1st Workshop on End-End Customer Journey Optimization

Abstract

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.