# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 2 Background
### Churn-Related Analysis in Various Domains  
This section reviews customer churn studies across different industries, focusing on their feature engineering approaches. Churn is commonly defined as inactivity for a long time [55], though the definition varies across businesses due to market competition and flexible service contracts. As an example, in industries such as Internet services (e.g., online banking, education, and entertainment) churn is frequent due to low switching costs and minimal investment requirements [56; 57; 58]. Most churn prediction models use log data, which record user interactions within a system ([[https://www.sumologic.com/glossary/log-file/](https://www.sumologic.com/glossary/log-file/)]([https://www.sumologic.com/glossary/log-file/](https://www.sumologic.com/glossary/log-file/)) (accessed on 13 April 2025)). Businesses employ log-based churn models to analyse transaction logs (e.g., purchases and cancellations), communication logs (e.g., emails and calls), and activity logs (e.g., login data) [3]. Various industries, including telecommunication, banking, and insurance, rely on churn analysis to improve customer retention. In social media churn prediction, [PERSON] et al. were the first to explore churn in microblogs (e.g., Twitter). Their study suggested that identifying churn-related content enables companies to offer personalized assistance to at-risk customers. Their main feature categories were 'demographic churn indicators', 'content churn indicators', and 'context churn indicators' [6]. For telecom churn analysis, [PERSON] et al. found that long-term contract customers were less likely to churn compared to those on short-term contracts. They also noted that high monthly charges increase churn likelihood. Their study used demographic data, subscription details, user billing profile, and tenure as key predictive features [59]. In the banking sector, churn is a major issue due to the availability of multiple service providers [60]. Customers who close all accounts and cease banking relationships pose financial risks to institutions [61]. Studies suggest that frequent credit card use is linked to lower churn rates, with factors such as total transaction amount; transaction frequency; \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Strengths** & **Limitations** \\ \hline Rule-based text mining & Explicit, interpretable rules & Requires manual rule creation, limited generalization [52] \\ \hline Sentiment analysis & Captures basic sentiment polarity (positive/negative) & May miss subtle or domain-specific sentiment nuances [53] \\ \hline Domain-specific lexicon & Provides structured vocabulary for churn indicators & Static and requires regular updates to remain relevant [52] \\ \hline Generative AI (proposed approach) & Extracts latent features, dynamically adapts to text, and generates new insights & Computationally expensive, potential risk of hallucination [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of generative AI vs. traditional NLP feature engineering. and revolving balance, i.e., the portion of credit card spending that goes unpaid at the end of a billing cycle, being significant predictors [62]. For insurance churn prediction, [PERSON] et al. proposed a dynamic modelling technique to assess monthly churn likelihood. Their model incorporated features such as partner status, gender, customer lifetime, age, premium amounts, discounts, and insurance type count [63]. They highlighted that discount possession and variations over time were key churn indicators. The study also suggested that external factors like competitor campaigns and final price offers could influence customer retention. While previous studies have employed sentiment analysis and text mining techniques for churn prediction [8; 64], these approaches often focus on individual feature extraction.