.png){kind=icon}
.png){kind=icon}
Return
User Information Demand Prediction and Intelligent Communication Strategies Based on Customer Lifetime Value
.png){kind=icon}
PRE
AI
H
C
DOI：10.1002/eng2.70704.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Customer Lifetime Value (CLV) is widely used as an analytical perspective for understanding user behavior and supporting marketing decision-making. However, accurately predicting user information demands across different lifecycle stages remains challenging due to the dynamic, sparse, and noisy nature of behavioral data. In this paper, we propose a sequential demand prediction framework that integrates multi-head attention mechanisms with LSTM networks to model temporal dependencies in user behavior sequences. Rather than directly predicting CLV, the proposed method leverages lifecycle-related behavioral patterns to infer user information demand tendencies over time. Experiments on a large-scale real-world dataset demonstrate that the proposed framework achieves stable and consistent performance in this challenging prediction setting, with an accuracy of 0.5293, a precision of 0.5162, a recall of 0.5194, an F1 score of 0.5156, and an AUC-ROC of 0.6414. These results indicate that incorporating temporal modeling and attention mechanisms can effectively support user demand inference and provide actionable insights for lifecycle-oriented marketing strategies.
Keywords:
computer science
customer lifetime value (CLV) analysis
lifecycle-oriented marketing
LSTM
user demand prediction
Journal
IF:
2
Papers:
362
Citations:
1.7K
Organization
Cited Papers