  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 when acquiring new customers. Modeling
LTV is a challenging problem, due to its complex and mutable data distribution.
Existing approaches either directly learn from posterior feature distributions
or leverage statistical models that make strong assumption on prior
distributions, both of which fail to capture those mutable distributions. In
this paper, we propose a complete set of industrial-level LTV modeling
solutions. Specifically, we introduce an Order Dependency Monotonic Network
(ODMN) that models the ordered dependencies between LTVs of different time
spans, which greatly improves model performance. We further introduce a Multi
Distribution Multi Experts (MDME) module based on the Divide-and-Conquer idea,
which transforms the severely imbalanced distribution modeling problem into a
series of relatively balanced sub-distribution modeling problems hence greatly
reduces the modeling complexity. In addition, a novel evaluation metric Mutual
Gini is introduced to better measure the distribution difference between the
estimated value and the ground-truth label based on the Lorenz Curve. The ODMN
framework has been successfully deployed in many business scenarios of
Kuaishou, and achieved great performance. Extensive experiments on real-world
industrial data demonstrate the superiority of the proposed methods compared to
state-of-the-art baselines including ZILN and Two-Stage XGBoost models.
