Exploit Customer Life-time Value with Memoryless Experiments

TL;DR

This paper introduces a novel LTV modeling approach and a fast optimization method based on memoryless experiments, significantly improving long-term customer value in real-world e-commerce applications.

Contribution

It presents a comprehensive LTV model and an efficient dynamic programming solution leveraging memoryless assumptions, addressing limitations of existing short-term focused methods.

Findings

Achieved a 10% increase in customer lifetime value in a large e-commerce app.

Validated the model's effectiveness on real-world datasets.

Demonstrated the method's applicability to recommendation systems.

Abstract

As a measure of the long-term contribution produced by customers in a service or product relationship, life-time value, or LTV, can more comprehensively find the optimal strategy for service delivery. However, it is challenging to accurately abstract the LTV scene, model it reasonably, and find the optimal solution. The current theories either cannot precisely express LTV because of the single modeling structure, or there is no efficient solution. We propose a general LTV modeling method, which solves the problem that customers' long-term contribution is difficult to quantify while existing methods, such as modeling the click-through rate, only pursue the short-term contribution. At the same time, we also propose a fast dynamic programming solution based on a mutated bisection method and the memoryless repeated experiments assumption. The model and method can be applied to different service scenarios, such as the recommendation system. Experiments on real-world datasets confirm the effectiveness of the proposed model and optimization method. In addition, this whole LTV structure was deployed at a large E-commerce mobile phone application, where it managed to select optimal push message sending time and achieved a 10\% LTV improvement.