ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering

作者：Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Jin Foo, Alireza Jolfaei, Ambreen Hanif, Nasrin Shabani · 发表于：Algorithms · 年份：2025 · DOI：10.3390/a18040238 · 被引用次数：11 · 研究领域：Customer churn and segmentation、Consumer Market Behavior and Pricing、Data Mining Algorithms and Applications

Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of customers’ cognitive status and behaviours, as well as early signs of churn. Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. A robust ML-driven churn analysis depends on a well-developed feature engineering process. Traditional churn analysis studies have primarily relied on demographic, product usage, and revenue-based features, overlooking the valuable insights embedded in customer–company interactions. Recognizing the importance of domain knowledge and human expertise in feature engineering and building on our previous work, we propose the Customer Churn-related Knowledge Base (ChurnKB) to enhance feature engineering for churn prediction. ChurnKB utilizes textual data mining techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), cosine similarity, regular expressions, word tokenization, and stemming to identify churn-related features within customer-generated content, including emails. To further enrich the structure of ChurnKB, we integrate Generative AI, specifically large language models, ...