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• W2954496872 abstract "Abstract Cloud price modeling is the major challenge facing many cloud computing practitioners and researchers in the field of cloud economics, which is also known as “Cloudonomics.” Previous attempts mainly focused on a uniform market and used existing price models to explain the issue of revenue maximization for cloud service providers (CSPs) from a cost or internal rationality perspective but paid less attention to the cloud market segmentation for cloud business customers from a surplus value or external rationality perspective. This study considers both aspects of the value proposition. Based on the assumptions of the customers’ utility values for different market segments, we establish a framework of value-based pricing strategy and demystify the process of modeling and optimizing cloud prices for CSP to maximize its profits. This framework is built upon the theory of value co-creation for both customers and CSPs to form a business partnership. We show how to create four cloud pricing models, namely: on-demand, bulk-selling, reserved, and bulk + reserved. We also demonstrate how to identify the optimal price point of each model to maximize CSP’s profit by genetic algorithm. We exhibit that reserved, bulk + reserved, on-demand, and bulk-selling can deliver a profit margin of 203%, 183%, 166%, and 157% for CSPs respectively. While the reserved model provides the highest profit margin, it does not necessarily mean that CSPs should adopt one model only. We provide a novel solution that allows CSPs to achieve the maximum profit by offering multiple pricing models simultaneously to various customers in the segmented market. We argue CSPs should capitalize on cloud pricing rather than price to gain more cloud market share and profit. Thus, we present state-of-the-art cloud pricing for segmented business to business cloud market." @default.
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• W2954496872 date "2019-12-01" @default.
• W2954496872 modified "2023-10-16" @default.
• W2954496872 title "Value-based cloud price modeling for segmented business to business market" @default.
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• W2954496872 doi "https://doi.org/10.1016/j.future.2019.06.013" @default.
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