Protecting customer privacy when marketing with second-party data

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.