Recency, Frequency, Monetary Model Research Articles

Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach

This paper examines the progression and implementation of customer segmentation theory in banking, focusing particularly on an improved RFM (Recency, Frequency, Monetary) model. It highlights the essential role of identifying high-value customers for the sustained growth and success of commercial banks. The study elucidates the pivotal role of Customer Relationship Management (CRM) and detailed management in the realm of retail banking as crucial for achieving success. By analyzing real-case scenarios from the Chinese banking sector, this paper illuminates the evolving nature of customer segmentation theory and its varied applications. The objective is to provide insightful and practical recommendations for commercial banks aiming to develop or enhance their customer segmentation frameworks. This research contributes both to the theoretical understanding of customer segmentation and offers a pragmatic guide for banks seeking to improve their customer management approaches in a market that is becoming increasingly competitive.

Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

The key points in customer segmentation are determining target customer groups and satisfying their needs. Recency-Frequency-Monetary (RFM) analysis and K-Means clustering algorithm are the popular methods for customer segmentation when analyzing customer behavior. In our study, we adapt the K-means clustering algorithm to RFM model by extracting features that represent RFM aspects of home appliances. Customers with similar RFM-oriented features are assigned to the same clusters, while customers with non-similar RFM-oriented features are assigned to different clusters. In the experiments, clustering achieved the determined threshold for Silhouette Score. The resulting clusters were ranked and named by Customer Lifetime Value (CLV) metric, which measures how valuable a customer is to the business.

Predicting customer churn based on changes in their behavior patterns

Customer retention is one of the most important tasks of a business, and it is extremely important to allocate retention resources according to the potential profitability of the customer. Most often the problem of predicting customer churn is solved based on the RFM (Recency, Frequency, Monetary) model. This paper proposes a way to extend the RFM model with estimates of the probability of changes in customer behavior. Based on an analysis of data relating to 33 918 clients of a large Russian retailer for 2019–2020, it is shown that there are recurring patterns of change in their behavior over a single year. Information about these patterns is used to calculate the necessary probability estimates. Incorporating these data into a predictive model based on logistic regression increases prediction accuracy by more than 10% on the metrics AUC and geometric mean. It is also shown that this approach has limitations related to the disruption of behavioral patterns by external shocks, such as the lockdown due to the COVID-19 pandemic in April 2020. The paper also proposes a way to identify these shocks, making it possible to forecast degradation in the predictive ability of the model.

Analisis Segmentasi Recency dan Customer Value Pada AVANA Indonesia Dengan Algoritma K-Means dan Model RFM (Recency, Frequency and Monetary)

Avana Indonesia is a social commerce startup headquartered in Malaysia. Wanting to expand their business and enter the Indonesian market, they still don't have the best marketing strategy in place, so a service sales deal is not enough. That's why we need a marketing strategy that focuses on customers with customer relationship management, one of which is customer segmentation. Customer segmentation can be done by implementing a data mining process which is carried out using the K-Means clustering algorithm based on the RFM (Recency, Frequency, Monetary) model. The number of clusters in the clustering process is determined using the elbow method. Cluster analysis based on customer value using the recency clustering method reveals active, warm, cold, and inactive customers. Then the two from recency frequency (customer value) segmentation produce common, ultra-high, low, and high clusters.

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 the Integration of the Recency Frequency Monetary Model and the K-Means Cluster Algorithm

Purpose: This research aims to do customer segmentation in retail companies by implementing the Recency Frequency Monetary (RFM) K-Means cluster model and algorithm optimized by the Elbow method.Methods: This study uses several methods. The RFM model method was chosen to segment customers because it is one of the optimal methods for segmenting customers. The K-Means cluster algorithm method was chosen because it is easy to interpret, implement, fast in convergence, and adapt, but lacks sensitivity to the initial partitioning of the number of clusters. To help classify each category of customers and know the level of loyalty, they use a combination of the RFM model and the K-Means method. The Elbow method is used to improve the performance of the K-Means algorithm by correcting the weakness of the K-Means algorithm, which helps to choose the optimal k value to be used when clustering.Result: This research produces customer segmentation 3 clusters with a Sum of Square Error (SSE) value of 25,829.39 and a Callinski-Harabaz Index (CHI) value of 36,625.89. The SSE and CHI values are the largest ones, so they are the optimal cluster values.Novelty: The application of the integrated RFM model and the K-Means cluster algorithm optimized by the Elbow method can be used as a method for customer segmentation.

Exploring the Individual Travel Patterns Utilizing Large-Scale Highway Transaction Dataset

With the spread of electronic toll collection (ETC) and electronic payment, it is still a challenging issue to develop a systematic approach to investigate highway travel patterns. This paper proposed to explore spatial–temporal travel patterns to support traffic management. Travel patterns were extracted from the highway transaction dataset, which provides a wealth of individual information. Additionally, this paper constructed the analysis framework, involving individual, and temporal and spatial attributes, on the basis of the RFM (Recency, Frequency, Monetary) model. In addition to the traditional factors, the weekday trip and repeated rate were introduced in the study. Subsequently, various models, involving K-means, Fuzzy C-means and SOM (Self-organizing Map) models, were employed to investigate travel patterns. According to the performance evaluation, the SOM model presented better performance and was utilized in the final analysis. The results indicated that six groups were categorized with a significant difference. Through further investigation, we found that the random traveler occupied over 40% of the samples, while the commuting traveler and long-range freight traveler presented relatively fixed spatial and temporal patterns. The results were also meaningful for highway authority management. The discussion and implication of travel patterns to be integrated with the dynamic pricing strategy were also discussed.

Segmentation and Formation of Customer Regression Model Based on Recency, Frequency and Monetary Analysis

During this pandemic, the number of customers of a securities company has increased quite high. This requires securities companies to conduct analysis related to security customer data against transaction data so that the company can find out the segmentation of registered customers and also so that companies can predict the transaction patterns of customers in the company. In processing transaction data, the RFM (Recency, Frequency, Monetary) model can be used as a way to group customers according to their business values. After doing the modeling using RFM, the data is clustered using the K-Means algorithm to find out the segmentation in the RFM model in each group. The RFM model that has been clustered will produce segments based on the RFM group. In this data, a linear regression analysis process is carried out where each group and segment is analyzed and predicted related to variables such as recency, monetary and frequency. The results of data grouping, customer segmentation and also predictions with linear regression can be one of the company's references to make a business decision. From the linear regression process carried out on the RFM attributes, a prediction of the monetary value of the existing recency value is generated and the monetary value of the frequency can also be known with a fairly good error rate.

Pengelompokan Pelanggan Toko Kerajinan Menggunakan K-Means dengan Model RFM dan LRFM

Customer groups within a company or shop are important to identify for determine the right sales strategy in a competitive market. This research was conducted at one of the online handicraft shops in Bali which was affected by the intense competition in the market. The handicraft industry in Bali is considered to provide a large contribution to the community's economy. One solution that can be done is to identify the customer groups in the store using the clustering technique. This research aims to get the best customer data cluster and model. The stages in this research start from preprocessing data on historical data and customer orders to generate data model Recency, Frequency, Monetary (RFM) and Length, Recency, Frequency, Monetary (LRFM), then the clustering process with K-Means and evaluation of cluster quality with Silhouette Coefficient (SC). Results Based on the research, the RFM model becomes a data model that produces the best clustering results with an SC value of 0.545 when k = 2 which is included in the medium structure. LRFM model only produces the largest SC value when k = 3 with a value of 0.415. The SC value calculated from each data model tends to increase when the percentage of data is added. Cluster 1 has 817 members with the last transaction taking a long time, but has a below average ordering and monetary frequency. Cluster 2 has 158 members, making the last transaction at the most recent time, and has an average order and monetary frequency.
 
 Keywords: Clustering, Customer, Craft, K-Means, RFM, LRFM, Silhouette Coefficient

An approach for sales forecasting

Inaccurate sales forecasting (SF) leads to over-stock or stock-out, in which can increase inventory costs, thereby reducing profits and return on investment. Furthermore, stock-out can eliminate customer loyalty, as well as opportunities to acquire new customers, and maximize sales or competitive advantage. This study proposed a retail SF model called the SalesKBR, which integrates the decision-making (Best-Worst Method/BWM) and the data mining methods (k-Means) into the Recency-Frequency-Monetary (RFM) model. The BWM was used to extract criteria that have a significant influence on retail SF. The k-Means method and six validity indices were applied to improve the quality of the product clustering results, while the RFM model was used for assessment. The extraction results from BWM that correlate with the retail company databases showed the criteria that significantly affect SF are frequency, quantity, and monetary. The results also showed that SalesKBR is a retail SF model with a reasonable level of accuracy. Therefore, it can be utilized to forecast retail sales, and it has the potential to be an alternative solution for making scientific and valuable management decisions.

IMPLEMENTATION RFM ANALYSIS MODEL FOR CUSTOMER SEGMENTATION USING THE K-MEANS ALGORITHM CASE STUDY XYZ ONLINE BOOKSTORE

XYZ online bookstore is one of the companies engaged in the online book sales industry that located in Jakarta, Indonesia, but the marketing strategy given to customers has not been maximized, so it has not been able to increase book purchase transactions. Therefore a customer-centered marketing strategy is needed by implementing Customer Relationship Management, One of the methods that can be applied is customer segmentation. Customer segmentation can be done by implementing a data mining process which carried out by using the K-means clustering algorithm and based on the RFM (Recency, Frequency, Monetary) model. . Determining the number of clusters in the clustering process using the elbow method. Performance tests on cluster results using the silhouette method, and the Calinski-Harabasz index. The results of cluster analysis based on customer value using the RFM Combination and Customer Value Matrix methods show that based on the RFM Combination method produces 3 types of customer characteristics namely loyal customers, new customers, and lost customers. Meanwhile, based on the customer value matrix method, it produces 2 types of customer characteristics namely best customer and uncertain customer.

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.

Customer Behavior Analysis using Machine Learning

RFM (Recency, Frequency, Monetary) investigation is a demonstrated showcasing model for conduct based client division. It groups clients dependent on their exchange history – how as of late, how frequently and what amount they buy.RFM helps partition clients into different classes or groups to distinguish clients who will react to advancements and how. This RFM examination depends on a blend of three boundaries. For instance, we can say that individuals who spend the most on items are our best clients. A large portion of us coincide and think about something very similar. In any case, Imagine a scenario in which they were bought just a single time. Or on the other hand an extremely quiet past? Consider the possibility that they are done utilizing our item. would they be able to in any case be viewed as your best clients? Most likely not. Making a decision about client esteem from only one perspective will give you a mistaken report of your client base and their lifetime. That is the reason, the RFM model joins three diverse clients ascribed to rank clients. In the event that they purchased in the recent past, they get higher focus. On the off chance that they purchase ordinarily, they get a higher score. What's more, on the off chance that they spend greater, they get more focus. Thus, we Combine these three scores to make the RFM score. At long last we can portion the client data set into various gatherings dependent on this RFM score.

A review of data mining methods in RFM-based customer segmentation

Data mining (DM) is the process of extracting knowledge from data. Knowledge from customer behaviour segmentation is useful for companies in setting the target market and developing a marketing strategy. Recency Frequency Monetary (RFM) model is the most behaviour segmentation used. Many customer-segmentation studies in various application areas use the RFM model that collaborates with DM. With many methods in DM, the selection of appropriate methods can reveal useful hidden patterns in customer segments. This paper aims to analyse DM methods that collaborate with the RFM model and synthesize them to propose a customer segmentation framework. This study uses a comprehensive literature review published in 2015-2020. The most widely used methods are clustering and visualization from seven DM methods analysed. Due to the increased visualization function and the need for customers’ geo-demographic data to be considered in the analysis, this study presents a new framework for using DM methods with the RFM based segmentation in the Geographic Information Systems (GIS) environment. This framework helps analysts utilize DM methods to uncover and understand customer characteristics, so companies can set the target market and develop a marketing strategy to increase their competitive advantage.

An Extended Regularized K-Means Clustering Approach for High-Dimensional Customer Segmentation With Correlated Variables

The omnichannel business has becomes a hot topic due to the fast development on e-commerce and the customers' acquaintance with multichannel shopping mode. Various business organizations have started to work on omnichannel business issue in order to satisfy the new trend of customer demand and tend to devote their efforts to both online and offline business. Thus, there is no doubt that understanding the shopping behavior for online customers is vital for the omnichannel business. The RFM (recency, frequency, monetary) model and the k-means clustering method are commonly used to extract customers' information and segment customers, respectively. To extend the RFM model, we divide the total frequency and monetary information into weekly level data, and as a consequence, the number of variables corresponding to one customer increases significantly, leading to the problem of high-dimensional analysis. To address this issue, in this paper we extend the regularized k-means clustering method with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm for independent case to the clustering method with elastic net penalty with a focus on correlated variables. Our simulation results show that the proposed method performs better than the standard k-means method by providing lower error rates and can select variables simultaneously under 4 different scenarios. A real example of an online retailer is presented to illustrate the use of the proposed method and highlight its high potential in clustering high-dimensional applications. In particular, the number of variables is reduced from 108 to 98 without any loss on clustering accuracy.

Measuring Customer Similarity and Identifying Cross-Selling Products by Community Detection.

Product affinity segmentation discovers groups of customers with similar purchase preferences for cross-selling opportunities to increase sales and customer loyalty. However, this concept can be challenging to implement efficiently and effectively for actionable strategies. First, the nature of skewed and sparse product-level data in the clustering process results in less meaningful solutions. Second, customer segmentation becomes challenging on massive data sets due to the computational complexity of traditional clustering methods. Third, market basket analysis may suffer from association rules too general to be relevant for important segments. In this article, we propose to partition customers into groups with their product purchase similarity maximized by detecting communities in the customer-product bipartite graph using the Louvain algorithm. Through a case study using data from a large U.S. retailer, we demonstrate that the proposed method generates interpretable clustering results with distinct product purchase patterns. Comprehensive characteristics of customers and products in each cluster can be inferred with statistical significance since they are essentially driven by products purchased by customers. Compared with the conventional RFM (recency, frequency, monetary) model, the proposed approach leads to higher response rates in the recommendation of products to customers in the same cluster. Our analysis provides greater insights into customer purchase behaviors, improves product recommendation effectiveness, and addresses computational complexity in the context of skewed and sparse big data.

Güncellik Sıklık Parasallık Modeline Dayalı Müşteri Bölümlendirme: E-Perakende Sektöründe Bir Uygulama

Marketing studies have often drawn attention to the importance of customers for businesses that aim to endure in a harsh competitive environment. Customer Relationship Management (CRM) has been a prominent approach in marketing management that aims to improve relationships with customers. A practical implication of the CRM approach is the analysis of customer data to extract value for businesses, as well as customers. In this context, customer segmentation has been a useful task that helps to group customers with similar attributes and designate better-tailored marketing strategies for customer groups. Among a variety of approaches for customer segmentation, Recency Frequency Monetary (RFM) Model stands out as an easy-to-adopt and effective technique. Based on three dimensions regarding the sales data, the RFM Model depends on scoring customers with different approaches. In this study, a prototype software is introduced that helps to apply the RFM technique with two scoring approaches. Moreover, the sales data obtained from an e-retailer has been analyzed for clustering using the prototype software, and clusters discovered with RFM variants were compared using cluster evaluation metrics. Finally, the segments were presented along with relevant offers for marketing strategies.

SEGMENTASI PELANGGAN BISNIS DENGAN MULTI KRITERIA MENGGUNAKAN K-MEANS

Customer knowledge is an important asset, in gathering, and managing from sharing customer knowledge into valuable capital for the company. This causes the company to continue to innovate in producing products and serving according to customer needs. To find out the needs of each customer, the company needs to make customer segmentation. Customer segmentation is defined as the division into different groups with similar characteristics to develop marketing strategies that are tailored to customer characteristics. The easiest, simplest, well-known and commonly used model of customer characteristics is the model of the recency, frequency, monetary (RFM) criteria. The RFM model still has weaknesses in low customer segmentation capacity and does not provide information on the continuity of customer transactions in understanding customer loyalty. The research method used is the Knowledge Discovery in Database (KDD) method. The data is transformed into another format that suits the needs of analysis and then the customer is segmented using clustering data mining techniques with the K-Means algorithm. From the experiments, the RFM model guesses loyal customers when reviews, frequency and monetary are high. In reality, the recency only provides information on the customer making the last transaction and the high number of transaction frequencies can be done without the customer's stability in making transactions each period. Implementing multi-criteria in customer segmentation can be better than just RFM criteria. So it will not be wrong to treat customers according to the groups that have been formed.

Telecom customer clustering via glowworm swarm optimisation algorithm

The glowworm swarm optimisation (GSO) algorithm is a novel algorithm with the simultaneous computation of multiple optima of multimodal functions. Data-clustering techniques are classification algorithms that have a wide range of applications. Since K-means algorithm is easy to fall into the local optimum by the selection of initial clustering centre, GSO algorithm is used for the telecom customers clustering. The customer consumption data is extracted by means of the recency frequency monetary (RFM) model and the standardised data is clustered automatically using the GSO algorithm's synchronous optimisation ability. In the clustering optimisation algorithm, adaptive step size is used instead of the original fixed step size to avoid local optimisation of the algorithm and obtain higher accuracy. Compared with K-means clustering algorithm, GSO approach can automatically generate the number of clusters and use RFM model to reduce effectively the size of the data processing. The results of the experiments demonstrate that the GSO-based clustering technique is a promising technique for the data clustering problems.

Telecom customer clustering via glowworm swarm optimisation algorithm

The glowworm swarm optimisation (GSO) algorithm is a novel algorithm with the simultaneous computation of multiple optima of multimodal functions. Data-clustering techniques are classification algorithms that have a wide range of applications. Since K-means algorithm is easy to fall into the local optimum by the selection of initial clustering centre, GSO algorithm is used for the telecom customers clustering. The customer consumption data is extracted by means of the recency frequency monetary (RFM) model and the standardised data is clustered automatically using the GSO algorithm's synchronous optimisation ability. In the clustering optimisation algorithm, adaptive step size is used instead of the original fixed step size to avoid local optimisation of the algorithm and obtain higher accuracy. Compared with K-means clustering algorithm, GSO approach can automatically generate the number of clusters and use RFM model to reduce effectively the size of the data processing. The results of the experiments demonstrate that the GSO-based clustering technique is a promising technique for the data clustering problems.