Designing Incentive Schemes For Privacy-Sensitive Users

TL;DR

This paper develops a Markov decision process model to optimize personalized coupon incentives for privacy-sensitive consumers, accounting for subjective privacy perceptions and dynamic consumer states.

Contribution

It introduces a novel MDP framework capturing consumer privacy sensitivities and derives optimal coupon policies, extending to multiple states and uncertain feedback scenarios.

Findings

Stationary threshold-based policies are optimal for simple two-state models.

The threshold depends on model parameters and consumer belief states.

Extensions include multiple privacy states and noisy feedback handling.

Abstract

Businesses (retailers) often wish to offer personalized advertisements (coupons) to individuals (consumers), but run the risk of strong reactions from consumers who want a customized shopping experience but feel their privacy has been violated. Existing models for privacy such as differential privacy or information theory try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. We propose a Markov decision process (MDP) model to capture (i) different consumer privacy sensitivities via a time-varying state; (ii) different coupon types (action set) for the retailer; and (iii) the action-and-state-dependent cost for perceived privacy violations. For the simple case with two states ("Normal" and "Alerted"), two coupons (targeted and untargeted) model, and consumer behavior statistics known to the retailer, we show that a stationary threshold-based policy is the optimal coupon-offering strategy for a retailer that wishes to minimize its expected discounted cost. 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 this two-state model to consumers with multiple privacy-sensitivity states as well as coupon-dependent state transition probabilities. Furthermore, we study the case with imperfect (noisy) cost feedback from consumers and uncertain initial belief state.