Journal article
Protecting customer privacy when marketing with second-party data
International journal of research in marketing, v 34(3), pp 593-603
Sep 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Data sharing is a strategically important marketing initiative in many industries. Increasingly, companies seek to enhance the value of their customer data by supplementing this information with customer-level information from another company. However, this arrangement requires one company to reveal its customer-level data to another and face privacy risks which may result in substantial losses in brand value, customer trust, and competitive advantage, or legal penalties from not conforming to regulations. To overcome this problem, we propose a decision-theoretic approach for use by companies to protect their customer segmentation data prior to entering into collaborative arrangements. Our approach extends the literature because it allows the data provider to protect all customer segmentation data at the individual customer level instead of only at the aggregate level. We show that the optimal data protection strategy depends on a risk-return tradeoff based on the probabilities of misclassification of customers into segments, the opportunity costs of erroneously assigning segment membership, and the anticipated cost of a data breach.
Metrics
Details
- Title
- Protecting customer privacy when marketing with second-party data
- Creators
- Matthew J. Schneider - Drexel UniversitySharan Jagpal - Rutgers, The State University of New JerseySachin Gupta - Cornell UniversityShaobo Li - University of Cincinnati Medical CenterYan Yu - University of Cincinnati Medical Center
- Publication Details
- International journal of research in marketing, v 34(3), pp 593-603
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000412266700001
- Scopus ID
- 2-s2.0-85015420457
- Other Identifier
- 991019169655804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Business