  Traditionally, firms have offered coupons to customer groups at predetermined
discount rates. However, advancements in machine learning and the availability
of abundant customer data now enable platforms to provide real-time customized
coupons to individuals. In this study, we partner with Meituan, a leading
shopping platform, to develop a real-time, end-to-end coupon allocation system
that is fast and effective in stimulating demand while adhering to marketing
budgets when faced with uncertain traffic from a diverse customer base.
Leveraging comprehensive customer and product features, we estimate Conversion
Rates (CVR) under various coupon values and employ isotonic regression to
ensure the monotonicity of predicted CVRs with respect to coupon value. Using
calibrated CVR predictions as input, we propose a Lagrangian Dual-based
algorithm that efficiently determines optimal coupon values for each arriving
customer within 50 milliseconds. We theoretically and numerically investigate
the model performance under parameter misspecifications and apply a control
loop to adapt to real-time updated information, thereby better adhering to the
marketing budget. Finally, we demonstrate through large-scale field experiments
and observational data that our proposed coupon allocation algorithm
outperforms traditional approaches in terms of both higher conversion rates and
increased revenue. As of May 2024, Meituan has implemented our framework to
distribute coupons to over 100 million users across more than 110 major cities
in China, resulting in an additional CNY 8 million in annual profit. We
demonstrate how to integrate a machine learning prediction model for estimating
customer CVR, a Lagrangian Dual-based coupon value optimizer, and a control
system to achieve real-time coupon delivery while dynamically adapting to
random customer arrival patterns.
