Generative AI-enabled Knowledge Base Fine-tuning: Enhancing Feature Engineering for Customer Churn

Abstract

Customers are the most critical component in a business’s success regardless of the industry or product. Companies make significant efforts to acquire and, more importantly, retain their existing customers. Customer churn is a significant challenge for businesses, leading to financial losses. To address this challenge, understanding customer’s cognitive status, behaviors, and early signs of churn is crucial. However, predictive and ML-based analysis, being fed with proper features that are indicative of a customer’s cognitive status or behavior, is extremely helpful in addressing this challenge. Having practical ML-based analysis relies on a well-developed feature engineering process. Previous churn analytical studies mainly applied feature engineering approaches that leveraged demographic, product usage, and revenue features alone, and there is a lack of research on leveraging the information-rich content from interactions between customers and companies. Considering the effectiveness of applying domain knowledge and human expertise in feature engineering, and motivated by our previous work, we propose a Customer Churn-related Knowledge Base (ChurnKB) to enhance the feature engineering process. In the ChurnKB, we leverage textual data mining techniques for extracting churn-related features from texts created by customers, e.g., emails or chat logs with company agents, reviews on the company’s website, and feedback on social media. We use Generative AI (GAI) to enhance and enrich the structure of the ChurnKB regarding features related to customer churn-related cognitive status, feelings, and behaviors. We also leveraged feedback loops and crowdsourcing to enhance and approve the validity of the proposed ChurnKB and apply it to develop a classifier for customer churn problems.