ABSTRACT
In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.