1st Workshop on End-End Customer Journey Optimization

What is the workshop about ?

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 content on a web page? What is the most appropriate time to show a 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.

For the holistic/long term  success of a customer on a platform - the key is to understand the levers that can help make customers more successful on the platform in the long term by estimating customers’ growth and retention patterns, lifetime value, interest to buy new products, propensity to churn, etc. Also, it is critical to not only predict success/propensities/lifetime  but also be able to take the customer to a more successful path on the platform. Throughout the user journey and life cycle, there are interesting opportunities for customer optimization.

• First is new user Acquisition: 

  • Targeting optimization: which user group is worth targeting?

  • Ads design and channel optimization: which ads design and channel would generate the best conversions and customer value across multiple products? 

  • Cost optimization: how to optimize bidding strategy to maximize scale at an efficiency guard-rail such as LTV/CAC?

• New User Onboarding:

  • How to make the onboarding flow as smooth as possible?

  • How to identify and fix new user pain points?

  • How to leverage messages and paid levers such as promotions intelligently to move users through the funnel?

• Mature User Experience:

  • How to provide the best product experience to users - product design, recommendation algorithm, search algorithm, pricing strategy, incentives, segmentation?

  • What’s the best way to communicate with our customers? How to send emails / push notifications to the right audience at the right time with a proper frequency?

•  Churn Prevention and Win-back:

  • How to prevent user churn?

  • How to downsell or cross sell to prevent churn?

  • How to win back churned users ?

The goal is to provide a forum so that industrial practitioners can expose real-world challenges and share practical experiences; academic researchers can popularize state-of-art research; and collaboration between the two can be fostered.

Call for Paper

We invite submissions of papers describing ML and data science solutions for  customer journey optimization. These include solutions in the space of sales, marketing, go-to-market, monetization data science, with emphasis on building  holistic solutions for new user acquisition and onboarding, user retention and long term success, churn prevention,  upsell, cross-sell, pricing optimization, to name a few. From a machine learning/AI perspective, this translates into interesting problems in the area of 

• Semi-supervised and multi-task learning frameworks/algorithms to handle complex prediction and optimization scenarios.

• Deep learning networks to model end-end customer life cycle.

• Embeddings and representation learning for customers and their related entities.

• Reinforcement learning to iteratively optimize personalized policies for maximizing user experience.

• MAB to select the optimal design and content dynamically through explore-and-exploit.  

• Propensity scoring to target the customers who are ready to buy from you

• Uplift modeling to predict the effect of intervention on user behavior

• LTV prediction to get a measurement of customers who are more likely to be successful. 

• Causal inference: Understand LTV changing events 

• Churn prediction to  predict which users are likely to churn and design interventions accordingly

 Key Dates

CMT portal opens: April 15th, 2022

Submission deadline: June 7th, 2022 (for abstract), June 12, 2022 (for paper)

Author notification: June 23rd, 2022