# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 2 Related Works
### Customer Retention Studies  
Customer retention is a critical aspect of the automotive industry's business model, with AI technologies playing a pivotal role in enhancing customer engagement and loyalty. While it is commonly assumed that retaining a customer is significantly more cost-effective than acquiring a new one, recent work [1] emphasizes that this assumption must be evaluated through the lens of marginal versus average costs. From this perspective, firms are advised to align retention and acquisition spending with expected customer lifetime value, reinforcing the importance of data-driven strategies in customer relationship management. In this context, AI-based models not only support churn prediction and personalized marketing but also enable dynamic resource allocation for customer retention efforts. Recent industry research highlights the growing organizational investment in CRM systems as part of broader customer retention strategies. According to a large-scale CRM implementation report [2], companies spend an average of 11 weeks selecting a CRM, with nearly 48% of employees using the system once deployed. The average cost per user is estimated at USD 7500 over a five-year period. Most firms cited \"supporting business growth\" as the leading motivation for CRM adoption, suggesting that CRM platforms are increasingly viewed as strategic assets rather than mere data repositories. Although AI applications in customer service are widely adopted to enhance efficiency and customer retention, some implementations may carry hidden risks that undermine their intended value. One recent study [3] proposes a framework to diagnose the value-destruction potential of AI and machine learning systems within business processes. The study highlights how flaws in input data, model logic, or system outputs can compromise the effectiveness of AI-based tools such as customer service chatbots. These risks can lead to diminished service quality, customer dissatisfaction, and ultimately reduced loyalty--directly contradicting the strategic goals of retention-focused AI deployments. A customer churn prediction model utilizing unstructured data from telephone communications is proposed in [4]. This study explores customer loyalty through call center data, employing various text mining techniques to analyze clients' call logs. The churn models developed can predict churn risks with high accuracy and provide meaningful insights. In the broader telecommunications domain, related models offer methodological insights that can inspire solutions in the automotive context. In [5], experiments are conducted to enhance the predictive ability of churn models in the context of a personal phone service system. The findings show that the developed churn model performs well, even with an imbalanced class distribution and limited customer data. In [6], the authors present a comparative study of the most widely used machine learning methods for addressing the customer churn problem in the telecommunications industry. Among various classification techniques, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Tree (DT), Naive Bayes (NB), and logistic regression (LR), the SVM-POLY model using AdaBoost is identified as the most effective, achieving an accuracy of nearly 97% and an F1-score exceeding 84%. Such performance benchmarks inform model selection processes in automotive churn prediction problems, especially in environments with high dimensionality and class imbalance. Similarly, ref. [7] investigates the combination of two neural network methods--backpropagation ANN and Self-Organizing Maps (SOMs)--for predicting customer churn. These hybrid models outperform single neural network models in terms of prediction accuracy and the reduction in Type I and Type II errors across three test sets.