• 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.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• 한국IT서비스학회지
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• 제16권3호
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• pp.113-125
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• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.