Incentive schemes for privacy-sensitive consumers

Abstract

Businesses (retailers) often offer personalized advertisements (coupons) to individuals (consumers). While proving a customized shopping experience, such coupons can provoke strong reactions from consumers who feel their privacy has been violated. Existing models for privacy try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. We use a Markov decision process (MDP) model for this problem. Our model captures different consumer privacy sensitivities via a time-varying state, different coupon types via an action set for the retailer, and a cost for perceived privacy violations that depends on the action and state. The simplest version of our model has two states (“Normal” and “Alerted”), two coupons (targeted and untargeted), and consumer behavior dynamics known to the retailer.We show that the optimal coupon-offering strategy for a retailer that wishes to minimize its expected discounted cost is a stationary threshold-based policy. The threshold is a function of all model parameters: the retailer offers a targeted coupon if their belief that the consumer is in the “Alerted” state is below the threshold. We extend our model and results to consumers with multiple privacy-sensitivity states as well as coupon-dependent state transition probabilities.