Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41810

Capturing Value from Data: Revenue Models for Data-Driven Services

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Title: Capturing Value from Data: Revenue Models for Data-Driven Services
Authors: Schüritz, Ronny
Seebacher, Stefan
Dorner, Rebecca
Keywords: analytics
business models
data
revenue models
services
Issue Date: 04 Jan 2017
Abstract: Undisputedly, the amount of data is growing exponentially and huge opportunities exist to exploit them. New service business models are being built around value propositions based on data and analytics. Suitable revenue models need to reap the benefits of these value propositions. However, the question of how to best turn a value proposition into revenue for data-driven services is not systematically addressed in literature. \ \ We provide an overview of possible revenue models for data-driven services. Based on a sample of 100 start-ups, we apply qualitative analysis to identify different revenue models for newly established data-driven services such as subscription, gain sharing and multi-sided revenue models. \ \ This paper will contribute to the fundamental understanding of how companies can capture value from data-driven services. It should give guidance on the design and selection of appropriate revenue models and, thus, inspire new forms of revenue generation from the use of data. \
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41810
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.648
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Organizational Issues of Business Intelligence, Business Analytics and Big Data Minitrack



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