This research presents a new approach to derive recommendations for segment-specific, targeted marketing campaigns on the product category level. The proposed methodological framework serves as a decision support tool for customer relationship managers or direct marketers to select attractive product categories for their target marketing efforts, such as segment-specific rewards in loyalty programs, cross-merchandising activities, targeted direct mailings, customized supplements in catalogues, or customized promotions. The proposed methodology requires customers' multi-category purchase histories as input data and proceeds in a stepwise manner. It combines various data compression techniques and integrates an optimization approach which suggests candidate product categories for segment-specific targeted marketing such that cross-category spillover effects for non-promoted categories are maximized. To demonstrate the empirical performance of our proposed procedure, we examine the transactions from a real-world loyalty program of a major grocery retailer. A simple scenario-based analysis using promotion responsiveness reported in previous empirical studies and prior experience by domain experts suggests that targeted promotions might boost profitability between 15 % and 128 % relative to an undifferentiated standard campaign. Keywords Cross-category purchases Á Target marketing Á Customized coupons Á Clustering Á Association rule mining JEL Classification C52 Á C55 Á M3
To process Big Data, the cloud computing architecture is used since several years. Service Level Objectives (SLO) become key indicators for the performances of the cloud computing providers. Controlling the cluster size is one of the main issues. Static decision procedures are available but they fail to provide an optimal decision in the presence of highly time varying service demands. The cluster has to be viewed as a dynamic system where the service demand is a disturbance to be compensated dynamically by a control action which is in this case the cluster size. PI feedback control has been already considered as a mean for dynamically controlling the size of the cluster [1]. However since the disturbance is measurable an adaptive feedforward compensation can be added for improving the performance of a feedback controller. A general algorithm for adaptive feedforward control in the context of cloud computing is proposed and analyzed. Simplified versions are also presented and analyzed. Experimental results obtained on the Grid'5000 (French nation-wide cluster infrastructure) will be presented.
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