Functional Cluster and Canonical Correlation Analysis of EU Countries by Number of Daily Deaths and Stringency Index During Covid-19 Pandemic

The danger of a global pandemic, such as the new Coronavirus (Covid-19),is obvious. This study aims to investigate the behavior and relationship of thenumber of daily new conrmed deaths per million and the stringency indexof twenty-seven European Union (EU) countries by utilizing functional clusteranalysis and functional canonical correlation analysis. Functional clusteranalysis was used to observe how countries cluster together according to dailydeaths during the time interval between March and July 2020. Functionalcanonical correlation analysis was also utilized to measure the correlationbetween the frequency index and daily deaths, and also to determine therelative positions of countries concerning their respective variability structure.The data is obtained from OWID. Here, it is seen that Italy, Spain,Belgium, and France are particularly aected by the pandemic during thetime interval within the EU countries, and the course of daily deaths is in adierent position compared to other EU countries. At the same time, a veryhigh relationship emerged between the stringency index and daily deaths asexpected

A customer segmentation model proposal for retailers: RFM-V

Today\u27s businesses have large quantity of demographic, economic and behavioral data on their customers with the rapid development of computer and internet technologies. Customer segmentation analyzes are carried out on the basis of various parameters in order to identify and group consumers with different needs and wishes and to develop marketing applications and solutions specific to each group. RFM analysis is a commonly used and well-known customer value evaluation tool for analyzing and classifying vast volumes of customer data quickly and effectively. It is used to numerically identify the correct customers by examining how recently, how often and to which monetary value a customer has made purchases. This study proposes a new model to be used in customer segmentation. In this model called RFM-V, the V parameter indicates the diversity of the customer\u27s purchases, which can define customer depth in terms of customer relationship management literature. The study also proposes a new matrix, Customer-Product Depth Matrix, with this new variable V added to the model. With this matrix created by using M and V parameters, customers can be examined in four quadrants according to their depth. Analysis findings can also be associated with basket analysis data in order to develop healthier marketing strategies and realize effective promotional suggestions