 기본적인 방법은 특정 고객이 어떤 상품 혹은 상품군을 구매할 것인지를 정확히 예측하는 것이다. 이미 국내외 실무현장에서 전통적인 데이터마이닝 기법을 활용한 고객구매예측모형이 널리 적용되고 있다. 하지만 전통적인 기법의 경우, 정확도가 상대적으로 떨어지거나 혹은 모형의 구축 및 유지관리가 어렵다는 문제가 종종 제기되어 왔다. 이에 본 연구에서는 기존 모형의 문제점을 개선하기 위한 대안으로, 매우 높은 예측력을 나타내면서 동시에 일반화 능력이 우수한 것으로 알려진 Support Vector Machine(SVM)을 이용하여 고객구매예측모형을 구축하고자 한다. 본 연구에서는 고객구매예측의 도구로써 SVM의 적합성을 판단하기 위하여 전통적인 기법인 로지스틱 회귀분석, 인공신경망과 그 성과를 비교하였다. 그 결과, SVM이 다른 기법들에 비해 상대적으로 우수한 성과를 나타냄을 확인할 수 있었다.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.157-163
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• 2023

The current research aims to examine the moderating effect of consumers' mindset on their product purchase intention in the multi-promotion offers containing both a bonus pack and a price discount (i.e., BP + PD offers). That is, this research investigateswhether consumers' product purchase intention in the BP + PD offers variesdepending on their mindset (growth mindset vs. fixed mindset). Specifically, it is predicted that consumers with a fixed mindset will have higher product purchase intention in the offers containing the high PD but low extra amount of BP (LBP HPD) than in the offers with a high extra amount of BP but low PD (HBP LPD), whereas consumers with a growth mindset will have higher product purchase intention in the HBP LPD offers than in the LBP HPD offers. An experiment wasconducted to test the prediction. Consistent with the prediction, it was found that participants' mindset moderates their product purchase intention in multi-promotion offers. The findings imply that marketers can evoke more positive consumer purchase intention toward BP and PD offers, considering consumer mindset.

• 한국공간정보시스템학회 논문지
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• 제11권4호
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• pp.74-84
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• 2009

이 연구는 국내 우편물량 감소와 우편사업 경쟁력 강화를 위하여 GIS 및 시공간 마이닝 기술을 이용한 GIS 기반의 새로운 우편 마케팅 기법을 제안한다. 홍보를 원하는 기업체에게 의미 있고 정확한 마케팅 정보 제공을 위해서 Geo-Lifestyle 군집화를 적용한 인구 사회학적 마켓 세분화 기법과, 시간 공간 차원의 다차원적 분석을 통한 시공간 구매 소비 성향 예측 기법을 제안하였다. Geo-Lifestyle 군집분석 및 시공간 큐브 마이닝의 평가를 위해서 강남구, 송파구 지역의 내부 외부데이터를 사용하였고, 실험결과 14개의 최적 마케팅 클러스터를 생성하였으며 구매 소비 성향 예측을 위한 시 공간 패턴을 추출하였다.

• 한국인공지능학회지
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• 제9권1호
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• 지능정보연구
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• 제16권4호
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• pp.213-225
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• 2010

본 연구에서는 기업의 마케팅 프로모션에 따른 반응고객의 구매액 예측을 위한 방법을 제시하고 SVR의 효과적인 학습방법을 제시하였다. 프로모션에 의한 고객의 구매액을 기반으로 고객을 5등급으로 등급화하고 각 등급 내에서 SVR을 적용하여 고객의 구매액을 예측하였다. 본 연구에서 제안하는 예측된 고객의 등급 내에서 고객 구매액을 예측하는 분리데이터 학습법이 프로모션에 반응한 모든 고객을 대상으로 구매액을 예측하는 전체데이터 학습법보다 높은 예측성과를 보여주었다. 일반적으로 세분화된 고객집단을 하나의 집단으로 보고 동일한 마케팅 전략을 제시하나 본 연구를 통해 구매액에 따라 등급화 된 고객의 등급 내에서 다시 고객의 거래 구매액을 예측하여 동일한 집단 내에서도 차별화된 마케팅 전략을 제시할 수 있는 기반을 제시하였다. 즉 동일한 등급에서도 고객 구매액에 따라 고객의 우선순위를 정할 수 있으며, 이는 마케팅 담당자가 프로모션을 제시할 고객을 선정할 때 유용한 정보로 활용될 수 있다.

• 한국의류학회지
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• 제22권5호
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• pp.617-627
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• 1998

In marketing reserach, the ultimate goal is to increase predictability of consumer's purchse behavior. However, most of the marketing researchers measure purchase intention rather than behavior assuming that the consumer's purchase behaviors will coincide with their intentions. Lately, there have been many arguments whether purchase intention is meaningful as a determinant or a predictor for purchase behavior. Additionally, many studies reported substantial variations among the 'product categories. The purpose of this study was to find out the relationship between consumer's purchase intention and purchase behavior of apparel products. Two research questions were set up. One was to find out the relationship between the intention and behavior, and the other was to find out the factors affecting the relationship. It was found out that mere intention could neither explain nor predict behavior, and that the factors affecting intenting intention- behavior inconsistency had to be submitted for explanation and prediction. These factors could explain inconsistency between the intention and the behavior. Regression equations in the past researches explained aggregate results, but coludn't explain each consumer's intention-behavior inconsistency. The integration of the specific intention and the affecting factors can increase predictability of each consumer's purchase behavior.

• Journal of Multimedia Information System
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• 제5권3호
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• 한국정보시스템학회지:정보시스템연구
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• 제30권2호
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• pp.105-126
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• 2021

Purpose The purpose of this study is to implement a optimal machine learning model about the cancellation prediction performance in car sales business. It is to apply the data set of accumulated contract, cancellation, and sales information in sales support system(SFA) which is commonly used for sales, customers and inventory management by imported car dealers, to several machine learning models and predict performance of cancellation. Design/methodology/approach This study extracts 29,073 contracts, cancellations, and sales data from 2015 to 2020 accumulated in the sales support system(SFA) for imported car dealers and uses the analysis program Python Jupiter notebook in order to perform data pre-processing, verification, and modeling that is applying and learning to Machine learning model after then the final result was predicted using new data. Findings This study confirmed that cancellation prediction is possible by applying car purchase contract information to machine learning models. It proved the possibility of developing and utilizing a generalized predictive model by using data of imported car sales system with machine learning technology. It can reduce and prevent the sales failure as caring the potential lost customer intensively and it lead to increase sales revenue by predicting the cancellation possibility of individual customers.

• Journal of Information Technology Applications and Management
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• 제24권1호
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• pp.111-128
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• 2017

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.