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

SinkrOn ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 137-143
Author(s):  
Amir Mahmud Husein ◽  
Februari Kurnia Waruwu ◽  
Yacobus M.T. Batu Bara ◽  
Meleyaki Donpril ◽  
Mawaddah Harahap
Keyword(s):  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Monetary Model ◽  
Business World ◽  
Homogeneous Groups ◽  
Rfm Model ◽  
Proposed Model ◽  
Marketing Analysis ◽  
Purchase Patterns ◽  
Potential Customers

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.

scholarly journals KOMPARASI METODE CLUSTERING K-MEANS DAN K-MEDOIDS DENGAN MODEL FUZZY RFM UNTUK PENGELOMPOKAN PELANGGAN

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Elly Muningsih - AMIK BSI Yogyakarta
Keyword(s):  
Data Clustering ◽  
Small Data ◽  
Clustering Methods ◽  
Monetary Model ◽  
Clustering Method ◽  
Online Sales ◽  
Rfm Model ◽  
Potential Customers ◽  
Cluster 2 ◽  
Better Than

Abstract ~ The K-Means method is one of the clustering methods that is widely used in data clustering research. While the K-Medoids method is an efficient method used for processing small data. This study aims to compare two clustering methods by grouping customers into 3 clusters according to their characteristics, namely very potential (loyal) customers, potential customers and non potential customers. The method used in this study is the K-Means clustering method and the K-Medoids method. The data used is online sales transaction. The clustering method testing is done by using a Fuzzy RFM (Recency, Frequenty and Monetary) model where the average (mean) of the third value is taken. From the data testing is known that the K-Means method is better than the K-Medoids method with an accuracy value of 90.47%. Whereas from the data processing carried out is known that cluster 1 has 16 members (customers), cluster 2 has 11 members and cluster 3 has 15 members. Keywords : clustering, K-Means method, K-Medoids method, customer, Fuzzy RFM model. Abstrak ~ Metode K-Means merupakan salah satu metode clustering yang banyak digunakan dalam penelitian pengelompokan data. Sedangkan metode K-Medoids merupakan metode yang efisien digunakan untuk pengolahan data yang kecil. Penelitian ini bertujuan untuk membandingkan atau mengkomparasi dua metode clustering dengan cara mengelompokkan pelanggan menjadi 3 cluster sesuai dengan karakteristiknya, yaitu pelanggan sangat potensial (loyal), pelanggan potensial dan pelanggan kurang (tidak) potensial. Metode yang digunakan dalam penelitian ini adalah metode clustering K-Means dan metode K-Medoids. Data yang digunakan adalah data transaksi penjualan online. Pengujian metode clustering yang dilakukan adalah dengan menggunakan model Fuzzy RFM (Recency, Frequenty dan Monetary) dimana diambil rata-rata (mean) dari nilai ketiga tersebut. Dari pengujian data diketahui bahwa metode K-Means lebih baik dari metode K-Medoids dengan nilai akurasi 90,47%. Sedangkan dari pengolahan data yang dilakukan diketahui bahwa cluster 1 memiliki 16 anggota (pelanggan), cluster 2 memiliki 11 anggota dan cluster 3 memiliki 15 anggota. Kata kunci : clustering, metode K-Means, metode K-Medoids, pelanggan, model Fuzzy RFM.

Implementation of Clustering Algorithm Method for Customer Segmentation

2020 ◽  
Vol 17 (2) ◽  
pp. 1388-1395
Author(s):  
Nurmalasari ◽  
Anna Mukhayaroh ◽  
Siti Marlina ◽  
Sari Hartini ◽  
Sri Muryani ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Goods And Services ◽  
Customer Group ◽  
Digital Era ◽  
The Everyday ◽  
The Right ◽  
Online Shops ◽  
Potential Customers

The intense competition in the sale of goods and services in the digital era of e-commerce requires to manage customers optimally. Some online shops try to improve their marketing strategies by classifying their customers. This study aims to determine potential customers, namely loyal customers. Potential customers can be determined by customer segmentation. Sampling from several online shops in Indonesia. The model used for segmentation is RFM (Recency, Frequency, and Monetary) and data mining techniques, namely clustering method with the K-Means algorithm. The results of this segmentation research divide the customer into 2 clusters. The best number of clusters is determined based on the Davies Bouldin index. The first cluster is cluster 0 consisting of 261 customers with RFM Score between 111–543. The first cluster includes the Everyday Shopper group. The second cluster, cluster 1 consists of 102 customers with RFM Score 443–555. The second cluster includes the Golden Customer group. With the existence of research on customer segmentation, it is expected to help in grouping customers so that companies can determine the right strategy for each group of customers.

scholarly journals Customer Segmentation using RFM Model and K-Means Clustering

2021 ◽  
pp. 591-597
Author(s):  
Rahul Shirole ◽  
Laxmiputra Salokhe ◽  
Saraswati Jadhav
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Clustering Method ◽  
Good Relationship ◽  
Rfm Model ◽  
Input Dataset ◽  
Segmentation Approach ◽  
Business Analyst

Today as the competition among marketing companies, retail stores, banks to attract newer customers and maintain the old ones is in its peak, every company is trying to have the customer segmentation approach in order to have upper hand in competition. So Our project is based on such customer clustering method where we have collected, analyzed, processed and visualized the customer’s data and build a data science model which will help in forming clusters or segments of customers using the k-means clustering algorithm and RFM model (Recency Frequency Monetary) for already existing customers. The input dataset we used is UK’s E-commerce dataset from UCI repository for Machine Learning which is based on customer’s purchasing behavioral. At the very simple the customer clusters would be like super customer, intermediate customers, customers on the verge of churning out based on RFM score .Along with this we also have created a web model where an e-commerce startup or e-commerce business analyst can analyze their own customers based on model we created .So using this it will be easy to target customers accordingly and achieve business strength by maintaining good relationship with the customers .

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 Multi-Layer Blockchain-Based Security Architecture for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 772 ◽  
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammad Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Model Development ◽  
Base Stations ◽  
Smart Devices ◽  
Security Model ◽  
Cluster Heads ◽  
Authentication Mechanism ◽  
Proposed Model ◽  
Authentication And Authorization

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.

scholarly journals Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Milka Bochere Gesicho ◽  
Martin Chieng Were ◽  
Ankica Babic
Keyword(s):  
Clustering Algorithm ◽  
Distinct Pattern ◽  
Health Care Facilities ◽  
Health Facilities ◽  
Hiv Care ◽  
Care Providers ◽  
Homogeneous Groups ◽  
Reporting Requirements ◽  
Low Middle Income Countries ◽  
Over Time

Abstract Background The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV-indicators, from the time of DHIS2 implementation, using Kenya as a case study. Methods A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 and 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters. Results Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups. Conclusions The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks

2022 ◽  
pp. 1-9
Author(s):  
P. Vijayalakshmi ◽  
K. Muthumanickam ◽  
G. Karthik ◽  
S. Sakthivel
Keyword(s):  
Clustering Algorithm ◽  
Water Drop ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Uterine Wall ◽  
Health Issues ◽  
Testing Dataset ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Difference

Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.

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