Modeling cloud business customers’ utility functions

Abstract

Modeling a utility function for cloud business customers is one of the critical challenges facing many cloud service providers (CSPs) for their pricing strategy. It concerns how to measure various subjective experiences of the business customers and how to translate their cloud service experiences into a quantifiable unit, which can be determined by a utility function that reflects cloud resource consumption. The aim of this modeling process is to set up a pricing foundation so that CSPs can target a broader range of customers from various market segments and identify the optimal price point of their various pricing models. Previous studies have either focused on simple theoretical proof or drifted the meaning of utility between demand and supply or proposed a solution based on a uniform cloud market assumption. This paper proposes a novel and practical solution to define multiple utility functions based on a scenario of six cloud market segments, which are analyzed by three analytic approaches that are known as Markov chains analysis, queueing theory, and risk assessment. The entire pricing strategy emphasizes value co-creation between CSP and cloud business customers. In comparison with other methods, such as calibrated, price-quality, resource-based, simple linear, and capacity-aware, this method provides both internal and external rationalities for CSP to capture the subjective value of cloud business customers. Consequently, our experiment results show that this modeling method can increase a profit margin by 51% and decrease a unit cost by 22% for a CSP.