Segmentasi Pelanggan menggunakan Algoritma K-Means Sebagai Dasar Strategi Pemasaran pada LAROIBA Seluler

LAROIBA Seluler merupakan suatu perusahaan yang proses bisnis utamanya sebagai distributor pulsa elektrik di dareah Kabupaten Boja. Persaingan yang ketat si bidang sejenis mendorong LAROIBA untuk mengelola pelanggannya secara maksimal. Salah satu cara untuk mengelola pelanggannya adalah dengan mengelompokkan pelanggan dan menerapkan strategi pemasaran yang sesuai untuk setiap kelompok.Penelitian ini bertujuan untuk melakukan segmentasi pelanggan pada LAROIBA Seluler dengan memanfaatkan proses data mining dengan teknik Clustering. Metode yang digunakan adalah CRISP-DM dengan melalui proses business understanding, data understanding, data preparation, modeling dan evaluasi. Algoritma yang digunakan untuk pembentukan kluster adalah algoritma K-Means. K-Means menghasilkan model visual cluster dengan tools Rapidminer 5.2 yang merepresentasikan jumlah pelanggan di setiap kluster dengan menggunakan atribut RFM (Recency, Frequency dan Monetary). Kelompok pelanggan yang terbentuk adalah empat kelas dengan kluster pertama 4 pelanggan, kluster kedua 76 pelanggan, kluster ketiga 20 pelanggan dan kluster keempat dengan jumlah pelanggan 173

Using multiple criteria decision making models for ranking customers of bank network based on loyalty properties in weighted RFM model

One of the most basic requirements of financial institutes, governmental and private banks in the present age is to have a good understanding on customers' behaviors of bank network. It helps banks determine customer loyalty, which yields profit making for bank. On the other hand, it is important to know about credit risk of customers with the goal of decreasing loss and better allocation of bank resources to applicants of receiving loan. According to nature of customer loyalty discussion and credit risk, these two issues are separately studied. The present article deals with studying customer loyalty and prioritizing based one private bank in Kurdistan province. The proposed model of this paper studies customer loyalty by using Recency Frequency Monetary (RFM) factor for prioritizing customer based on loyalty properties and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In addition, in order to calculate the relative importance coefficient or weight of loyalty properties in RFM method, the pair wise comparison matrix based on analytical hierarchy process (AHP) is used. Results show that in the present study, necessarily customers having higher average monetary value during a specified time period does not have much higher priority compared with other customers

Modelling Customers Lifetime Value For Non-Contractual Business

Due to the increasing importance placed on customer equity in today's business environment, many companies are focusing on the notion of customer loyalty and profitability to increase market share. Building a successful Customer Relationship Management (CRM), a company starts from identifying true value and customer loyalty because customer value can provide basic information to spread more targeted and personalized marketing. In this paper, customer lifetime value (CLV) is used for customer segmentation in non-contracted businesses. The results obtained from this study are very acceptable. CLV has successfully analyzed and produced a fairly strong assumption about the value possessed by each customer whether they will make a return transaction or not

Customer Segmentation of Cross-border E-commerce based on FRMD Using Unsupervised Machine Learning

As buyers buy things from beyond national borders, cross-border e-commerce has quietly gathered significant momentum. E-commerce, which may be loosely described as the usage of the Internet as a medium for commercial transactions and the dissemination of market information, is expected to play an increasingly significant role in fueling economic expansion throughout the world. Under the assumptions of traditional mass marketing, consumers are all the same. The business has a unified strategy for producing, delivering, and engaging with customers, enabling it to save time and money while expanding its customer base to new heights. Companies relied much more heavily on mass marketing before consumer data was easily accessible. Big data has caused the proliferation of market segmentation. Market segmentation in the context of cross-border e-commerce is the process of identifying distinct groups of international customers and categorizing them so that targeted advertising campaigns may be developed. We extend the traditional FRM (frequency, recency, and monetary value) analysis to include the geographical distance of the foreign customers from the e-commerce company. This research used 3,000 overseas customer data from 8 different e-commerce stores. The unsupervised hierarchical clustering is based on four dimensions, namely, frequency of shopping, recency of transaction, monetary values of the purchased items, and finally, the geographical distance of the foreign customers. Companies of all sizes utilize market segmentation to hone their strategy and provide the highest quality products for their specific target populations

Image-Guided Surgical Robotic System for Percutaneous Reduction of Joint Fractures

Complex joint fractures often require an open surgical procedure, which is associated with extensive soft tissue damages and longer hospitalization and rehabilitation time. Percutaneous techniques can potentially mitigate these risks but their application to joint fractures is limited by the current sub-optimal 2D intra-operative imaging (fluoroscopy) and by the high forces involved in the fragment manipulation (due to the presence of soft tissue, e.g., muscles) which might result in fracture malreduction. Integration of robotic assistance and 3D image guidance can potentially overcome these issues. The authors propose an image-guided surgical robotic system for the percutaneous treatment of knee joint fractures, i.e., the robot-assisted fracture surgery (RAFS) system. It allows simultaneous manipulation of two bone fragments, safer robot-bone fixation system, and a traction performing robotic manipulator. This system has led to a novel clinical workflow and has been tested both in laboratory and in clinically relevant cadaveric trials. The RAFS system was tested on 9 cadaver specimens and was able to reduce 7 out of 9 distal femur fractures (T- and Y-shape 33-C1) with acceptable accuracy (≈1 mm, ≈5°), demonstrating its applicability to fix knee joint fractures. This study paved the way to develop novel technologies for percutaneous treatment of complex fractures including hip, ankle, and shoulder, thus representing a step toward minimally-invasive fracture surgeries

Study about customer segmentation and application in a real case

The hospitality industry generates a huge variety of data that grows by the day, becoming incrinsingly difficult to analyse this data manually in order to build a good data model. A thorough understanding of current customer profiles enables better resource allocation and leads to better definition of product and market development strategies. Dividing customers into similar groups to help develop more objective and focused marketing messages for each of the segments. Thus, in the present dissertation methods of classification and segmentation of existing data in the literature review are studied. Then, a real case study is presented, using data from Property Management Systems of eight Portuguese hotels, four city hotels and four resort hotels. This data set consists of fortyone attributes but, after selection of the most predictive variables, only a subset of attributes is used for data modeling. Next, the classification and segmentation methods studied in the literature review are applied for extracting the relevant information. The results are analyzed and discussed to understand their suitability to study the particular characteristics of hotel reservations.O setor de hospitalidade gera uma enorme variedade de dados que crescem a cada dia, tornando-se fisicamente impossível analisar esses dados manualmente a fim de construir um bom modelo de dados. Um profundo entendimento dos perfis dos atuais clientes permite uma melhor alocação de recursos e leva a uma melhor definição das estratégias de desenvolvimento de produtos e mercados. A divisão dos clientes em grupos semelhantes para ajudar a desenvolver mensagens de marketing mais objetivas e focadas para cada um dos seus segmentos. Desse modo na presente dissertação são estudados métodos de classificação e segmentação de dados existentes na revisão da literatura. De seguida, procede-se à apresentação de um estudo de um caso real, usando dados pertencentes a Sistemas de Gestão de Propriedade de oito hotéis portugueses, quatro hóteis de cidade e quatro hóteis de resort, este conjunto de dados é composto por quarenta e um atributos, mas, após uma selecção das variáveis com maior poder preditivo, apenas um subconjunto de atributos é utilizado para a modelação dos dados. Em seguida, são aplicados os métodos de classificação e segmentação estudados na revisão de literatura de modo a extrair informação relevante. Os resultados são analisados e discutidos para entender sua adequação ao estudo das características particulares das reservas de hotéis

Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means

Perusahan penyedia layanan internet service provider (ISP) memiliki jumlah pelanggan yang sangat banyak dan beragam. Dengan semakin banyak dan beraamnya jumlah pelanggan peusahaan akan sulit untuk mengetahui tipe pelanggan yang dimiliki oleh perusahaan. Akibatnya perusahaan akan kesulitan menerapkan strategi pemasaran yang tepat kepada konsumen. Dalam paper ini digunakan metode pillar K-means untuk melakukan segmentasi pelanggan. Algoritma pilar k-means untuk melakukan segmentasi pelanggaran . Algoritma Pillar merupakan metode optimasi untuk menentukan centroid awal dalam algoritma K-Means. Dengan mengoptimasi centroid awal maka akan menghasilkan cluster yang lebih baik . setelah memperoleh hasil cluster yang optimal selanjutnya tipe pelanggaran dianalisis dengan menggunakan metode RFM (Recency, Frequency, Montetery). Hasil penelitian ini menunjukan bahwa pllar K-means mampu mengoptimasi hasil cluster :  k = 4 dengan a = 0.5 dan b = 0.8 serta nilai silheoette 8 = 0.47103. Dari hasil segmentasi 150 pelangganan diperoleh tipe pelangganan yang terdiri Most Valuable Costmers (33) Most Growable Costomers (41), Migrators (23) dan Below Zero (53)