Points-based rewards programs are commonly used to encourage customer loyalty by offering free rewards after accumulating points from purchases.
Recent scrutiny has highlighted concerns about unfair practices in implementing these rewards programs.
A study focuses on designing fair points-based rewards programs while addressing challenges related to customer heterogeneity and unknown customer behavior.
Proposed learning algorithms aim to ensure fairness in rewards programs by limiting point devaluation through experimentation and achieving optimal regret in expectation.