scholarly journals Customer Attrition and Retention

2020 ◽  
Vol 9 (7) ◽  
pp. 925-927
Keyword(s):  
Logistic Regression ◽  
Customer Retention ◽  
Customer Segmentation ◽  
Attrition Rate ◽  
Competitive Environment ◽  
Rfm Model ◽  
Customer Attrition

In a competitive environment, organizations and firms are susceptible to customer attrition. Customer attrition and customer retention terms are widely spoken about. Customer retention which is quite the opposite of attrition is important for the company’s sustainability in today’s market. Many studies have come up with an attempt to find factors that influence customer retention. Firms have long desired to know who might end their relationship with them. Similarly, companies try to find how many existing customers did not return to purchase. Customer attrition is a problem that deals with clients and customers who are attrited from a particular brand or firm. In simple terminology it deals with the loss of profit associated with companies. This paper deals with the different ways to overcome the increasing attrition rate among customers. It also includes the implementation of customer segmentation using RFM model and K-means clustering. It also includes the prediction of customer retention using logistic regression.

scholarly journals Three-Layer Data Clustering Model for Multi-View Customer Segmentation using K-Means

2020 ◽  
Vol 8 (6) ◽  
pp. 1840-1846
Keyword(s):  
Customer Relationship Management ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Specific Information ◽  
K Value ◽  
Crm System ◽  
Clustering Model ◽  
Rfm Model ◽  
Novel Method ◽  
Score Range

Customer Relationship Management (CRM) system is one of the methods to increase customer satisfaction with the services provided by the company. The data in a CRM system sometimes have not been utilized properly to find specific information about customer needs. The data mining process can help companies to segment and retrieve useful information about customers. The segmentation of customers can be categorized into groups based on the RFM (Recency, Frequency, and Monetary) values of the customers. Several studies have used the RFM model as a basis for customer segmentation. However, the methods proposed in previous studies are very specific to certain industries and the range of RFM scores used is also very subjective. Also, as the business grows there are challenges with RFM score measurement. RFM score measurement needs frequent adjustments in which this adjustment is not easy using the existing methods. Therefore, this study proposed a novel method to overcome the limitation of the existing methods using combined K-Means and Davies-Bouldin Index (DBI) to find the appropriate range of RFM scores. Based on our study in a telecommunication industry the proposed method simplify the measurement of the RMF scores as the data grows. This research also provided the appropriate RFM score range through the K-Means approach based on the optimal K value of the K-Means algorithm. Our proposed method could be implemented in other industries since it only depends on the values of RFM from the correspond data for each customer.

scholarly journals Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

2020 ◽  
Keyword(s):  
Data Mining ◽  
Customer Segmentation ◽  
Food Distribution ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Marketing Activities ◽  
Using Data ◽  
Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

Integrating multi-criteria decision making and clustering for business customer segmentation

2015 ◽  
Vol 115 (6) ◽  
pp. 1022-1040 ◽  
Author(s):  
Hülya Güçdemir ◽  
Hasan Selim
Keyword(s):  
Decision Making ◽  
Clustering Algorithms ◽  
Customer Segmentation ◽  
Database Management System ◽  
Multi Criteria Decision Making ◽  
Business Markets ◽  
Content Type ◽  
Partitional Clustering ◽  
Rfm Model ◽  
Business Customers

Purpose – The purpose of this paper is to develop a systematic approach for business customer segmentation. Design/methodology/approach – This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. Findings – Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. Research limitations/implications – The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment. Practical implications – The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers. Social implications – The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies. Originality/value – This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

scholarly journals Prediksi Customer Churn dengan Algoritma Decision Tree C4.5 Berdasarkan Segmentasi Pelanggan untuk Mempertahankan Pelanggan pada Perusahaan Retail

2018 ◽  
Vol 1 (1) ◽  
pp. 16-24
Author(s):  
Ni Wayan Wardani ◽  
Gede Rasben Dantes ◽  
Gede Indrawan
Keyword(s):  
Decision Tree ◽  
Prediction Models ◽  
Confusion Matrix ◽  
Customer Segmentation ◽  
Customer Churn ◽  
Rfm Model ◽  
Performance Results ◽  
Retail Company ◽  
Number Of Customers ◽  
A Company

Customer is a very important asset for retail companies. This is the reason why retail companies should plan and use a fairly clear strategy in treating customers. With the large number of customers, the problem that must be faced is how to identify the characteristics of all customers and able to retain existing customers in order not to stop buying and moving to a competitor retail company. By applying the concept of CRM, a company can identify customers by segmenting customers while also being able to implement customer retention programs by predicting potential churn on each customer class. The data used comes from UD.Mawar Sari. Customer segmentation process uses RFM model to get customer class. UD. Mawar Sari customer class is dormant, everyday, golden and superstar. The construction of prediction models using the Decision Tree C4.5. The application of the prediction model obtains performance results, that is: Dormant: Recall 97.51%, Precision 75.18%, Accuracy 76.18%. Everyday: Recall 100%, Precision 99.04%, Accuracy 99.04%.  Golden: Recall 100%, Precision 98.84%, Accuracy 98.84%. Superstar: Recall 96.15%, Precision 99.43%, Accuracy 95.63%. Results of the evaluation with confusion matrix it can be concluded that the dormant customer class is a potentially churn customer class.

scholarly journals Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

2019 ◽  
Vol 8 (2) ◽  
pp. 78-83
Author(s):  
Novianti Puspitasari ◽  
Joan Angelina Widians ◽  
Noval Bayu Setiawan
Keyword(s):  
Customer Loyalty ◽  
Customer Segmentation ◽  
Number Of Clusters ◽  
Transaction Data ◽  
Model Based ◽  
Silhouette Coefficient ◽  
Rfm Model ◽  
A Company ◽  
Coefficient Method

Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.

scholarly journals PENERAPAN METODE K-MEANS DENGAN METODE ELBOW UNTUK SEGMENTASI PELANGGAN MENGGUNAKAN MODEL RFM(Recency, Frequency, & Monetary)

Repositor ◽  
2020 ◽  
Vol 2 (7) ◽  
pp. 945
Author(s):  
Adnan Burhan Hidayat Kiat ◽  
Yufiz Azhar ◽  
Vinna Rahmayanti
Keyword(s):  
Euclidean Distance ◽  
Motor Vehicle ◽  
Cluster Formation ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Manhattan Distance ◽  
Automotive Company ◽  
Rfm Model ◽  
Coefficient Method

Segmentasi pelanggan pada perusahaan merupakan tindakan yang dapat mempermudah perusahaan dalam mengambil keputusan ke depan. Pada penelitian ini data yang digunakan berasal dari perusahaan otomotif, PT Hasjrat Abadi Ambon. Data yang dipakai terdiri dari data transaksi dan pelanggan kendaraan bermotor. Penerapan model RFM dapat mengelompokkan pelanggan-pelanggan berdasarkan nilai variabel Recency, Frequency dan Monetary. Hasil dari model RFM akan memperoleh status baru pada tiap pelanggan dari skala terbaik sampai terburuk. Pelanggan yang telah memiliki status akan dikelompokkan menggunakan metode K-Means menjadi beberapa Cluster(kelompok). Dalam menentukan jumlah Cluster yang optimal maka diterapkan metode Elbow. Algoritma yang digunakan dalam pembentukan Cluster terdiri dari Euclidean Distance dan Manhattan Distance. Kedua algoritma akan dibandingkan kualitas pembentukan Clusternya menggunakan metode Silhoutte Coefficient. Hasil yang diberikan pada penelitian ini berupa data yang terbagi atas 5 kelompok dengan dilakukannya lima kali pengujian untuk menentukan centroid yang unggul. Cluster yang unggul akan dibuatkan visualisasi datanya untuk memudahkan perusahaan dalam mengambil keputusan. Berdasarkan penerapan Silhoutte Coefficient, algoritma yang lebih unggul yaitu Manhattan Distance dengan nilai s(i) sebesar 0.152695. Customer segmentation at the company is an action that can facilitate the company in making decisions going forward. In this study the data used came from an automotive company, PT Hasjrat Abadi Ambon. The data used consists of transaction data and motor vehicle customers. The application of the RFM model can classify customers based on the value of the Recency, Frequency and Monetary variables. The results of the RFM model will obtain a new status on each customer from the best to the worst scale. Customers who already have status will be grouped using the K-Means method into several Clusters (groups). In determining the optimal number of Clusters, the Elbow method is applied. The algorithm used in Cluster formation consists of Euclidean Distance and Manhattan Distance. The two algorithms will be compared the quality of the Cluster formation using the Silhoutte Coefficient method. The results given in this study are in the form of data divided into 5 groups by conducting five tests to determine superior centroids. Excellent clusters will be made of data visualization to facilitate the company in making decisions. Based on the application of Silhoutte Coefficient, a superior algorithm is Manhattan Distance with value s(i) : 0.152695.

scholarly journals An Effective Method to Understand Bank Customer Retention System

2020 ◽  
Vol 8 (6) ◽  
pp. 4279-4283
Keyword(s):  
Risk Analysis ◽  
Banking Industry ◽  
Banking System ◽  
Customer Retention ◽  
Customer Segmentation ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Retention System ◽  
Real Time Data ◽  
The Given

Banking industry is one of those industries where data is generated every day in large amounts. This data can be used for extracting useful information. Hence it is important to store, process, manage and analyze this data. It helps in making business lucrative. This data helps in making prediction which helps in solving problems that are faced by banks these days. People are constantly working on various aspects of Banking System like fraud detection, Risk Analysis etc. Various Machine Learning algorithms like CNN, ANN etc. have been used in order to study the patterns from such datasets. Here, we are focusing on risk analysis, customer retention and customer segmentation. In this paper, we have implemented classification algorithm, namely Decision Tree, for different aspects. Training of model is done on the given data and testing is done on real time data provided by the user. This study might help various banking systems to gain knowledge about their investment scheme for a particular customer. Thus, the banking companies will have a greater control on their customer and can develop policies that will benefit both the parties.

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