Customer Portfolio Analysis Using the SOM

In order to compete for profitable customers, companies are looking to add value using Customer Relationship Management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analyzing the profitability of customers. This study was made for a case organization, who wanted to identify their profitable and unprofitable customers, in order to gain knowledge on how to develop their marketing strategies. Data about the customers were gathered from the case organization’s own database. The Self-Organizing Map (SOM) was used to divide the customers into segments, which were then analyzed in light of product sales information

Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour

The aim of study is to discover outlier of customer data to found customer behaviour. The customer behaviour determined with RFM (Recency, Frequency and Monetary) models with K-Mean and DBSCAN algorithm as clustering customer data. There are six step in this study. The first step is determining the best number of clusters with the dunn index (DN) validation method for each algorithm. Based on the dunn index, the best cluster values were 2 clusters with DN value for DBSCAN 1.19 which were minpts and epsilon value 0.2 and 3 and DN for K-Means was 1.31. The next step was to cluster the dataset with the DBSCAN and K-Means algorithm based on the best cluster that was 2. DBSCAN algorithm had 37 outliers data and K-means algorithm had 63 outliers (cluster 1 are 26 outliers and cluster 2 are 37 outliers). This research shown that outlier in DBSCAN and K-Means in cluster 1 have similarities is 100%. But overal outliers similarities is 67%. Based the outliers shown that the behaviour of customers is a small frequency of spending but high recency and monetary

COMBINING VISUAL CUSTOMER SEGMENTATION AND RESPONSE MODELING

Customer Relationship Management (CRM) is a central part of Business Intelligence and sales campaigns are often used for improving customer relationships. This paper explores customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than one million customers

Development a Spatial Recommender System for Covid 19 Vaccination Center Based on Fuzzy Method Type II (Study Area: District 6 of Tehran)

Covid 19, also known as coronavirus, is an infectious disease caused by the SARS-CoV-2 virus. Anyone can get Covid 19 at any age and become seriously ill or even die. In the meantime, vaccines have saved millions of lives. Covid vaccines are effective for most people 18 years of age and older with autoimmune disorders or underlying diseases. The recommender systems are an intelligent complement to the retrieval of information and refining concepts by analyzing user behaviors and one of the main tools in overcoming the problem of information redundancy. Therefore, in the process of vaccination centers recommending, uncertainty in preferences can be modeled and personalized recommendations can be performed. Therefore, by using the information of users' preferences and properties of vaccination centers based on knowledge-based recommender systems, appropriate arguments can be made about which centers are suitable for users

Clustering of 27,525,663 death records from the United States based on health conditions associated with death: an example of big health data exploration

Background: Insight into health conditions associated with death can inform healthcare policy. We aimed to cluster 27,525,663 deceased people based on the health conditions associated with death to study the associations between the health condition clusters, demographics, the recorded underlying cause and place of death. Methods: Data from all deaths in the United States registered between 2006 and 2016 from the National Vital Statistics System of the National Center for Health Statistics were analyzed. A self-organizing map (SOM) was used to create an ordered representation of the mortality data. Results: 16 clusters based on the health conditions associated with death were found showing significant differences in socio-demographics, place, and cause of death. Most people died at old age (73.1 (18.0) years) and had multiple health conditions. Chronic ischemic heart disease was the main cause of death. Most people died in the hospital or at home. Conclusions: The prevalence of multiple health conditions at death requires a shift from disease-oriented towards person-centred palliative care at the end of life, including timely advance care planning. Understanding differences in population-based patterns and clusters of end-of-life experiences is an important step toward developing a strategy for implementing population-based palliative care

Penerapan Algoritma K-Means Dan Metode Marketing Mix dalam Segmentasi Mahasiswa dan Strategi Pemasaran

Idealnya, semakin tinggi jumlah pendaftar di suatu institusi pendidikan, semakin tinggi kualitas dari mahasiswa yang diterima. Akan tetapi, kondisi ideal ini tidak dicapai oleh Fakultas Sains dan Teknologi (FST) UIN Suska Riau disebabkan karena sedikitnya jumlah mahasiswa yang berasal dari sekolah menengah unggul yang mendaftar sebagai mahasiswa di FST UIN Suska Riau. Berdasarkan survey terhadap dosen di FST UIN Suska, masih terdapat kendala yang cukup besar dalam proses transfer ilmu kepada mahasiswa terutama dalam hal daya tangkap dan pemahaman mahasiswa terhadap materi ajar. Oleh karena itu, analisis lebih lanjut mengenai mahasiswa saat ini diperlukan untuk merancang sebuah strategi bagaimana menarik minat siswa yang berasal dari sekolah menengah unggul untuk mendaftar sebagai mahasiswa di FST UIN Suska Riau. Metodologi yang digunakan dalam penelitian ini adalah segmentasi mahasiswa menggunakan algoritma K-Means dan teknik kluster menggunakan algoritma Dunn Index. Pengumpulan data dilakukan sebanyak 614 mahasiswa FST tahun ajaran 2015 sampai 2017. Output dari segmentasi mahasiswa digunakan untuk menentukan target dari Marketing Mix 7P. Penelitian ini menghasilkan 2 kluster mahasiswa dengan nilai Dunn Index 1,99. Kuesioner disebarkan kepada mahasiswa dari dua kluster yang berbeda tersebut dan hasilnya menunjukkan bahwa terdapat tiga variabel Marketing Mix yang berpotensi untuk meningkatkan target pasar yaitu people, product, dan place. AbstractIdeally, the higher number of registrant in an educational institution, the higher quality of the accepted students. But, this ideal condition was not achieved by Faculty of Science dan Technology UIN Suska Riau due to the minimum number of students from top ranking senior high school who registered as student in FST UIN Suska Riau. Based on a survey of lecturers at FST UIN Suska, there are still significant limitations in the process of transferring knowledge to students, especially in terms of catching dan understdaning students towards teaching materials. Therefore, further analysis of current input of students is needed to design a strategy how to attract the high quality of high school students to register as student in FST UIN Suska Riau. The methodology in this research are student segmentations using K-Means Algorithm dan Clustering with Dunn Index Algorithm. The data collection derived from 614 students of FST in academic year 2015 until 2017. The output of student segmentations were used to determine the target using Marketing Mix 7P.  This research yield two clusters of students with Dunn Index Value was 1.99. The questionnaire were spreaded to students from the two different clusters dan the results showed that there are three variables of Marketing Mix that potentially increase the market target that is people, product, dan place

Приложение на математическото моделиране при управление на клиентски портфейли

Управлението на взаимоотношенията с клиенти е сред основните тенденции в съвременното маркетингово управление на бизнес организациите. Това е така, защото клиентите и стойността, която те могат да генерират за компанията, са от съществено значение за успеха на всяка компания. Динамичното развитие на научните достижения и практическите решения в тази насока обуславят интереса към търсене и проектиране на инструменти, с чиято помощ клиентите и взаимоотношенията с тях да се анализират, оценяват, развиват и управляват. Възможности за това предоставя концепцията „клиентски портфейл“, която разглежда клиентите и взаимоотношенията с тях като активи, способни да генерират стойност. Обект на изследване е управлението на портфейли от клиенти. Предмет на изследване са възможностите за оптимизиране на портфейли от клиенти чрез икономико-математическо моделиране. Основната теза, която се поддържа в изследването, е, че съществуват възможности за приложение и проектиране на икономико-математически модели при управлението на клиентски портфейли, които могат да се използват като адекватни управленски инструменти за вземане на решения във връзка с оптималното управление на взаимоотношенията с клиенти на организациите. Счита се, че икономико-математическото моделиране представлява адекватна методологическа база за оптимизиране на клиентски портфейли. Представен е критичен ретроспективен обзор на познатите модели за управление на клиентски портфейли и използваните в тях икономико-математически модели. Систематизирани са предпоставки за употреба на икономико-математически модели и критични фактори за техния успех при управление на клиентски портфейли