# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 1 Introduction  
Customer churn refers to a customer's tendency to discontinue business with a company within a given period or contract [1]. Nearly all businesses encounter customer churn, often exacerbated by the limited visibility of customer data. This lack of insight leads to customer relationship crises and churn, making it difficult for companies to predict when customers are likely to leave [2]. Retaining existing customers is significantly more profitable than acquiring new ones, as retained customers generate higher profit margins [3]. Moreover, attracting a new customer costs 5-10 times more than retaining an existing one [4]. Therefore, preventing customer churn is critical for businesses offering ongoing services. Addressing churn effectively requires a deep understanding of customers' cognitive status and behaviour, enabling businesses to anticipate and mitigate churn risks proactively. To this end, leveraging analytical technologies to identify early signs of churn can be of great benefit. Extensive research has been conducted on customer churn analysis, utilizing various analytical and machine learning (ML) techniques to gain insights into customer churn tendencies. Most of these studies rely on feature engineering as a fundamental prerequisite for model training and predictive analysis [3, 5, 6]. However, traditional churn-related analytical studies have predominantly focused on demographic, product usage, and revenue-based features. Some recent studies have incorporated social network analysis features into churn models, which yield promising results [5, 6, 7]. While some prior studies have explored social media sentiment analysis [8], call transcripts [9], and support tickets [10] for churn prediction, there is still a gap in systematically integrating a structured knowledge base and generative AI into feature engineering for churn analysis. Our approach, ChurnKB, extends prior work by combining knowledge-driven text mining with generative AI to enhance feature extraction and improve predictive performance [11]. Leveraging knowledge bases, which integrate domain knowledge and experts' experience, can enhance the feature engineering process [12, 13, 14]. Human expertise and intuition play a crucial role in feature engineering, a fundamental step in ML projects that involves designing and selecting relevant features [15]. In our previous work [16], we explored various psychological and cognitive science sources to develop a domain-specific knowledge base related to mental health disorders, referred to as the Mental Disorder Knowledge Base (mKB). Our findings indicated that depression is closely linked to negative emotions such as sadness, anger, and disgust. Building upon the promising results of that study and recognizing that negative cognitive status, emotions, and feelings (e.g., dissatisfaction with a product or service) increase the likelihood of churn [1], we propose the development of the Customer Churn-related Knowledge Base (ChurnKB) to enhance customer churn analysis. Recent advancements in generative AI (GenAI) have expanded the opportunities available to businesses, allowing them to enhance various aspects of their operations beyond what was previously considered achievable [17]. GenAI is already transforming industries such as journalism, visual arts, and customer service by enabling automated content creation [18]. ChurnKB leverages generative AI in conjunction with textual data mining techniques to extract churn-related features from customer-generated content, such as emails, chat logs with support agents, website reviews, and social media feedback. Unlike traditional NLP-based approaches (e.g., rule-based text mining, sentiment analysis, and domain-specific lexicons), which rely on predefined heuristics, generative AI offers the flexibility to handle unstructured text, uncover latent features, and generate contextually enriched representations of customer behaviour [19].