Recency, Frequency, And Monetary Research Articles

Identifying city bus passenger ridership patterns: a mixed-method analysis

PurposeThis study uses big data analysis aimed at discovering city bus passenger ridership patterns. Hence, marketing managers can get sufficient insights to formulate effective business plans and make timely decisions about company operations.Design/methodology/approachThis study uses a mixed-method analysis to analyze the results. First uses the RFM (recency, frequency, and monetary) model combined with a big data technique (K-means) to analyze bus passenger boarding behavior. In order to improve the validity and quality of the research, this study also conducted interviews with senior managers of the bus company from which the data was obtained.FindingsThe study identifies six distinct groups of passengers with different boarding behaviors, ranging from “general passengers” to “most valuable passengers”. General passengers constituted the largest group. As such, they should be the main target for municipal governments when promoting bus ridership as part of energy conservation and carbon-reduction activities. This group of passengers should be encouraged to take public transport vehicles more, instead of relying on personal vehicles. The fourth group identified included elderly passengers with hospitals as their destinations. Bus companies can cooperate with municipal government to provide morning “medical bus” services for the elderly. Interviews with bus company managers confirmed that the analytical results of this study correspond with the observations, experiences, and actual business operating plans of bus companies.Originality/valueOnly few studies have analyzed passengers' boarding behavior applying a mixed-method analysis.

Segmentation Strat Egy of Consumer Interest in Contemporary Coffee Shop Using RFM Model

Increase customer loyalty by grouping customers into several groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be done through the clustering method. This research aims to analyze customer Recency, Frequency and Monetary (RFM) from the segmentation results of 5 contemporary coffee shops, namely Janji Jiwa, Kopi Kenangan, Tomoro Coffee, Fore Coffee, and Fami Cafe, from Persona Analysis Research and segmentation of consumer interest in contemporary coffee shops. Previously it could be summarized that cluster 3 or potential customers were the targets of the research by implementing appropriate marketing strategies based on the characteristics and profiles of the respondents. This cluster consists of 31 respondents, most of whom are women aged 24-28 years with a bachelor's degree and work as private employees in Jakarta. Interestingly, cluster 3 is divided between coffee fans and those who don't like coffee. Respondents in this study tended to prefer Tomoro Coffee, while those who didn't like coffee preferred Fami Cafe. In cluster 3, there are 29 people who are active Instagram users. The analysis used includes descriptive analysis where the data used is primary data. The data collection technique used is the observation method and distributing questionnaires to customers who are in cluster 3. The results of the research show that based on the results of the revenue segmentation analysis for the Tomoro Coffee and Fami Cafe coffee shops, it can be concluded that customers in cluster 3 consist of 3 segments. namely recency 3 which consists of 16 active customers who are very loyal and faithful. The strategy to retain these customers is by personalizing customer data to understand product/service shortcomings and knowing customer individual preference needs in order to attract them back. Keywords: Frequency, Monetary, Recency.

Sistem Penentuan Jenis Promosi Berdasarkan Tingkat Loyalitas Pelanggan di CV. XYZ

CV. XYZ is a company engaged in selling fabrics. The company's current customers are around 372 customers, of which 81% or around 301 customers only make transactions less than 10 times and 19% or around 71 customers make transactions more than 10 times, this shows a low level of customer loyalty towards the company. Company owners currently have difficulty determining the type of promotion that will be given to each customer based on their level of loyalty. The method used to segment customers is the Recency, Frequency and Monetary (RFM) method and delivering promotions to customers using WhatsApp API Gateway Services. The results of this RFM method can make it easier for company owners to determine the type of promotion that will be given to each customer as well as the delivery of the promotion in an effort to increase customer loyalty to the company. Of the 24 customers used as sample data, the Most Valuable Customer group has 4 customers, the Most Growable Customer group has 15 customers, the Migrator group has 5 customers and the Below Zeros group has 0 customers.

Multiple criteria decision support system for customer segmentation using a sorting outranking method

For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups and its parameters have been adapted for contexts with thousands of alternatives. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.

An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market

Recently, peoples’ awareness of online purchases has significantly risen. This has given rise to online retail platforms and the need for a better understanding of customer purchasing behaviour. Retail companies are pressed with the need to deal with a high volume of customer purchases, which requires sophisticated approaches to perform more accurate and efficient customer segmentation. Customer segmentation is a marketing analytical tool that aids customer-centric service and thus enhances profitability. In this paper, we aim to develop a customer segmentation model to improve decision-making processes in the retail market industry. To achieve this, we employed a UK-based online retail dataset obtained from the UCI machine learning repository. The retail dataset consists of 541,909 customer records and eight features. Our study adopted the RFM (recency, frequency, and monetary) framework to quantify customer values. Thereafter, we compared several state-of-the-art (SOTA) clustering algorithms, namely, K-means clustering, the Gaussian mixture model (GMM), density-based spatial clustering of applications with noise (DBSCAN), agglomerative clustering, and balanced iterative reducing and clustering using hierarchies (BIRCH). The results showed the GMM outperformed other approaches, with a Silhouette Score of 0.80.

ANALISIS SEGMENTASI PELANGGAN BERBASIS MODEL RECENCY FREQUENCY DAN MONETARY (RFM) MENGGUNAKAN ALGORITMA K-MEANS

Business development is currently growing very rapidly, with the development of internet technology that can facilitate all business activities. Increasing business development has an impact on presenting new business competitors, so companies need strategies that are able to maintain customer quality. This study aims to segment customers from the company's sales transaction data, with a large number of transactions, technology is needed to group a data so that the method used in this study is a data mining method and uses the K-Means algorithm. With the K-Means Algorithm, it can help in grouping customers to make it easier for companies to strategize each customer group. This customer grouping uses an initial model of Recency, Frequency and Monetary (RFM) to help calculate customer groups. Data mining evaluation was carried out using Silhouette Coefficient with test results using Visual Studio Code software python programming language, The results of this study selected 3 clusters consisting of Low Loyalty totaling 137 customers, Medium Loyalty totaling 1636 customers and Highest Loyalty totaling 2395 customers.

Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis

Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques, customer churn prediction is a prominent one. In order to forecast customer switching, data scientists employ a variety of demographic, social, transactional, and behavioral variables and attributes. Unfortunately, many businesses in the United Kingdom still lack the comprehensive and adaptable consumer data required to perform accurate analyses. As a result, they often rely heavily on data produced by enterprise resource planning systems, which is primarily transactional in nature. Consequently, businesses are often limited to modeling and forecasting on transactional data alone and are unlikely to invest significantly in marketing research or other customer-related sources. Businesses are often limited to performing modeling and forecasting on transactional data that are most often not based on advanced techniques like recency, frequency and monetary (RFM) and ML. So, the major objective of the current work is to provide a mix of ML and RFM analysis techniques for churn prediction using mostly transactional data. The dataset was taken from the dataset search website containing online retail datasets. Every customer's RFM scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a limited time. Through this paper, different techniques are compared. We used K-means and DBSCAN clustering. By the end of this paper, it may be inferred that the act of dividing customers into six distinct clusters is a more practical and straightforward approach.

Location Selection of Shared Bicycle Distribution Points Based on User Demand

Shared bicycle systems must select the best delivery point locations to meet user demand. Various types of users have different needs, and here we used the RFM (Recency, Frequency and Monetary) model to classify user types and then used the analytic hierarchy process to determine the weights of user types. We selected the shortest weighted total walking distance of users as the optimization objective, constructed a siting model of shared bicycle delivery points based on user demand, and solved this problem using genetic algorithm. Finally, we validated the model using the bike-sharing data of a region in Shanghai. The results showed that for 600 delivery points, the average walking distance of each user was 206.98 m. Thus, this model can effectively optimize the layout of delivery points of a bike-sharing system.

SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL RFM (RECENCY, FREQUENCY, MONETARY)

Companies or entrepreneurs must better understanding customers data in all aspects, including detecting similarities and differences among customers, predicting their behavior, and offering better options and opportunities to customers. Customer segmentation is carried out to obtain this information, which is part of CRM (Customer Relationship Management). One of the general models in the application of customer segmentation is the RFM (Recency, Frequency, and Monetary) model. This research method uses a combination of the RFM model and clustering. RFM is used as a description of customer behavior in conducting transactions. Clustering is a process that is widely used and is designed to categorize data. Clustering uses the K-Means Algorithm to determine the number of clusters using the Elbow and Silhouette methods. The application of RFM analysis and the K-Means resulted in two customer segments, namely potential customers and non-potential customers. Potential customers have the characteristics of frequent transactions and also large expenses. Non-potential customers have the characteristics of infrequent transactions and also standard expensesKeywords: Customer Segmentation, RFM Model, K-Means Clustering

Sorting Center Value Identification of "Internet + Recycling" Based on Transfer Clustering.

As the core link of the “Internet + Recycling” process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method.

Segmentation of Mall Customers Using RFM Analysis and K-Means Algorithm

Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.

Segmentation of Mall Customers Using RFM Analysis and K-Means Algorithm

Customer Segmentation is the technique of separating customers into different clusters based on their specific characteristics. Segmenting customers is very essential in every business sector because each individual is different from one another and has distinct interests. But with the help of machine learning techniques, the data can be sorted to find the target group by applying algorithms to the dataset. Based on Recency, frequency and monetary (RFM) value customers purchasing behavior is segmented and the scope of this project is to divide customers based on different groups like loyal, new and churned customers and this is done by RFM table which is used to analyze customer value and K means algorithm is used to cluster the data and to determine the optimal clusters, elbow method is used. The obtained data is then used for further analysis by the organizations to improve the quality of the product, services offered to the customers and develop their relation which can help to improve sales and plan marketing strategy. Every person is different from one another and we don’t know what he/she buys or what their likes are but, with the help of machine learning technique one can sort out the data and can find the target group by applying several algorithms to the dataset.

Customer Segmentation With Machine Learning for Online Retail Industry

In this study, a comparative analysis of various techniques is presented on customer segmentation methods based on online retail data. A few unsupervised machine learning (ML) clustering models such as K-means clustering model, hierarchical clustering model, Density-based Spatial Clustering of Applications with Noise (DBSCAN) model and a traditional model based on recency, frequency and monetary (RFM) clustering are evaluated in terms of the insight each model offers. The traditional model is included in the analysis since clustering models are not optimization models and the goodness of unsupervised models could only be evaluated with a practical business approach. The results are shared, and each model is assessed in terms of usability for marketing and communication strategies. At the end, the strengths and weaknesses of each model are discussed, and a methodology is proposed for selecting the best clustering method when facing the customer segmentation problem. A detailed literature review is also presented covering the developments in the field of artificial intelligence, clustering models in ML and examples of customer segmentation implementations in various industries.

Electric vehicle user classification and value discovery based on charging big data

With the rapid development of electric vehicles (EVs) in recent years, it is important to understand the varied EV users for EV sector business innovation. Therefore, identifying different types of EV users and implementing differentiated marketing strategies can assist charging service enterprises in improving profitability and user loyalty. Recency, frequency and monetary (RFM) model is an important data mining method that has important practical applications in customer relationship management and direct marketing fields. To classify EV users, an integrated approach incorporating an extended RFM model, a two-stage clustering method, and the Entropy Weight Method is proposed in this study. Analysis results demonstrate that 7426 EV users are divided into six groups, namely “high value users”, “key users to maintain”, “key users to develop”, “potential users”, “new users” and “lost users”. To estimate the performances of the proposed approach, the traditional cluster algorithm and fuzzy c-means method are compared with the improved entropy-cluster algorithm by using the intraclass method. The results indicate that the proposed approach is more robust than other methods. Finally, we develop related marketing strategies for each group of EV users to assist charging service enterprises in improving their marketing effectiveness and financial performance.

Customer interaction strategy, brand purpose and brand communities

PurposeCustomer interactions are integral to service brands. Indeed, many product brands have added services in order to create more opportunity for customer interaction. This paper deals with the strategic use of customer interactions to build a strong brand. Customer interaction strategy has evolved considerably beyond traditional sales and advertising in how customer interactions are conducted. Yet the Pareto rule (e.g. 80% of sales from 20% of customers) continues to guide who brands interact with. Conventional wisdom is that firms should target heavy users or customers high on the recency, frequency and monetary (RFM) value of purchases. The purpose of this conceptual paper is to examine how brands can use brand purpose and brand communities to target light users and even nonuser stakeholders as well.Design/methodology/approachThere is reason to hypothesize that brands can go beyond the heavy user limit of targeting customer interactions by developing brands around purpose. Purpose can be based on engagement with personal goals or values or on societal values about doing good for others. Brand purpose can be reinforced by either life purpose brand communities or societal purpose brand communities.FindingsThe paper reviews findings in the brand interaction, brand purpose and brand communities literature.Research limitations/implicationsThe two types of brand purposes and brand communities offer the possibility of growing a brand by targeting customer interactions at light users and nonuser stakeholders.Practical implicationsThere are important future research issues to be addressed, but this approach could increase the value of brands to consumers and extend the life cycle of brands for organizations.Social implicationsFurthermore, the concept of societal brand purpose could allow firms to focus on creating shareholder value as well as addressing social and environmental problems.Originality/valueThis paper broadens the current conception of customer interaction strategy and is thus relevant to relationship and experience marketing.

A Novel Hybrid Segmentation Approach for Decision Support: A Case Study in Banking

Abstract Improving decision-making has become of paramount importance for gaining a competitive edge across organizations. Segmentation/clustering is a key enabler for enhancing decision-making. The RFM (Recency, Frequency and Monetary) is a scoring model, which consists of three parameters that is widely used for the same purpose, especially for customer-centric organizations. However, the current RFM still has some drawbacks that diminish the effectiveness of decision-making and thus, research is still needed in this area. In an attempt to fill this gap, a novel data-driven weighted model is proposed. It introduces a new parameter; ‘Adoption’, to be added to the existing RFM parameters. The research contribution is extended to hybrid the proposed model with a clustering technique to segment data into distinct and meaningful groups or clusters. The hybrid model was applied on real bank data as a case study to enhance the usage of its digital channels. The results showed that bank customers were grouped into four distinct clusters. The bank stakeholders were able to identify the characteristics of customers and develop their relevant strategies accordingly. The developed model was evaluated by measuring its stability and robustness from both scientific and business perspectives.

Multi-Behavior RFM Model Based on Improved SOM Neural Network Algorithm for Customer Segmentation

Previous research on RFM (recency, frequency, and monetary value) models focused on only one type of user behavior data, i.e., the purchase behavior, without considering the interactions between users and items, such as clicking, favorite, and adding to cart. In this study, we propose a novel solution for deconstructing the multiple behaviors of consumers in a specific period and performing customer segmentation in an application promotion system called multi-behavior RFM ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MB-RFM</i> ) based on the self-organizing map (SOM) algorithm. First, using the R, F, and M values, we analyzed the weight relationship between multiple behaviors of users and items using the superiority chart and entropy value methods. Each behavior ascribed to a customer was considered to be a part of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MB-RFM</i> model values, which were then used to classify customers using an improved SOM neural network. Subsequently, various promotion strategies were developed according to customer categories that can help application vendors in improving their application utilization and implementing targeted promotion strategies. To prove the effectiveness of the proposed method in sparse datasets, two real-world datasets were used to perform experiments, whose results demonstrated that the classification performance of our method was significantly more accurate.

Customer segmentation to identify key customers based on RFM model by using data mining techniques

The main goal of this research is to identify key or strategic customers using the RFM model. In this part after determining the amount of RFM (recency, frequency, and monetary) in, registered transactions of one store in Iran (Refah Chain Store) at a time about seven months from 23 September 2017 to 20 April 2018 (71161 transactions as final inputs were used), the weight of each variable according to the fuzzy analytic hierarchy process was determined. At the next stage customers using the K-means and Two-step’s algorithms were clustered and K-means the method according to the Silhouette index was the better algorithm of this letter. According to the results, customers were segmented into three parts and customer lifetime value was calculated and for identifying key or strategic customer segmentation, the clustering process was repeated and priorities of all clusters were indicated. Results of data analysis are below: Segment 3: customers of this segment were 3425 members and 11.5% of all company customers were the most loyal customers those are identified as golden customer's segment and all of the variables were higher than average of all data. This research identified the valuable customers for the shop, and it gives them a chance to choose goal customers and invest in them, but it doesn’t mean that the shop doesn’t pay attention to the other customers, and they limited their effort to satisfy their profitable customers but it means that they allocated a suitable budget for performing the relationship programs with customers

New fuzzy RFMD model. A method to identify new customer profiles due to an increase in online activity. Case study of a retail business

Gaining customer loyalty has become one of the main objectives of all companies. Retailers, especially the online ones, have the advantage of knowing their customers’ historical purchase data, which provides them with an understanding of the customers’ buying patterns. A widely-used tool in strategic marketing and customer loyalty is segmentation based on the traditional Recency, Frequency and Monetary (RFM) model. Subsequently, the fuzzy RFM model proved to be an improvement on the traditional RFM model. There has been a change in the retail customer profile, with the growth of a new cluster, the “One-Shot Customer”, new customers that buy from a retailer just once and never come back. In response to this change, the fuzzy RFM model has been modified to include a new dimension capturing Length or Duration. This study presents the new fuzzy RFMD model (Recency, Frequency, Monetary and Duration model), which can be used to better identify that new, large group of customers. The paper also provides a case study based on an e-commerce clothing retailer. Its customer database was segmented using the k-means algorithm and the Isolation Forest algorithm was applied to identify and correctly treat possible anomalies. The Customer Lifetime Value and the weights for the RFMD attributes were calculated by applying the Analytic Hierarchy Process (AHP) model. Results reveal the improvement that the weighted fuzzy RFMD model offers to retailers, enabling them to detect the One-Shot Customers and thus optimize their strategic marketing plans.

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details.