Cloud computing applications and services go hand in hand, yet there is no clear
mechanism for ensuring that the cloud applications are designed from a customer’s
perspective. Likewise services can require adaptation for multiple customers of
stakeholders, which require differing user experience outcomes. This paper describes the
initial design and development of a predictive analytics cloud service application, which
uses historic customer data to predict the existing customers that are most likely to
churn. Service blueprinting, a service innovation method, was used as the underlying
design model for developing an initial shared understanding of the required service.
Personas were used in the requirements analysis to develop insights into multi-stakeholder
needs. Using the design science paradigm an extended cloud service design
theory is proposed, as an outcome of the ongoing development of this analytics
platform.