The applications of some multivariable statistical methods in marketing research were performed on the white goods sector

Today's fast changing business environment leads companies to collect more information about their competitors, consumers or customers, and the business environment as a whole. With this goal in mind, the importance of marketing research has increased significantly in the past decade. One of the fastest growing sectors in the Turkish Economy, the white goods sector, has been making significant contributions to the overall economy with its improving technology, increasing production levels, export capacity and developing related industries, service, salespoint networks and employment opportunities that grow in line with the overall sector. There are a lot of factors that have impacted the fast growth of the white goods sector. One of these factors is the importance given to marketing research studies, being efficient in market growth, and being effective in promotions that provide consumers with appropriate opportunities. In our study, the applications of some multivariable statistical methods in marketing research were performed on the white goods sector, which is positioned in a highly competitive marketplace and which gives importance to marketing research. In the study, the products in the white goods sector, brand preferences and differences were analyzed from 129 sub variables obtained through the surveys, which were directed at some 245 people. Firstly, frequency tables were formed from descriptive statistical applications to study and interpret the distribution of the sample size on variables, after which it was evaluated at the theoratical statistics dimension. In the research, multivariable statistical methods were preferred due to their ability to make it possible to analyze together more than one specification about the subject or the event being evaluated. Multivariable statistical methods are defined under two main categories as "dependency" and "inter-dependency"; and in the research "dependent" methods are used. By not giving detailed emphasis on "inter-dependency" methods at the theoratical level, the applications and descriptions about these techniques were left for the future studies about the subject. From the "dependency methods", "CHAID" and "DISCRIMINANT" are the analysis applied to the white goods sector. A discriminant analysis has been used in the study to compare the differences among three groups classified by their income levels as to their needs in the white goods products, importance levels, satisfaction, recommendation to a relative, the brand used, and the brand preferred. Also in the study, it is intended to research the factors that affect the satisfaction from a white goods product in terms of functionality as well as the factors that determine the preference of a white goods brand for consumers. For this purpose, a tree-based classification analysis, CHAID, xiv has been used.As inclusive in the model according to the sample data set, it has been observed in the study that the variables of ADVERTISEMENT, PRICE, PRIOR USAGE EXPERIENCES, and THE WHITE GOOD PRODUCT CURRENTLY BEING USED are significant in determining the satisfaction level about white goods. Keywords: Marketing Research, White goods sector, Discriminant Analysis, AID / CHAID Analysis