Accurate predictions of customers' future lifetime value (LTV) given their attributes and past
purchase behavior enables a more customer-centric marketing strategy. Marketers can …

Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to
a business. It is widely used in a variety of business scenarios to make operational decisions …

The prediction of the Customer Lifetime Value (CLV) is an important asset for tool-supported
marketing by customer relationship managers. Since standard methods based on purchase …

The ultimate goal of recommender systems is satisfying users' information needs in the long
term. Despite the success of current recommendation techniques in targeting user interest …

Companies across the globe are keen on targeting potential high-value customers in an
attempt to expand revenue, and this could be achieved only by understanding the customers …

In this paper, we propose a flexible machine learning framework to predict customer lifetime
value (CLV) in the Business-to-Business (B2B) Software-as-a-Service (SaaS) setting. The …

In industry, customer lifetime value (LTV) prediction is a challenging task, since user
consumption data is usually volatile, noisy, or sparse. To address these issues, this paper …

In non-contractual freemium and sharing economy settings, a small share of users often
drives the largest part of revenue for firms and co-finances the free provision of the product …

Accurate estimation of customer lifetime value (LTV), which reflects the potential
consumption of a user over a period of time, is crucial for the revenue management of online …

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.
com, a global online fashion retailer. CLTV prediction is an important problem in e …