scholarly journals Segmentasi Konsumen Berdasarkan Model Recency, Frequency, Monetary dengan Metode K-Means

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Atik Febriani ◽  
Syahfara Ashari Putri
Keyword(s):  
Customer Value ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Customer Data ◽  
Rfm Model ◽  
External Sources ◽  
Customer Purchases ◽  
Business Behavior ◽  
A Company ◽  
Potential Customers

A good company is a company that is responsive to market changes and opportunities by utilizing existing data and information. Company data and information can come from internal or external sources. One of the internal data sources that can be utilized is customer data. This data will be used as the basis for determining customer segmentation. Segmentation is a process to determine customer characteristics with certain similarities, making it easier to extract information related to profitable customers. Customer business behavior can be seen from recency (last transaction period), frequency (number of transactions), and monetary (rupiah issued) or known as RFM analysis. The effective RFM analysis helps achieve the implementation of customer relationship management because this model is an important facility in measuring the profitability of customer value. To consider this RFM model, researchers use clustering which assumes that customers are in the same cluster, then consider customers with customers in the cluster. This clustering will display customer segmentation. This clustering method uses K-Means clustering. From the results of data processing, 3 clusters were formed from 25 customer data. Based on the clusters formed, it can be concluded that customer purchases have a different pattern. Clusters included in the segment of potential customers are cluster 1. Clusters are needed to get customers who previously had low R, high F, and high M values. While the strategy that needs to be improved is cluster 2.

scholarly journals Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

2020 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Rahma Wati Sembiring Brahmana ◽  
Fahd Agodzo Mohammed ◽  
Kankamol Chairuang
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Cluster Center ◽  
Cluster Group ◽  
Rfm Model ◽  
Silhouette Index ◽  
Marketing Activities ◽  
Potential Customers

A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.

scholarly journals Strategies of SMM Management, Comparison of Instagram and Facebook Social Networks

2017 ◽  
Vol 3 (5) ◽  
pp. 51
Author(s):  
Ramis Akhmedov
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Customer Relationship ◽  
Social Media Marketing ◽  
Crm System ◽  
Research Questions ◽  
Short Time ◽  
A Company ◽  
Potential Customers ◽  
The Ideal

<p class="Default">SMM occupies an important role in the lives of people and so many people are represented in social networks, it provides the ideal platform for companies so they can communicate with their current and potential customers. This study continues to explore how companies can use social media marketing to build and maintain relationships with customers. This investigates through conducted research questions. How SMM is effective in terms of CRM? Can Facebook replace CRM system? Why do people choose to follow a company on Instagram? To analyze more clearly the focus will be on Instagram and Facebook applications, which in a short time acquired great popularity among private users as well as among the companies. The purpose of this study is to indicate the integration of customer relationship management (CRM) with social media marketing (SMM) strategies, and defines its benefits for business.</p><p class="Default"> </p>

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.

Customer Relationship Management and Data Mining

2016 ◽  
pp. 1362-1401
Author(s):  
Niccolò Gordini ◽  
Valerio Veglio
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Value ◽  
Relationship Management ◽  
Global Market ◽  
Customer Relationship ◽  
Digital Revolution ◽  
Marketing Activities ◽  
Potential Customers

In the global market of today, Customer Relationship Management (CRM) plays a fundamental role in market-oriented companies to understand customer behaviors, achieve and maintain a long-term relationship with them, and maximize the customer value. Moreover, the digital revolution has made information easy and fairly inexpensive to capture. Thus, companies have stored a large amount of data about their current and potential customers. However, this data is often raw and meaningless. Within the CRM framework, Data Mining (DM) is a very popular tool for extracting useful information from this data and for predicting customer behaviors in order to make profitable marketing decisions. This research aims to demonstrate the classification decision tree as one of the main computational data mining models able to forecast accurate marketing performance within global organizations. Particular attention is paid to the identification of the best marketing activities to which firms should concentrate their future marketing investments. The criteria is based on the loss functions that confirm the accuracy of this model.

Customer Relationship Management (CRM) Implementation: Failures, Successes, And Lessons Learned

2018 ◽  
Vol 4 (04) ◽  
Author(s):  
Dileep Kumar Pagoti ◽  
Sajeev Thomson Mathew ◽  
Karthikeyan Manoharan
Keyword(s):  
Customer Relationship Management ◽  
Relationship Management ◽  
Implementation Process ◽  
Lessons Learned ◽  
Customer Relationship ◽  
Management Technique ◽  
Crm System ◽  
The Relationship ◽  
A Company ◽  
Potential Customers

Our paper discusses an important strategic management technique called Customer Relationship Management (CRM) and talks about the successes and failures companies face while implementing this concept in their organization. Customer Relationship Management (CRM) is a technology used to build relationship between an organization and existing customers or potential customers. CRM system records information about customers, manages marketing campaigns, finds sales opportunities and stores information/ data of the customers. This makes it easy to satisfy the customers, improve the relationship with the customer, increase sales, and improve the organization’s profitability. Another important feature discussed in the paper is the Scope of CRM and what roles do various entities play in the success of maintaining positive relationships with customers. In addition to this, our paper also dives into the implementation process of CRM in a company and the various software used in the real world. We also venture into the positive and negative aspects of implementing CRM in your company and where you can go right or wrong. Prime examples of various companies facing success or failures are mentioned in detail to help understand the effects of CRM implementation.

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.

Big Data In Banking: A Bird’s Eye View

GIS Business ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 1129-1139
Author(s):  
C. RADHA PRIYA ◽  
KANNIGA PRASHANTH
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Banking Industry ◽  
Big Data Analytics ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Risk Modeling ◽  
Customer Data ◽  
Customer Lifetime ◽  
Big Data Application

Banking industry is the backbone of any economy. It plays a very significant role in leading the country towards the growth path by improving the gross capital formation, which consecutively improves the GDP. Success of the banking industry depends on its ability to serve its customers efficiently and expeditiously. The functionality of the CRM (Customer Relationship Management) can be effectuated by felicitous use of customer data. Banks have voluminous data about their customers, which most of the banks failed to utilize in a well-timed manner. Banks can fortuitously satisfy their customers by offering much personalized and focused services by pursuing big data analytics and other hi-tech tools or applications. Big data analytics can be actuated in key areas like customer segmentation, offering customer lifetime value, fraud detection, risk modeling, etc. Preeminent banks in the industry are utilizing big data to leverage the accumulated customer data for improvising their services. Big data offers a promising scope of ventures to banks which consider it strategically. This article is attempts to present an overview of the big data application in the banking industry.

SEGMENTASI PELANGGAN BERDASARKAN PRODUK MENGGUNAKAN METODE K- MEDOIDS

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Putri Eka Prakasawati ◽  
Yulison H Chrisnanto ◽  
Asep Id Hadiana
Keyword(s):  
Test Data ◽  
Market Segmentation ◽  
Food Products ◽  
Customer Segmentation ◽  
Customer Data ◽  
Marketing Process ◽  
Food And Beverage ◽  
And Behaviors ◽  
Targeted Marketing ◽  
A Company

Market segmentation is a division of consumer groups that have different needs, characteristics and behaviors (heterogeneous) in a particular market so that it becomes a homogeneous market unit, in this case it is very helpful in a more targeted marketing process so that company resources can be used effectively and efficient for example makes it easy to distinguish markets and recognize competitors with the same segment. CV. Lampegan Jaya is a company engaged in the distribution of food and beverage products including Meses Tulip Chocolate, Vita Zone, Bintang Sobo Tea, Preso Tea, Okky Jelly Drink, Fruit Tea, Bima Energy Nails, Coptic Cappuccino, Mizon and My Tea. These products are distributed to outlets spread across Bandung, Cianjur, Cileunyi, Cimahi, Soreang and Sumedang. Distribution of products is carried out based on the demand for outlets for the product. In this study a system of classifying customer segmentation based on products. This system can classify customers based on the number of purchases and area. The process of this customer grouping system uses a K-Medoid clustring algorithm to classify customers based on segmentation on the product purchase amount and area. With test data of 6 regions, 600 customer data and 28 products. Keywords: Market Segmentation, k-Medoids, , Food Products

scholarly journals One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Jin Xiao ◽  
Bing Zhu ◽  
Geer Teng ◽  
Changzheng He ◽  
Dunhu Liu
Keyword(s):  
Customer Value ◽  
Missing Values ◽  
Credit Scoring ◽  
Customer Relationship ◽  
Classifier Ensemble ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
One Step

Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

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