Leveraging business transformation with machine learning experiments
Paper in proceedings, 2019

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.

Retail industry

Dynamic pricing

Machine learning

Business transformation

Continuous experimentation

Author

David Issa Mattos

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for People, Architecture, Requirements and Traceability

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for Cyber Physical Systems

Helena Holmström Olsson

Malmö university

Lecture Notes in Business Information Processing

1865-1348 (ISSN)

Vol. 370 LNBIP 183-191

10th International Conference on Software Business, ICSOB 2019
Jyväskylä, Finland,

Subject Categories

Software Engineering

Information Science

DOI

10.1007/978-3-030-33742-1_15

More information

Latest update

1/9/2020 4