Analysis of Purchase History Data Based on a New Latent Class Model for RFM Analysis

Qian Zhang,Haruka Yamashita,Kenta Mikawa,Masayuki Goto 대한산업공학회 2020 Industrial Engineeering & Management Systems Vol.19 No.2

Recently, it has become easier to make use of various kinds of information on customers (e.g. customers’ purchase history), due to the development of information technology. Especially in the marketing field, in fact, many compa-nies try to employ customer segmentation for the services customization which leads to increase customer loyalty and to keep high customer retention. One of the well-known approaches for the customer analysis based on purchase history data is the RFM analysis. The RFM analysis is usually used to segment customers into several groups by using three variables; how long it has been since their last purchase, how many times they purchased, and how much they spent. However, the conventional method of the RFM analysis did not assume a generative model. Therefore, when applying to an actual data set and scoring each index of R, F, M scores, several problems occur. The main problem is that an analyst should arbitrarily decide the threshold for the scores of RFM. On the other hand, in the field of ma-chine learning, the probabilistic latent semantic analysis is widely used for soft clustering. The latent class model ena-bles us to cluster customers into latent classes and to calculate the assignment probabilities of each customer to each latent class. In this paper, we propose a new latent class model for the RFM analysis based on the purchase history data. The proposed model enables to decide the scoring of RFM and segment customers automatically, and the soft clustering approach helps the interpretation of the result. Furthermore, the proposed model takes account of the generation model of RFM scores. From the result of actual data analysis, it became clear that it is possible to extract latent classes that express the statistical characteristics of data well. Given a generative model estimated from the given data, it is also possible to predict future purchase behaviors of customers or to generate virtual data for simulation analysis and make decisions based on the result. We verify the effectiveness of our model by analyzing a real purchase history data of a Japanese major retail company.

Target Market Determination for Information Distribution and Student Recruitment Using an Extended RFM Model with Spatial Analysis

ERNAWATI,BAHARIN Safiza Suhana Kamal,KASMIN Fauziah 한국유통과학회 2022 유통과학연구 Vol.20 No.6

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.

AHP와 RFM 결합모델 기반의 환경교육 성과분석

김병무(Kim, Byoung-Moo),서광규(Seo Kwang-Kyu) 한국산학기술학회 2012 한국산학기술학회논문지 Vol.13 No.2

환경문제가 심각해지며, 환경교육에 대한 중요성이 강조되고 있으나 대학교육에서는 환경교육은 활발히 이루 어지고 있지 않다. 대학에서의 환경교육은 환경공학 중심으로 이루어지고 있는데, 공학교육의 일환으로써의 환경교육 은 환경공학에서의 학문분야뿐만 아닌 공과대학생 전체에 적합한 환경교육이 이루어져야만 더욱 큰 효과가 있을 것 이다. 본 연구에서는 공과대학생을 위한 환경교육을 위하여 공학교육과 환경교육 전문가의 의견을 반영한 AHP 기법 을 적용하여 공학도에게 적합한 환경교육 범주를 선정하였다. 공학도를 위한 환경교육범주를 선정한 후 공과대학생을 대상으로 환경교육을 실시하였으며 교육전후의 인식변화 설문조사를 통한 교육전후의 환경에 대한 인식변화를 통계 적으로 검증하였고 추가적으로 친환경제품의 구매 데이터를 기반으로 통계적 분석 및 RFM 분석을 실시하여 환경교 육의 실질적 성과분석을 수행하였다. The environmental education is more and more important according to increasing environmental problems, but university students don't receive it actively. Actually, environmental education in university is conducted and focused on environmental engineering. In order to have an effect on environmental education for all engineering college students, the course of environmental education consists of various fields of engineering study including environmental engineering. The environmental education categories for engineering college students are determined by using an AHP technique in this study. After educating engineering college students with the developed environmental education course, we evaluated the environmental education performance using the survey on perception level and purchasing data of environmentally consciously products by statistical and RFM analysis.

인터넷 소매유통업의 RFM 모델 기반 충성고객관리를 위한 웹서비스(WeLCM) 프레임웍

박광호 한국지능정보시스템학회 2002 지능정보연구 Vol.8 No.1

소매유통업에 있어 충성고객을 발견하고 효과적으로 관리하는 일은 마케팅 부서의 주요 관심사라고 할 수 있다. 최근 성숙된 유통 채널로 자리잡고 있는 인터넷 소매유통업도 다양한 마케팅 노력을 기울이고 있으며 그 성과가 기존 소매유통업 보다 클 것으로 기대하고 있는데 이는 인터넷 소매유통업이 기본적으로 디지털 기반 구조 하에 사업이 수행되기 때문이다. 그러나, 매출 규모가 확장됨에 따라 고객 관계가 보다 복잡해지고 거래 건수도 크게 확장되고 있는 인터넷 소매유통업은 전자적으로 이용 가능한 고객 관리 서비스를 필요로 하고 있다. 본 논문은 인터넷 소매유통업의 충성고객관리를 위한 웹서비스의 프레임웍 및 적용 사례를 제시하고 있다. 고객관리 웹서비스의 기본 모델은 전통적인 RFM분석에 기반을 두고 있는데 복잡한 충성고객관리 업무를 처리하는 에이전트를 제공한다. 인터넷 쇼핑몰이나 상점의 운영 시스템과 용이하게 통합될 수 있는 웹 서비스는 적은 비용으로 효과적인 고객 관리를 실현하는데 기여할 것으로 기대된다. In retail industry, it has been a major focus of marketing to identify and manage loyal customers effectively. Being established as a mature distribution channel, Internet retailing has launched various one-to-one marketing efforts and enjoyed much more fruitful outcome because it is founded on digitally enabled infrastructure. As more complicated and crowded transactions are expected, Internet retailing is in need of electronically available customer management services. This research presents architectural design of Web services for loyal customer management in Internet retailing. The fundamental models of the services are based on traditional RFM analysis. The Web services provide various agents that automate complicated loyal customer management tasks. beadily available Web services are expected to easily integrate into existing applications of any electronic retailers.

실시간 IoT 데이터를 활용한 고객 관계 관리 방안에 관한 연구

배지원(Ji Won Bae),백동현(Dong Hyun Baek) 한국산업경영시스템학회 2019 한국산업경영시스템학회지 Vol.42 No.2

As information technology advances, the penetration of smart devices connected to the Internet, such as smart phone and tablet PC, has rapidly expanded, and as sensor prices have fallen the Internet of Things has begun to be introduced in the industry. Today s industry is rapidly changing and evolving, requiring companies to respond to the new paradigm of business. In this situation, companies need to actively manage and maintain customer relationships in order to acquire loyal customers who bring them a high return. The purpose of this study is to suggest a method to manage customer relationship using real time IoT data including IoT product usage data, customer characteristics and transaction data. This study proposes a method of segmenting customers through RFM analysis and transition index analysis. In addition, a real-time monitoring through control charts is used to identify abnormalities in product use and suggest ways of differentiating marketing for each group. In the study, 44 samples were classified as 9 churn customers, 10 potential customers, and 25 active customers. This study suggested ways to induce active customers by providing after-sales benefit for product reuse to a group of churn customers and to promote the advantages or necessity of using the product by setting the goal of increasing the frequency of use to a group of potential customers. Finally, since the active customer group is a loyal customer, this study proposed an one-on-one marketing to improve product satisfaction.

RFM 모형을 활용한 지역별 재해 위험도 분석 방법론 제안

김태진,김성수,전다희,박상현 한국재난정보학회 2020 한국재난정보학회 논문집 Vol.16 No.3

연구목적: 본 연구는 재해위험지역을 개선하는 재해예방사업을 실시하는 과정에서 예방사업의 우선순 위를 선정하기 위한 분석 방법론 제안을 목적으로 한다. 연구방법: 최근성(Recency), 빈도성(Frequency), 화폐성(Monetary)을 기준으로 데이터의 등급을 나누고 타겟 마케팅을 수행할 수 있는 RFM 모형을 활 용하여 데이터 분석을 수행하였다. 연구결과: RFM 값이 높은 상위 10% 지역이 동해와 남해 연안을 중 심으로 나타났으며, 피해 유형을 살펴보면 사유시설의 피해 건수가 크게 나타났다. 결론: 본 연구에서 는 RFM 모형을 활용하여 재해위험의 우선순위를 선정하고 GIS를 활용한 지역재해위험도를 구현하였 다. 이러한 결과는 재해예방사업의 우선 사업대상지를 선정할 수 있는 기초자료와 재해예방사업의 의 사결정 과정에서 기초자료로 활용되기를 기대한다. Purpose: The purpose of this study is to propose an analytical methodology for selecting the priority of preventive projects in the course of carrying out disaster prevention projects that improve disasterhazardous areas. Method: Data analysis was performed using RFM model which can divide data grade and perform target marketing based on Recency, Frequency, and Monetary. Result: The top 10% of the area with high RFM value was mainly in the East Sea and the South Sea coast, and the number of damage in private facilities was high. Conclusion: In this study, we used the RFM model to select the priority of disaster risk and to implement the regional disaster risk using GIS. These results are expected to be used as basic data for selecting priority project sites for disaster prevention projects and as basic data in the decision-making process for disaster prevention projects.

전자상거래 고객의 클러스터링 분석방법 고찰을 통한 효과적인 군인체력 모형 RSC 클러스터링 분석방법 도출

이준호,노병인,신동규 한국인터넷정보학회 2023 인터넷정보학회논문지 Vol.24 No.6

군에서 군인들의 체력 관리와 전투력 향상을 위한 필수적인 요소로서, 군인들의 운동 능력, 체력, 건강 수준을 효과적으로 측정하고 모니터링하는 중요성을 강조한다. 체력의 효과적인 측정이 관리의 핵심이며, 이는 현대경영학의 원칙과 일치하고 있다. 특히, 전장의 동적인 변화에 대비하기 위해서는 강인한 체력을 가진 군인을 양성하는 것이 중요하다. 이 연구에서는 RFM(Recency, Frequency, Monetary) 고객 분석 및 클러스터링 방법을 전자상거래에서 검증된 방법으로 소개하며, 이를 기반으로 머신러닝 알고리즘이 적용된 AI 고객 분석 방법을 군인 체력 평가에 적용하기 위해, RSC(Reveal, Sustainable, Control) 분석 모형을 도입하여 군인 체력을 효과적으로 분류하고 모니터링하는 방법을 검토하였다. RFM 기법을 적용한 RSC 분석 모형을 통해 군인 체력을 계량화하고 모델링함으로써 지속적인 발전을 도모하고 체력 관리의 효과를 높일 수 있는 전략을 모색하였다. 이러한 방법을 통해 군인의 체력을 향상하고 유지하는데 있어서 AI 고객 분석 기법을 응용한 RSC 클러스터링 분석방법을 도출하였다. This study emphasizes the essential need in the military for effective measurement and monitoring of soldiers' physical fitness, health, and exercise capabilities to enhance both their overall fitness and combat effectiveness. The effective assessment of physical fitness is considered a core element of management, aligning with principles of modern management. Particularly, preparing soldiers with robust physical fitness is deemed crucial for adapting to dynamic changes on the battlefield. In this research, the RFM (Recency, Frequency, Monetary) customer analysis and clustering methods, validated in e-commerce, are introduced as a basis for applying an AI-driven customer analysis approach to assess military personnel fitness. To achieve this, the study explores the incorporation of the RSC (Reveal, Sustainable, Control) analysis model. This model aims to effectively categorize and monitor military personnel fitness. The application of the RFM technique in the RSC analysis model quantifies and models military fitness, fostering continuous improvement and seeking strategies to enhance the effectiveness of fitness management. Through these methods, the study develops an AI customer analysis technique applied to the RSC clustering analysis method for improving and sustaining military personnel fitness.

Incremental Weighted Mining based on RFM Analysis for Recommending Prediction in u-Commerce

Young Sung Cho,Song Chul Moon,In-Bae Oh,Jung-Hoon Shin,Keun Ho Ryu 보안공학연구지원센터 2013 International Journal of Smart Home Vol.7 No.6

SFI 분석 기법을 이용한 내부자 활동 수준의 정량적 평가

엄정호 보안공학연구지원센터 2013 보안공학연구논문지 Vol.10 No.2

본 논문에서는 내부자에 의해서 발생하는 보안 사고를 사전에 예방하기 위해서 내부자가 접근한 객체에 대한 활동 수준을 정량적으로 평가할 수 있는 방법을 제시하였다. 내부자의 활동 수준을 정량 적으로 평가하기 위해서 RFM 모델을 활용한다. RFM 모델은 병원이나 백화점의 고객 관리를 위해 시기, 빈도, 금액 관점에서 평가는 방법이다. 본 논문에서는 RFM 모델을 개선하여 내부자가 최근에 시스템이나 데이터에 언제 접근했는지, 얼마나 자주 접근했는지, 그리고 접근한 데이터의 가치가 얼 마나 중요한지를 갖고 내부자 활동 수준을 정량적으로 평가할 수 있는 SFI 분석기법을 제안하였다. 즉, 접근시기, 접근빈도 그리고 자산의 중요도를 활용하여 객체에 대한 내부자의 활동 수준을 정량적 으로 평가한다. 평가결과는 사이버 포렌식의 감사기록으로도 사용할 수 있다.

Elucidating strategic patterns from target customers using multi-stage RFM analysis

Manojit Chattopadhyay,Subrata Kumar Mitra,Parikshit Charan 한국마케팅과학회 2023 마케팅과학연구 Vol.33 No.3

Predicting profitable customers is a strategic knowledge portfolio of retailer managers because some customers are better profitable than others in a business. The present work is an effort to demonstrate a better model of predicting profitable customers. We apply the k-means algorithm to identify customer patterns based on Recency, Frequency, and Monetary (RFM) attributes computed from a real-life dataset of UK-based and registered non-store online retail. Six data mining models have been applied to each identified pattern and overall data to predict whether each customer would purchase in the next six months or not. A comparative analysis of identified pattern characteristics and predictable performances and Type I and Type II errors have been performed to identify the target customer group in terms of better predictability and profitability. The identified patterns help to generate novel marketing strategies. Thus, the retailers may successfully target the most consistently profitable customer groups to apply diverse knowledge on marketing strategies for the specific pattern.