• Journal of Distribution Science
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• v.20 no.6
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• pp.1-10
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• 2022

Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.

• Asia Marketing Journal
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• v.1 no.2
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• pp.19-35
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• 1999

데이터베이스 마케팅 전략을 수립하고 집행함에 있어서 고객에게 접근하기 위한 촉진 매체로써 직접우편(Direct Mail)과 텔레 마케팅 등의 직접반응매체를 주요 수단으로 하는 경우 이를 다이렉트 마케팅이라고 한다. 다른 마케팅 전략들과 마찬가지로 다이렉트 마케팅에서도 마케팅 자원이 효과적으로 사용될 수 있도록 고객 데이터베이스를 세분화하는 작업을 수행한다. 리스펀스 모델링(Response Modeling)은 다이렉트 마케팅분야에서 고객리스트를 세분화하고 각 세그멘트별로 고객의 반응(구매행위)을 예측하는 기법을 말하며 RFM(Recency, Frequency, Monetary), 로지스틱, 신경망은 리스펀스 모델링을 위해서 가장 널리 사용되고 있는 기법이다. 과거에 이들 방법은 고객 데이터베이스 전체에 단독 모델로 적용되어 왔으나 이러한 단독 모델을 고객 데이터베이스에 적용하는 것이 정당화 되려면 고객들이 동일한 방식으로 반응한다는 전제가 필요하다. 그러나 일반적으로 고객의 반응방식에는 상당한 이질성이 존재한다. 예컨대 직업, 나이, 소득, 성별 등이 같다고 해서 같은 구매패턴을 보이지는 않는다는 것이다. 즉 고객A의 구매행위는 회귀선에 의해서 잘 설명되는 반면에 고객B는 신경망이나 RFM으로 잘 설명될 수 있는 경우가 존재하는 것이다. 이러한 구매행위의 이질성을 반영하기 위해서 최근에는 두개 이상의 방법을 결합하여 사용하는 결합 리스펀스 모델링 방법도 시도 되어 왔다. 그러나 결합 리스펀스 모델링에 관한 기존 연구들은 상관관계가 낮은 모델들을 결합함으로써 세분화의 효과를 단독 모델을 사용할 때 보다 개선할 수 있다고는 하였으나 구체적으로 어떤 모델들이 서로 낮은 상관관계를 갖는지는 보여주지 못하였다. 본 논문에서는 RFM 방법을 모델 내에서 사용하는 변수와 이를 이용한 모델링 방법상의 차이로 인하여 다른 두 방법(로지스틱, 신경망)과 매우 낮은 상관관계를 갖는 방법으로 제시하고 RFM과 다른 두 방법간의 낮은 상관관계를 이용하여 결합하는 경우 모델의 예측효과를 상당히 개선할 수 있음을 사례분석을 통해서 보이고자 한다.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Korea Journal of Hospital Management
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• v.17 no.2
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• pp.21-33
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• 2012

Purpose : This study aims to suggest application of patients DB to hospital marketing by performing market segmentation and selecting target market. Consequently help to establish suited strategy of marketing. Method : 14,072 patients hospitalized in a University Medical Center were recruited into this study. In order to classify the customer groups, cluster analysis was used with RFM(Recency, Frequency, Monetary) model, and 1-way ANOVA verified the differences among groups. And then, sociodemographical status, healthcare utilization and diagnosis(ICD-10) of each group were compared to draw a marketing strategy. Results : Four groups were classified through clustering analysis, and'high use and high profit' and'low use and high profit' groups were selected as a target market. The features of target market were as follows, the female proportion was high; used a private room; hospitalized through the emergency room; had operation; length of stay was long; had many comorbidity and cooperative treatment. There was difference in each feature of target market: as for the'high use and high profit' group, many patients were diagnosed with 'certain infectious and parasitic diseases'; and as for the'low use and high profit'group, the proportion of patients who purchased'industrial accident compensation insurance'and'auto insurance'was relatively high; many patients were diagnosed with'Injury, poisoning and certain other consequences of external causes'. Conclusion : It is needed to establish'positioning' strategy by monitoring and communicating with'high use and high profit' group. And for the case of'low use and high profit' group, it is necessary to make a follow-up management and lead them to have a medical check-up.

• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.125-144
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• 2004

The purpose of this study is to classify customer bye-mailing responsiveness on time-series analysis and RFM module and testify the effectiveness of grouping by ROI analysis. RFM (Recency, Frequency, Monetary Value) analysis are used for customer classification that is fundamental process of e-CRM application. ROI analysis were consisted of open, click-through, duration time, conversion rate, personalization and e-mail loyalty index. Major findings are as follows; Customer segmentation were loyal customer, odds customer, dormant customer, secession customer and observation customer by Activity email module. And Loyal, dormant and secession customer are segregated by RFM module. Loyal customer group have higher point of all ROI index than other groups. These results indicated that customer responsiveness of e-mailing and RFM analysis were appropriate methods to grouping the customer. Mid-small Internet Biz adapted marketing strategy by optimization of consumer information.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.107-129
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• 2014

Providing reliable and high quality information sources will be one of the basic skills of librarians in the future. Therefore, this study proposed evaluation criteria for health-related information sources based on a survey of public and medical librarians. As a result, a total of 21 items were selected as evaluation items, in three groups. The first, the health information content group, had 13 evaluation items, including accuracy, recency, medical expertise, regular updates, consideration of audience, objectivity, ease of understanding, plain (non-scientific or technical) language, completeness, relevance to the topic, verifiability, citation of information sources, and specification of precautions or warnings. The second group, the health-information sources group, had 5 evaluation items including clarity of health information for achieving its purpose, clarification of the responsibility of health information, compliance to the privacy policy, fairness of health information providers, and ethics of health information providers. The third group was the health-information website design group, and featured 4 evaluation criteria: ease of access, search capabilities, website ease of use, and query-response services.

• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.11-18
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• 2006

In the Internet of various networks, it is difficult to reduce real routing time by just minimizing their hop count. We propose an adaptive proximity route selection method in DHT-based peer-to-peer systems, in which nodes select the nぉe with smallest lookup latency among their routing table entries as a next routing node. Using Q-Routing algorithm and exponential recency-weighted average, each node estimates the total latency and establishes a lookup table. Moreover, without additional overhead, nodes exchange their lookup tables to update their routing tables. Several simulations measuring the lookup latencies and hop-to-hop latency show that our method outperforms the original Chord method as well as CFS' server selection method.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.113-124
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• 2017

The existing social search has a problem that search results are not suitable for a user since it does not take into account the recency of the user profile and the interests of similar users. Therefore, studies on a social search considering a temporal attribute and the interests of other users are required. In this paper, we propose a social search scheme that takes into account the recent interests of a user by time and the interests of the most similar users. The proposed scheme analyzes the activity information of a social media user in order to take into account the recent interests of the user. And then the proposed scheme improves the satisfaction and accuracy of search results by combining the interests of similar users with the analyzed information and performing ranking, It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.

• Spatial Information Research
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• v.22 no.2
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• pp.73-87
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• 2014

The fact that the limits of information recency, diversity, and usability are mainly caused by the supply oriented geospatial data and service development is commonly recognized. It is recently tried to overcome the limits by facilitating user experience and VGI(Volunteered Geographic Information) in several geospatial web services. This study suggests 10C framework of geospatial web service for VGI through review and examination of previous research. Based on the 10C framework, the value chain system of 23 use cases relevant to the geospatial web service involving the creation of user's VGI is investigated. The result of the value chain analysis is applied to examine and formulate the strategies to generate value addition from public spatial information with respect to creation, aggregation, delivery, and consumption process of VGI.