A Cross Domain Method for Customer Lifetime Value Prediction in Supply Chain Platform

Zhiyuan Zhou, Li Lin, Hai Wang, Xiaolei Zhou, Wei Gong, Shuai Wang

Keywords: Lifetime Value Prediction, Supply Chain Platform, Cross-Domain Knowledge, Economics

Abstract: Accurate Customer Lifetime Value (LTV) predictions are crucial for customer relationship management, especially in Supply Chain Platforms (SCP), which involve effectively managing the service resources in business decision-making. Previous LTV prediction methods usually rely on ample historical customer data, which is not available in the early stages of a customer’s lifecycle. It makes the modeling of the historical customer data challenging due to the data sparsity. Besides, the long-taildistribution of customer LTV shows a long tail characteristic, making the LTV prediction a challenging task. To tackle the above issues, we propose CDLtvS, a novel Cross Domain method for customer Lifetime value prediction in SCP. It leverages rich cross-domain information from upstream platforms to enhance LTV predictions in downstream platforms. Firstly, CDLtvS pre-trains the customer representations by an LTV modeling framework named LtvS in source and target domains separately. Specifically, LtvS incorporates the Expert Mask Network (ExMN), which not only effectively models the long-tail distribution of LTV in single-domain but also resolves cross-domain learning model bias resulting from this distribution. Then, the various-level alignment mechanism is introduced to keep the consistency of knowledge transferring from source to target domains on both sparse and non-sparse data. Comprehensive experiments on real-world data from JD, one of the world’s largest supply chain platforms, demonstrate that CDLtvS achieves a normalized mean verage error of 0.3378 in LTV prediction, outperforming 16.3% to the baseline. Additionally, the improvements of ≥2.3% across various data sparsity levels (0% – 80%) provide valuable insights into cross-domain LTV modeling.

Track: Web Mining and Content Analysis

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

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Student Author: Yes

Submission Number: 442