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Intent-Aware Audience Targeting for Ride-Hailing Service

  • Yuan Xia
  • Jingbo ZhouEmail author
  • Jingjia Cao
  • Yanyan Li
  • Fei Gao
  • Kun Liu
  • Haishan Wu
  • Hui Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

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.

Keywords

Audience targeting Location based service Marketing campaign 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuan Xia
    • 2
  • Jingbo Zhou
    • 1
    • 3
    Email author
  • Jingjia Cao
    • 2
    • 5
  • Yanyan Li
    • 1
    • 3
  • Fei Gao
    • 2
  • Kun Liu
    • 2
  • Haishan Wu
    • 4
  • Hui Xiong
    • 1
    • 3
  1. 1.Business Intelligence Lab, Baidu ResearchBeijingChina
  2. 2.Baidu IncBeijingChina
  3. 3.National Engineering Laboratory of Deep Learning Technology and ApplicationBeijingChina
  4. 4.SenSight.ai Ltd.BeijingChina
  5. 5.Beijing Jiaotong UniversityBeijingChina

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