Joint Incentive Optimization of Customer and Merchant in Mobile Payment Marketing.

In the mobile Internet era, mobile payment service becomes the foundation of inclusive finance, which brings convenience and security to people. Various marketing strategies are designed to encourage mobile payment activities by allocating incentives such as coupons or commissions to customers or merchants. We summary two significant issues. First, there is a phenomenon of mutual influence between merchants and customers, i.e., bipartite influence issue, thus making the independent optimization of customers and merchants non-optimal. Second, the redemptions of coupons are partially observed, as we can only observe that the customer redeems the coupon or not at a specific incentive value, but cannot observe that at other incentive value, i.e., data censorship issue. In this paper, we propose a novel joint incentive optimization framework to address the above two issues. We propose to use a graph neural network to represent customers and merchants jointly by modeling the underlying bipartite influences. We then formulate the response model under the hazard regression setting and model the hazard rate with a piecewise nonlinear function to capture the changes of responses to different incentive values. Finally, we propose a linear programming method to allocate approximated optimal incentive values to customers and merchants in real-time. Extensive offline and online experimental results demonstrate the effectiveness of our proposed approach.