Intent-Aware Audience Targeting for Ride-Hailing Service

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 11053; pp. 136 - 151
• Main Authors: Xia, Yuan, Zhou, Jingbo, Cao, Jingjia, Li, Yanyan, Gao, Fei, Liu, Kun, Wu, Haishan, Xiong, Hui
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Audience targeting; Location based service; Marketing campaign
• ISBN: 9783030109967; 3030109968
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-10997-4_9

As the market for ride-hailing service is increasing dramatically, an efficient audience targeting system (which aims to identify a group of recipients for a particular message) for ride-hailing services is demanding for marketing campaigns. In this paper, we describe the details of our deployed system for intent-aware audience targeting on Baidu Maps for ride-hailing services. The objective of the system is to predict user intent for requesting a ride and then send corresponding coupons to the user. For this purpose, we develop a hybrid model to combine the LSTM model and GBDT model together to handle sequential map query data and heterogeneous non-sequential data, which leads to a significant improvement in the performance of the intent prediction. We verify the effectiveness of our method over a large real-world dataset and conduct a large-scale online marketing campaign over Baidu Maps app. We present an in-depth analysis of the model’s performance and trade-offs. Both offline experiment and online marketing campaign evaluation show that our method has a consistently good performance in predicting user intent for a ride request and can significantly increase the click-through rate (CTR) of vehicle coupon targeting compared with baseline methods.