Leveraging Business Transformation with Machine Learning Experiments

  • David Issa MattosEmail author
  • Jan Bosch
  • Helena Holmström Olsson
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 370)


The deployment of production-quality ML solutions, even for simple applications, requires significant software engineering effort. Often, companies do not fully understand the consequences and the business impact of ML-based systems, prior to the development of these systems. To minimize investment risks while evaluating the potential business impact of an ML system, companies can utilize continuous experimentation techniques. Based on action research, we report on the experience of developing and deploying a business-oriented ML-based dynamic pricing system in collaboration with a home shopping e-commerce company using a continuous experimentation (CE) approach. We identified a set of generic challenges in ML development that we present together with tactics and opportunities.


Machine learning Continuous experimentation Retail industry Dynamic pricing Business transformation 



This work was partially supported by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation and by the Software Center.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringChalmers University of TechnologyGöteborgSweden
  2. 2.Department of Computer Science and Media TechnologyMalmö UniversityMalmöSweden

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