# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 2 Background
### Customer Churn from an Analytical Perspective  
Understanding and predicting customer churn is crucial in today's data-driven world. Churn analysis aims to identify patterns and reasons behind customer departures. By analysing data from various sources, businesses can gain insights into why customers leave. ML models help predict potential churners, enabling companies to take action and retain valuable customers [27]. An ML workflow relies on a good combination of data, features, and models [20]. Features act as a bridge between raw data and insights. Selecting the right features simplifies model training and improves performance. Poor feature selection may require more complex models to achieve the same accuracy [28]. A feature is a measurable property of data used in ML. Features can be numerical (e.g., age and income), categorical (e.g., gender and country), textual (e.g., TF-IDF and word embeddings), temporal (e.g., date and time), geospatial (e.g., location and distance), or transactional (e.g., product prices) [28]. Some models work better with specific feature types. The number of features also matters. Too few may lead to poor predictions, while too many can increase complexity and costs [28]. Feature engineering is the process of transforming raw data into useful features for ML models. It involves creating new features, modifying existing ones, and ensuring they are informative and relevant [28; 29; 30]. Effective feature engineering enhances a model's predictive power and performance [31]. Feature engineering transforms raw data into structured representations for ML, optimizing model performance [27; 29; 32]. It encompasses feature generation, preprocessing, extraction, and selection. Feature generation derives new attributes from existing data, enhancing interpretability and prediction performance [33]. Preprocessing ensures data consistency through cleaning, transformation, and aggregation, supporting meaningful feature extraction [34]. Feature extraction identifies informative patterns, leveraging techniques like embeddings and named entity recognition [12]. Feature selection refines model inputs by prioritizing relevant attributes, reducing dimensionality and noise [35]. Additionally, domain knowledge-based feature engineering integrates expert insights to improve feature relevance, with approaches such as rule-based engineering and external knowledge incorporation [16; 36]. Studies have explored advanced feature engineering strategies, including data curation [12], weak supervision [16], and visualization-driven ideation [20], further enhancing interpretability and predictive power. Artificial Intelligence (AI) refers to the simulation of human intelligence in computer systems. A key advancement in this field is GenAI, which mimics human creativity and problem solving by learning patterns from existing data [37]. As shown in Table 1, compared to traditional feature engineering techniques, which often rely on domain-specific heuristics or handcrafted rules, GenAI provides a more adaptive approach by leveraging deep learning to dynamically extract meaningful features from unstructured data. While rule-based text mining and sentiment analysis require predefined keywords and lexicons, generative AI can identify implicit signals, sentiment nuances, and context-aware patterns that may be overlooked by static methods [38]. GenAI uses deep learning techniques to generate human-like content in various domains, including text, images, and structured data. Its ability to produce realistic and innovative outputs has made it valuable in multiple industries. GenAI has revolutionized various tasks by providing models like Generative Adversarial Networks (GANs) [39] and Variational Autoencoders (VAEs) [40]. These models are particularly useful for feature engineering, especially when dealing with unstructured data [41], anomaly detection [42], data augmentation [43], synthetic data generation [43; 44], imbalanced data [45; 46], and data imputation [41; 47].