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When conducting direct marketing activities, companies strive to know whom to target with a marketing incentive to maximize the campaign effect. For example, which customer should receive churn prevention incentive to minimize overall churn rate? Uplift modeling is a promising approach to answer such a question. It allows to separate customers who would likely react positively to a treatment from those who would remain neutral or even react negatively. However, while different uplift approaches have been proposed, they usually suffer from high volatility and their performance often depends largely on data set and application context. Thus, it is difficult for practitioners and researchers to apply uplift modeling. To overcome these problems, we propose a weighted ensemble of different uplift modeling approaches to reduce volatility and improve robustness. We evaluate the novel approach against single uplift modeling approaches on multiple data sets and find that the ensemble is indeed more robust.

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Like other media industries before, radio broadcasting is increasingly facing competition from new media platforms and changing consumer expectations. Many broadcasters are experimenting with possible solutions and are changing their production processes. While this is necessary, research is needed to capture the whole phenomenon of digital transformation of radio broadcasting. We conducted exploratory qualitative content analysis on talks of radio practitioner to identify current challenges, possible solutions, and specific aesthetics that shape current and future radio experience. We conceptualize the case of digital transformation of radio from the perspective of service-dominant logic and digital service innovation and discuss relevant areas of service innovation. We thus offer orientation for practitioners and contribute to a rather new, yet fruitful area of research for the information systems discipline.