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

Gemfors, Olle and Persson, Erik (2018) MIOM05
Production Management
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
Gemfors, Olle and Persson, Erik
supervisor
organization
course
MIOM05
year
type
M1 - University Diploma
subject
other publication id
18/5603
language
English
id
8953528
date added to LUP
2018-06-28 11:01:04
date last changed
2018-06-28 11:01:04
@misc{8953528,
  abstract     = {{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.}},
  author       = {{Gemfors, Olle and Persson, Erik}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Customer Adoption of Data-Driven Services: A Model for Customer Prioritization}},
  year         = {{2018}},
}