Customer Adoption of Data-Driven Services: A Model for Customer Prioritization

Sammanfattning: Background In the latest years, developments in technology has created new opportunities within the area of after-sales services. Examples of such developments are the Internet of Things and Big Data, which can be used e.g. for predictive maintenance. Combined, the developments imply that there is potential gain for an OEM in using the IoT, Big Data and a transformed business model in their after-sales service offering. For OEMs operating within less mature industries it becomes of interest to explore these possibilities within their existing customer relationships. There are many aspects that need to be decided upon in this exploration, but one important aspect is which customers to begin with when developing and deploying the offering. Purpose The purpose of this thesis is to suggest a model that can be used by OEMs to find out which of their customers would most likely adopt data-driven service offerings. Methodology In order to fulfill the purpose of the thesis, a literature review was conducted to find out what affects a customer’s interest in a data-driven service offering. The results from the review were then used to suggest a model that could be used by OEMs for customer prioritization. The model was finally applied in a case study. Conclusions The customers’ interest in a data-driven service offering can be divided into two parts, which in this thesis are called Potential and Receptiveness. The former is based on the benefits the customer can get from implementing a data-driven service offering and the latter is based on factors affecting whether the customer is ready to adopt a data-driven service offering or not. These can be scored with the help of sub-areas and the scores can be used as a discussion basis for a prioritization of customers.