• Journal of Information Processing Systems
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• 제19권2호
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 한국의류산업학회지
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• 제18권6호
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• pp.800-811
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• 2016

This study aims to extract the multi-channel attributes of Dongdaemun-based fashion brands and consider the effects of these attributes on customer equity, customer satisfaction and re-purchase intention. In total, 493 samples of those who have purchased Dongdaemun-based fashion brand products using multi-channels were collected for the final data analysis, which was performed using SPSS 21.0 and AMOS 18.0. The findings of study are as follows. Among the multi-channel attributes of Dongdaemun-based fashion brands, entertainment and informativeness had a significant effect on all customer equity drivers. In terms of the effects of customer equity drivers on customer satisfaction and re-purchase intention, all customer equity drivers significantly influenced customer satisfaction, while brand equity significantly influenced re-purchase intention. Also, customer satisfaction significantly affected re-purchase intention. In the effective relationship between customer equity drivers and CLV, brand equity causes a significant influence on CLV amongst the customer equity drivers. There were significant differences among groups following the multi-channel shopping orientation of consumers. This study is significant for its scientific focus on the distribution channels of Dongdaemun, and in terms of the practical aspect of identifying the multi-channel attributes considered to be important to consumers. Measuring customer equity will suggest implications about the long-term direction of the development of Dongdaemun-based fashion brands.

• 한국경영과학회지
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• 제39권3호
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• pp.63-82
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• 2014

The main objective of this research is to investigate whether the RFM (recency-frequency-monetary value) information of a customer's redemption behavior of loyalty points can improve the prediction of future value of the customer. The conventional measurement of customer value has been primarily based on purchase transactions behavior although a customer's future behavior can be also influenced by other interactions between the customer and the firm such as redemption of rewards in a loyalty program. We theorize why a customer's redemption behavior can influence her future purchases and thereby the customer's total value based on operant learning theory, goal gradient hypothesis, and lock-in effect. Using a dataset from a major book store in Korea spanning three years between 2008 and 2010, we analyze both purchase transactions and redemption records of over 10,000 customers. The results show that the redemption-based RFM information does improve the prediction accuracy of the customer's future purchases. Based on this result, we also propose an improved estimate of customer lifetime value (CLV) by combining purchase transactions and loyalty points redemption data. Managerial implications will be also discussed for firms managing loyalty programs to maximize the total value customers.

• 지식경영연구
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• 제15권3호
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• pp.121-140
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• 2014

Many firms have been utilizing SNSs such as Facebook and Twitter actively in order to boost interactions with customers that promote product and service innovations and effective marketing. Although positive outcomes of the customer interactions in SNSs are expected, there exist few studies on the effects of interactions between customers and firms in the SNS context. This study empirically examines how customer experiences in multi-dimensional interactions (i.e., pragmatic, sociability, usability, and hedonic interaction) in corporate SNSs influence customer trust in the firm, and how customer learning benefits are associated with firm benefits such as gaining customer trust. The results indicate that all four dimensions of customer interactions in SNSs have significant effects on customer learning benefits, which in turn significantly influence customer trust in the firm. Meanwhile, the results reveal that there are also direct relationships between specific dimensions of customer interactions in SNSs and the two dimensions of customer trust (i.e., ability-based and benevolence/integrity-based). Based on the findings, this study diagnoses the status of corporate SNSs in terms of collaboration with customers and provides practical implications for firms which attempt to capitalize on the multi-dimensional customer interactions in SNSs and to facilitate innovative activities with customers.

• 유통과학연구
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• 제22권2호
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• pp.51-61
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• 2024

Purpose: This comprehensive study delves into the intricate relationship between customer engagement, perceived risk, and perceived value within China's burgeoning e-commerce livestreaming sector. It focuses on how different customer engagement types in livestreaming influence their perception of value and risk. Research Design, Data, and Methodology: Adopting a convenience sampling approach, this research scrutinizes data collected from 852 consumers actively involved in e-commerce livestreaming shopping. Participants provided their insights through a meticulously designed questionnaire survey. Structural equation modeling helped examine the interplay between customer engagement, perceived risk, and value. Results: Significant impacts of customer engagement on perceived value and risk were found. Observation-based, conversation-based, and action-based engagements enhance perceived risk, while conversation-based and action-based engagement reduce perceived risk. Interestingly, observation-based engagement did not significantly affect perceived risk. The study also uncovered that perceived risk negatively impacts perceived value. Conclusions: The research offers insights into customer behavior and value creation in e-commerce livestreaming. It underscores how different engagement types affect perceived value and risk, aiding e-commerce platforms and businesses in strategy development to improve customer experience and minimize risks, enhancing perceived value in this dynamic sector. Enhances understanding of customer engagement dynamics in China's e-commerce livestreaming, guiding strategic development.

• 스마트미디어저널
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• 제6권4호
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• pp.17-23
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• 2017

오늘날 사업장들은 고객의 소비활동을 촉진하기 위해 다양한 방법의 IT기반 서비스를 이용하고 있다. 대표적으로 고객이 해당 사업장 근처에 접근했을 때 광고 문자나 할인쿠폰을 보내는 위치기반 서비스를 예로 들 수 있다. 이것은 사업장과 고객간의 상호작용이 아닌 사업장의 광고 내용을 고객의 의사와는 관계없이 일방적으로 고객의 스마트폰에 전송하는 형태이며 사업장은 어떤 고객에게 해당 광고내용을 전송했는지 실시간으로 알 수 없다. 현재 대부분의 서비스가 이러한 형태에 속한다. 따라서 사업장과 고객간의 상호작용이 이루어지는 새로운 형태의 위치기반 고객 서비스가 필요하다. 이것은 고객이 사업장을 방문했을 때 어떤 고객이 어떤 사업장을 방문하였는지를 자동으로 식별하여 해당 사업장의 컴퓨터에 실시간으로 전달하고, 사업장은 해당 정보를 이용하여 고객 맞춤형 서비스를 제공하는 시스템이다. 본 논문에서는 비콘을 이용하여 사업장과 고객간의 상호작용 및 고객식별을 위한 서비스 플랫폼을 개발한다. 또한 특정한 하나의 사업장에 제한된 것이 아닌 다양한 사업장과 고객들이 손쉽게 접근할 수 있는 오픈 플랫폼 환경을 제공한다.

• 한국의류산업학회지
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• 제17권6호
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• pp.942-955
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• 2015

Hashtag has emerged and become one of cultural trend. Given that more and more firms in the fashion industry are using hashtag on images based on SNS to provide information of their products and to communicate with their customers. Especially, hashtags through voluntary participation of users provides the perspective of how customers consume their products. Therefore, this study focused on the using motives of hashtag in image based SNS with customer social participation as mediator towards brand equity. The purpose of this study is (1) to investigate the usage motivation of hashtag of image contents based SNS, (2) to expose how each usage motive affects customer social participation and (3) to find out how customer social participation has an effect on brand equity. In order to achieve the objectives of this study, first we conducted an in-depth interview on 8 image based SNS heavy users to understand the using motives of hashtags. Furthermore, we conducted online surveys amongst people aged between 20s and 30s of image contents based SNS users. As a result of this study, followings were figured out. First, four of usage motivation of hashtag were examined through in-depth interview and previous studies; interest sharing, social interaction, ease of use and enjoyment. Second, usage motivation of hashtag has a significant effect on customer social participation. Third, customer-media participation and customer-customer participation impact positively on brand equity. Lastly, level of customer social participation has the moderating effect on the relationship between motivation of hashtag and customer social participation.

• 한국산학기술학회논문지
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• 제19권3호
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• pp.260-266
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• 2018

고객의 기술 채택에 영향을 미치는 고객효용도의 개념에 대한 이해는 다세대 기술의 확산 및 대체 과정을 이해하는데 중요하다. 본 연구에서는 다세대 기술의 속성 기반 고객효용도의 개념에 대해 정의하고, 고객효용도를 측정할 수 있는 모형을 개발하였다. 문헌리뷰 및 모형화를 바탕으로 다세대 기술의 속성 기반 고객효용도에 대해 정의 및 측정 모형을 제시하였고, 도출한 모형의 정합성을 반도체 산업 사례를 바탕으로 검증하였다. 다세대 기술에서 속성 기반 고객효용도는 세대별로 또는 같은 세대 내에서 시간별 변화를 고려해야하고, 기술적 속성과 경제적 속성에 대해 가중치를 고려한 모든 효용도들의 합으로 정의된다. 또한 속성 기반 고객효용도는 효용도 변환표를 통해 속성들의 값을 효용도로 전환한 후 가중치를 고려한 모든 속성들의 효용도의 합으로 모형화 가능하다. 본 연구를 통해 다세대 기술이 확산 및 대체되는 과정에서 고객의 기술 채택의 근본 동인으로서 영향을 미치는 고객효용도에 대해 이해 가능하고, 고객효용도를 바탕으로 확산 및 대체 경로를 예측하여 기술전략을 수립하는데 유용할 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제32권1호
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• pp.201-221
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• 2023

Purpose Customers consider the overall experience with the company as important as the quality of the product, and companies are also paying attention to creating long-term relationships with customers through optimal customer experiences. In this study, we propose a customer experience management process called 'CX-Q', which combines customer journey map and Q-methodology to understand the importance of customer experience based on the overall customer experience. Design/methodology/approach. CX-Q is a process that combines Q-methodology and customer journey maps, allowing stakeholders to explore and improve customer experiences at each contact point while engaging with brands, products, and services. It also enables them to derive customer experience insights and important management points for each segment. To demonstrate the usefulness of the proposed CX-Q, this study analyzed the experience of customers who used the Airbnb travel platform service as an example, applying the CX-Q process. Findings A total of four customer segments were derived, and it was found that each segment valued different attributes during the customer journey stage. The customer experience analysis using the CX-Q process proposed in this study is expected to help understand customers in more detail and assist in managing and improving customer experience.

• 한국정보시스템학회지:정보시스템연구
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• 제27권3호
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• pp.63-80
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• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.