Details

Autor(en) / Beteiligte

• Zhao, Hongying
• Zhao, Zhenyu
• Xu, Anbang
• Gupta, Neha
• Bay, Mert

Titel

2nd Workshop on End-End Customer Journey Optimization

Ist Teil von

• Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, p.5905-5906

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2023

Link zum Volltext

Quelle

ACM Digital Library

Beschreibungen/Notizen

• Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. By fostering collaboration and cross-disciplinary discussion, the workshop aims to accelerate progress in this rapidly evolving field.