Recency, Frequency, Monetary Analysis Research Articles

A Manhattan distance based hybrid recommendation system

Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions[20]. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches[1]. This paper describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance-based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.

A Big Data Based Dynamic Weight Approach for RFM Segmentation

Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set. Firstly, Analytic Hierarchy Process is used to calculate the subjective weight, then the entropy method is applied to calculate the objective weight. Finally, we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference. The experiment shows that the dynamic weight we used in RFM model is vital, affects the customer segmentation performance positively. Also, this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior. At last, we discuss the limitations of RFM analysis.

CUSTOMER SEGMENTATION USING MACHINE LEARNING

RFM (Recency,Frequency, Monetary) analysis is a method to identify high-response customers in marketing promotions, and to improve overall response rates, which is well known and is widely applied today. Less widely understood is the value of applying RFM scoring to a customer database and measuring customer profitability. RFM analysis is considered significant also for the banks and their specific units like online shopping A customer who has visited an online shopping site Recently (R) and Frequently (F) and created a lot of Monetary Value (M) through payment and standing orders is very likely to visit and make payments again. After evaluation of the customer’s behaviour using specific RFM criteria the RFM score is correlated to the online shopping, with a high RFM score beingmore beneficial to the online shopping as well as in the future. Data mining methods can be considered as tools enhancing the online shopping RFM analysis of the customers in total as well as specific groups like the users of online shopping KEYWORDS-Data Mining online shopping, RFM analysis, Clustering

Determination of Discounts Using K-Means Clustering with RFM Models in Retail Business

Intense competition in the business sector motivates every company to manage services to regular consumers to the fullest. Increase customer loyalty can be done by grouping customers into several groups and determine appropriate and effective marketing strategies for each group. This study aims to propose the right targeting of discounts that can increase customer loyalty in the retail business. Customer grouping uses data mining techniques with the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, which is divided into six phases, namely business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The formation of this cluster uses k-means clustering method and is based on RFM (recency, frequency, monetary) analysis. From the results of the silhouette test on 2734 transaction data from 210 customers of PT. XYZ from October 2019 to March 2020, three customer clusters were formed. From these three clusters, one cluster that has the best frequency and monetary values is chosen so that it is considered the worthiest group to be given a discount in order to maintain its loyalty.

Penerapan Clustering Terhadap Segmentasi Zonasi Gangguan Layanan Pelanggan Dengan Menggunakan Analisis RFM

The service area and the wide distribution of customers often require quite a long time in terms of handling disturbances, which can reduce the level of customer trust and loyalty to using the network services provided. Customer segmentation by region can help make it easier to analyze disruptions that occur to customer service in customer area zoning so that customer trust and loyalty can be maintained. This research was conducted to understand the characteristics of disturbances that occur in several customer zoning areas. In this study, RFM (Recency, Frequency, Monetary) analysis was performed. Clustering using the K-Means modeling technique, and also performance checking using Cluster Distance Performance to determine the performance of each cluster performed. The results of this study indicate that K-Means Clustering with a value of K=2 is the with the smallest Davies Bouldin value. It means that K=2 has the best performance when compared to other clusters formed.

Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation

An effective marketing strategy is a method to identify the customers well. One of the methods is by performing a customer segmentation. This study provided an illustration of customer segmentation based on the RFM (Recency, Frequency, Monetary) analysis using a machine learning clustering that can be combined with customer segmentation based on demography, geography, and customer habit through data warehouse-based business intelligence. The purpose of classifying the customers based on the RFM and machine learning clustering analyses was to make a customer level. Meanwhile, customer segmentation based on demography, geography, and behavior was to classify the customers with the same characteristics. The combination of both provided a better analysis result in understanding customers. This study also showed a result that minibatch k-means was the machine learning model with the rapid performance in clustering 3-dimension data, namely recency, frequency, and monetary.

ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING

Intense competition in the business field motivates a small and medium enterprises (SMEs) to manage customer services to the maximal. Improve of customer royalty by grouping cunstomers into some of groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be performed by data mining approach with clustering method. The main purpose of this paper is customer segmentation and measure their loyalty to a SME’s product. Using CRISP-DM method which consist of six phases, namely business understanding, data understanding, data preparatuin, modeling, evaluation and deployment. The K-Means algorithm is used for cluster formation and RapidMiner as a tool used to evaluate the result of clusters. Cluster formation is based on RFM (recency, frequency, monetary) analysis. Davies Bouldin Index (DBI) is used to find the optimal number of clusters (k). The customers are divided into 3 clusters, total of customer in first cluster is 30 customers who entered in typical customer category, the second cluster there are 8 customer whho entered in superstar customer and 89 customers in third cluster is dormant cluster category.

Analysing customer behaviour in mobile app usage

PurposeBig data produced by mobile apps contains valuable knowledge about customers and markets and have been viewed as productive resources. The purpose of this paper is to propose a multiple methods approach to elicit intelligence and value from big data by analysing the customer behaviour in mobile app usage.Design/methodology/approachThe big data analytical approach is developed using three data mining techniques: RFM(recency, frequency, monetary) analysis, link analysis, and association rule learning. The authors then conduct a case study to apply this approach to analyse the transaction data extracted from a mobile app.FindingsThis approach can identify high value and mass customers, and understand their patterns and preferences in using the functions of the mobile app. Such knowledge enables the developer to capture the behaviour of large pools of customers and to improve products and services by mixing and matching the functions and offering personalised promotions and marketing information.Originality/valueThe approach used in this study balances complexity with usability, thus facilitating corporate use of big data in making product improvement and customisation decisions. The approach allows developers to gain insights into customer behaviour and function usage preferences by analysing big data. The identified associations between functions can also help developers improve existing, and design new, products and services to satisfy customers’ unfulfilled requirements.

Analyzing public participant data to evaluate citizen satisfaction and to prioritize their needs via K-means, FCM and ICA

A mutual interaction between citizens and government has become an important requirement for every successful urban decision making process. As cities develop, such interactions play a greater role in citizen satisfaction. Through these interactions, governments can recognize the needs of citizens and try to address their challenges. In these interactions, the role of the people is central; in fact, it is the people who express their needs to the service-providing organizations through different communication channels. A common channel that has been established in Iran is the ‘137 center,’ a municipal, phone-based request/response system.In this article, information from Municipal District 1 in the city of Bojnourd was gathered, analyzed to prioritize urban needs and assessed for citizen satisfaction. Forty-three citizen needs were identified and categorized based on K-means clustering, the Fuzzy Clustering Method (FCM) and the Imperialist Competitive Algorithm (ICA). The three algorithms were also evaluated. RFM (recency, frequency, monetary) analysis was performed for classification. The clustering methods were then assessed and compared using three parameters: execution time, accuracy and simplicity. The results of the FCM and ICA clustering were similar, however, the execution time for FCM was less than for ICA. Considering the similarity of the results and the flexibility of FCM, it was concluded that, if the execution time was of primary importance, then the use of FCM was more appropriate. In contrast, if accuracy was a priority, ICA was preferred. Our results also showed that if simplicity and speed were required, the K-means algorithm was the best choice. Finally, subjects such as the quality of the asphalt, garbage collection and park development were of primary importance to Bojnourd citizens, therefore the municipality should pay special attention to these subjects.

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

This paper proposes the application of clustering and classification techniques on finding groupings of retailers who use the Electronic Funds Transfer at Point Of Sale (EFTPOS) facilities of a major bank in Australia. The RFM (Recency, Frequency, Monetary) analysis on each retailer is used to reduce the large data set of customer purchases through the EFTPOS network for the purpose of the retailer clustering. We then incorporate attributes of the EFTPOS transaction data in addition to the derived RFM attributes to build a decision tree to facilitate the extraction of business rules that explain the characteristics of the retailer clusters.

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

This paper proposes a new incremental weighted mining based on RFM((Recency, Frequency, Monetary) analysis for recommending prediction in u-commerce. Association rules search for the associated item set on large database. Association rules are frequently used by the marketing pattern analysis in e-commerce, recommendation to promote for selling a product in marketing. The proposing method can extract frequent items and create weighted association rules using incremental weighted mining based on RFM analysis rapidly when new data are added persistently in order to predict frequently changing trends by emphasizing the important items with high purchasability according to the threshold for creative weighted association rules in u-commerce. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

우편 서비스의 법인 고객관계관리를 위한 프로토타입 소프트웨어 개발

Conventional research on customer relationship management(CRM) in general has focused on the effects of individual customer’s satisfaction, retention and profit management. However, corporate customers are more profitable than individual customers because of high volume and frequent transactions between companies. In this article, a prototype for a corporate customer relationship management is developed in the postal service. First, the frequency and amount of customers’ usage were examined, and thereby the corporate customer rating scheme was established to provide customized service. Second, five different types of usage patterns were determined using clustering analysis. In addition, we presented the rationales behind the five types of patterns. Third, RFM(recency, frequency, monetary) analysis was performed, and then action plans were developed to increase sales. Finally, the prototype software was developed to automatically perform the above analysis using MS Excel program. 1) Keyword: customer relationship management, postal service, corporate customer, RFM, marketing strategy

BUSINESS-ORIENTED ADMISSION CONTROL AND REQUEST SCHEDULING FOR e-COMMERCE WEBSITES

The article concerns the problem of quality of Web service (QoWS) in e-commerce with the focus on request admission control and scheduling from the profit perspective of the owner of an e-business site. Two new functional extensions for a Web server system supporting business-to-consumer (B2C) activity are proposed. The first new feature is an ability to differentiate the key customers of the Web store and to offer higher QoWS to more valued ones. The second new function concerns offering high QoWS to all customers finalizing their purchase transactions. A new approach to B2C service differentiation using RFM (Recency, Frequency, Monetary Value) analysis is presented. A simulation model of the B2C Web server system and some results of the system performance for our approach in comparison with first in–first out (FIFO) scheduling are discussed.

DSS for computer security incident response applying CBR and collaborative response

Recently, as hacking attempts increase dramatically; most enterprises are forced to employ some safeguards for hacking proof. For example, firewall or IPS (Intrusion Prevention System) selectively accepts the incoming packets, and IDS (Intrusion Detection System) detects the attack attempts from network. The latest version of firewall works in cooperation with IDS to immediately response to hacking attempts. However, it may make false alarms that misjudge normal traffic as hacking traffic and cause network problems to block the normal IP address by false alarms. By these false alarms made by IDS, system administrators or CSOs make wrong decisions and important data may be exposed or the availability of network or server system may be exhausted. Therefore, it is important to minimize the false alarms. As a way of minimizing false alarms and supporting adequate decisions, we suggest the RFM (Recency, Frequency, Monetary) analysis methodology, which analyzes log files with incorporating three criteria of recency, frequency and monetary with statistical process control chart, and thus leads to an intuitive detection of anomaly and misuse events. Moreover, to cope with hacking attempts proactively, we apply CBR (case based reasoning) to find out similarities between already known hacking patterns and new hacking patterns. With the RFM analysis methodology and CBR, we develop DSS which can minimize false alarms and decrease the time to respond to hacking events. In case that RFM analysis module finds out unknown viruses or worms occurred, this CBR system matches the most similar incident case from case-based database. System administrators can easily get information about how to fix and how we fixed in similar cases. And CSOs can build a blacklist of frequently detected IP addresses and users. This blacklist can be used for incident handling. Finally, we propose collaborative incident response system with DSS, this distributed agent systems interactively exchange the suspicious users and source IP addresses data and decide who is true-anomalous users and which IP addresses is the most riskiest and then deny all connections from that users and IP addresses automatically with less false-positives.

Principes de cartographie appliquee a l'ecologie humaine

A mutual interaction between citizens and government has become an important requirement for every successful urban decision making process. As cities develop, such interactions play a greater role in citizen satisfaction. Through these interactions, governments can recognize the needs of citizens and try to address their challenges. In these interactions, the role of the people is central; in fact, it is the people who express their needs to the service-providing organizations through different communication channels. A common channel that has been established in Iran is the ‘137 center,’ a municipal, phone-based request/response system.In this article, information from Municipal District 1 in the city of Bojnourd was gathered, analyzed to prioritize urban needs and assessed for citizen satisfaction. Forty-three citizen needs were identified and categorized based on K-means clustering, the Fuzzy Clustering Method (FCM) and the Imperialist Competitive Algorithm (ICA). The three algorithms were also evaluated. RFM (recency, frequency, monetary) analysis was performed for classification. The clustering methods were then assessed and compared using three parameters: execution time, accuracy and simplicity. The results of the FCM and ICA clustering were similar, however, the execution time for FCM was less than for ICA. Considering the similarity of the results and the flexibility of FCM, it was concluded that, if the execution time was of primary importance, then the use of FCM was more appropriate. In contrast, if accuracy was a priority, ICA was preferred. Our results also showed that if simplicity and speed were required, the K-means algorithm was the best choice. Finally, subjects such as the quality of the asphalt, garbage collection and park development were of primary importance to Bojnourd citizens, therefore the municipality should pay special attention to these subjects.