Customer segmentation is the process of grouping customers based on similar characteristics such as behavior, shopping habits…so that businesses can do marketing to each customer group effectively and appropriately. Customer segmentation helps businesses determine different strategies and different marketing approaches to different groups. Customer segmentation helps marketers better understand customers as well as provide goals, strategies and marketing methods for different target groups. This paper aims to examine the customer segmentation using clustering method in statistics and unsupervised machine learning. The algorithms used are K-means and Elbow which are famous algorithms that have been successfully applied in many areas such as marketing, biology, library, insurance, finance... The purpose of clustering is to find meaningful market segments. However, the adoption and adjustment of parameters in the algorithms so as to find significant customer segmentations remain a challenge at present. In this paper, we used data of customers of Thu Duc CoopExtra and found significant customer segmentations which can be useful for more effective marketing and customer care by the supermarket.
Constructing portfolios with high returns and low risks is always in great demand. Markowitz (1952) utilized correlation coefficients between pairs of stocks to build portfolios satisfying different levels of risk tolerance. The correlation coefficient describes the linear dependence structure between two stocks, but cannot capture a lot of nonlinear independence structures. Therefore, sometimes, portfolio performances are not up to investors' expectations. In this paper, based on the theory of copula by Sklar (see [19]), we investigate several new methods to detect nonlinear dependence structures. These new methods allow us to estimate the density of the portfolio which leads to calculations of some popular risk measurements like the value at risk (VaR) of investment portfolios. As for applications, making use of the listed stocks on the Ho Chi Minh city Stock Exchange (HoSE), some Markowitz optimal portfolios are constructed together with their risk measurements. Apparently, with nonlinear dependence structures, the risk evaluations of some pairs of stocks have noticeable twists. This, in turn, may lead to changes of decisions from investors.
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