Stochastic service life cycle analysis using customer reviews

Abstract

This study proposes a stochastic service life cycle analysis to gauge where a service is in its life cycle and to give forecasts about its future prospects. We employ customer review data to measure customer-oriented service maturity and use a hidden Markov model to estimate the probability of a service being at a certain stage of its life cycle. Based on this, we also develop three indicators to represent the future prospects of a service’s life cycle progression. The main advantages of the proposed approach lie in its ability to model different shapes of life cycles without any supplementary information and to examine a wide range of services at acceptable levels of time and cost. We believe our method will assist firms in building stage-customised post-launch service strategies. A case study of mobile game services in the Apple App Store is presented.