# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 3 Method
### Developing a Customer ChurnKB
#### 3.1.2 Feature Identification  
The previous step generates a set of churn-related features. Based on this, in the current step, various concepts and sub-concepts, as well as relationships, that may indicate a customer's propensity to churn are identified. In developing ChurnKB, two primary concepts that suggest customers' likelihood of leaving a company are identified as their feelings and the resulting _behaviours_. Among these, _dissatisfaction_ is one of the most significant emotions contributing to churn. Factors such as _poor customer service_, _low product or service quality_, _ineffective communication_, and _lack of value for money_ are commonly associated with dissatisfaction [1]. However, this research requires more detailed emotional and behavioural features related to these factors, which are not comprehensively covered in the existing literature. We use GenAI to extract granular churn-related features. Unlike rule-based NLP, it detects hidden patterns in customer interactions. It adapts to evolving language, making it valuable for churn prediction. GenAI also generates new insights beyond heuristics, improving the understanding of customer dissatisfaction and churn. Figure 1: The process of constructing the Churn Knowledge Base (ChurnKB). (**A**) The pipeline for ChurnKB development, consisting of seven steps: literature review, feature identification, taxonomy development, validation, and API integration for the linking of churn-related phrases and calculation of scores. Feedback loops ensure refinement through expert validation. (**B**) A hierarchical snapshot of ChurnKB, illustrating relationships between customer feelings, behaviours, and churn-related concepts across four levels, from broad concepts to granular sub-instances. By interacting with a generative model, we retrieve detailed features to be incorporated into ChurnKB. For example, following query _\"In addition to dissatisfaction, what are the primary emotions that might lead customers to leave a company or business?\"_ produces the following outcome:  
* Frustration: Frustration arises when customers encounter obstacles, delays, or challenges in their interactions with a company. This emotion may be triggered by poor customer service or unresolved issues. * Disconnection: Disconnection occurs when customers no longer feel emotionally or personally connected to a brand. This feeling may result from shifts in company values or inconsistent communication. As another example, the query _\"What are the main probable behaviours of customers with a tendency to churn, derived from those feelings?\"_ produces the following outcome:  
* Reduced Usage: Customers who are dissatisfied or frustrated or feel disconnected from a company are likely to reduce their usage of the company's products or services. * Seeking Alternatives: Dissatisfied, frustrated, and disconnected customers are prone to seek alternatives actively. They might research and explore other companies or businesses offering similar products or services. Continuing the question-answering process with GenAI yields a comprehensive set of features related to customer churn. By integrating insights from customer journey studies with GenAI interactions, we can identify that disconnection might be caused by factors such as _no emotional connection_ and a _lack of interest in the product/service._ Similarly, frustration could arise from _difficulty in using the product/service_, a _complicated processes_, or _frequent disruptions or downtime_. Additionally, behaviours like _cancelling a contract_ might result from an _intention to churn_. These feelings and emotions can be reflected in or predicted by the content of customers' interactions and communications with the company. Such communications might reveal specific emotions like _anger, disgust_, and _sadness_. They might also indicate _intent to churn_, the _customer's personality type_, or _negative feedback_.