330 Wirtschaft
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In times of e-commerce and digitalization, new markets are opening, young companies have the possibility to grow and new perspectives arise in terms of customer relationship. Customers require more possibilities of personalization. In the same time, companies have access to new and especially more information about the customer. Seems like it was a correlation that could evolve greatly if there weren't privacy issues. Vast amount of data about consumers are collected in Big Data warehouses. These shall be analyzed via predictive analytics and customers shall be classified by algorithms like clustering models, propensity models or collaborative filtering. All these subjects are growing in importance, as they are shaping the global marketing landscape. Marketers develop together with IT scientists new ways of analyzing customer databases and benefit from more accurate segmentation methods as that have been used until now. The following paper shall provide a literature review on new methods of consumer segmentation regarding the high inflow of new information via e-commerce. It will introduce readers in the subject of predictive analytics and will discuss several predictive models. The writing of the paper is not based on own empirical researches, but shall serve as a reference text for further researches. A conclusion will complete the paper.
Like many others, fashion companies have to deal with a global and very competitive environment. Thus companies rely on accurate sales forecasts - as key success factor of an efficient supply chain management. However, forecasters have to take into account some specificities of the fashion industry. To respond to these constraints, a variety of different forecasting methods exists, including new, computer-based predictive analytics. After the evaluation of different methods, their application to the fashion industry is investigated through semi structured expert interviews. Despite several benefits predictive analytics is not yet frequently used in practice. This research does not only reflect an industry profile, but also gives important insights about the future potential and obstacles of predictive analytics.