.png){kind=icon}
返回
Mining Willing-to-Pay Behavior Patterns from Payment Datasets
.png){kind=icon}
PRE
AI
DOI：10.1145/3485848.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The customer base is the most valuable resource to E-commerce companies. A comprehensive understanding of customers' preferences and behavior is crucial to developing good marketing strategies, in order to achieve optimal customer lifetime values (CLVs). For example, by exploring customer behavior patterns, given a marketing plan with a limited budget, a set of potential customers is able to be identified to maximize profit. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Moreover, effective future purchase estimation and recommendation help guide the customer to the up-selling stage. The proposed willing-to-pay prediction model (W2P) exploits the transaction data to predict customer payment behavior based on a probabilistic graphical model, which provides semantic explanation of the estimated results and deals with the sparsity of payment data from each customer. Existing work in this domain ranks the customers by their probabilities of purchase in different conditions. However, the customer with the highest purchase probability does not necessarily spend the most. Therefore, we propose a CLV maximization algorithm based on the prediction results. In addition, we improve the model by behavioral segmentation wherein we group the customers by payment behaviors to reduce the size of the offline models and enhance the accuracy for low-frequency customers. The experiment results show that our model outperforms the state-of-the-art methods in purchase behavior prediction.
Keyword:
Financial technology
customer lifetime value prediction
data sparsity
期刊
IF:
6.6
论文数:
1.5K
被引数:
6.2K
机构
引用论文
DC Offset Error Compensation Algorithm for PR Current Control of a Single-Phase Grid-Tied Inverter.png)
Energies
OAAI
EnergiesIF0
OAAI分享收藏
Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation面向POI推荐的空间感知分层协同深度学习
PREAI
分享收藏Virtual Prototyping Approach to Evaluate the Thermal Management of Li-Ion Batteries用于评估锂离子电池热管理的虚拟样机方法
PREAI
分享收藏
查看更多内容