Abstract
In modern marketing, knowing the development of different market segments is crucial. However, simply measuring the occurred changes is not sufficient when planning future marketing campaigns. Predictive models are needed to show trends and to forecast abrupt changes such as the elimination of segments, the splitting of a segment, or the like. For predicting changes, continuously collected data are needed. Behavioral data are suitable for spotting trends in customer segments as they can easily be recorded. For detecting changes in a market structure, fuzzy-clustering is used since gradual changes in cluster memberships can implicate future abrupt changes. In this paper, we introduce different measurements for the analysis of gradual changes that comprise the currentness of data and can be used in order to predict abrupt changes.
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Minke, A., Ambrosi, K. (2014). Predicting Changes in Market Segments Based on Customer Behavior. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_22
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DOI: https://doi.org/10.1007/978-3-319-01595-8_22
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