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. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their objectives.

Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates or 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. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their KPIs.

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.

At present, most machine learning research on customer optimization focuses on short term success of the customers by addressing questions such as - which users have a higher propensity to click? Where to place one ad/multiple contents on a web page? What is the most appropriate time to show content? There has been less/little thought put into building a coherent system for the long term/end-end customer optimization from acquisition by understanding a user's propensity to convert to a particular product at a certain time, to user's ability to be successful long term on a platform as measured by CLV (Customer Lifetime Value), to users' ability to buy more products (cross sell) on the same platform, and finally users propensity to churn. Currently, such models and algorithms are built in isolation to serve a single purpose which leads to inefficiencies in modeling and data pipelines. Also, most of the time the customer is not looked at as a single entity - but each product/subgroup within an organization (marketing, sales, product growth, go-to-market, product) considers the customer independently. This workshop aims to connect academic researchers and industrial practitioners who are working on, or interested in building holistic systems and solutions in the field of end to end customer journey optimization.

No abstract available.