This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

This study applies optimization-based algorithm to develop combination classification methods. We propose a genetic algorithm-based combination classification method of multiple decision trees to improve predictive accuracy, optimize classification rules, and interpret classification results. The basic algorithm for decision tree has been constructed in a top-down recursive divide-and-conquer manner. Based on different split measures (attribute selection measures), different decision tree algorithms can be produced, and then multiple decision trees can be formed. We proposed the parallel combination model of multiple decision trees. On top of the combination model, multiple decision trees are parallel combined. Each decision tree produces its own classification rules according to training samples from which one can present the classification result using probability distribution of target class label. At the bottom, the classification result of each decision tree serves as the input for combination algorithm in producing classification result and rules for the combination model. Combination algorithm adopts weighted summation of the outputs of probability measurement levels from individual decision trees, while genetic algorithm optimizes connection weight matrix. Finally the target class label with the largest probability output value is selected as the decision result for combination classification methods. The proposed method is applied to the issues of customer credit rating assessment and customer response behaviour pattern recognition in CRM. From the simulation results it is concluded that the proposed method has higher predictive accuracy than single decision tree. Moreover, it retains good interpretability and optimizes classification rules.

Recently, service quality must reflect several demands of customers who show rapid and various changes so as to be compared with the past. So, objective and rapid methods for them are necessary more. For them, first of all, service company must calculate their standard of service quality accurately by measuring service quality exactly. To measure service quality accurately, this researcher collected and analyzed data by survey for customers who are customers of logistics services, grasped potential satisfaction standard(P) by 5 point Likert scale and one survey for accurate classification of quality attributes through weighted customer satisfaction coefficient changing quality attributes by developing the study on Kano model and Timko's customer satisfaction coefficient, and suggested Potential Customer Satisfaction Improvement index(PCSI) for examining the improvement of customer satisfaction so as to utilize them as an index of differentiated and concrete measurement of service quality.

The principal objective of this study was to categorize service attributes on the basis of the asymmetric and non-linear relationship existing between service attributes and customer satisfaction. Researchers generally assume that service attribute performances and customer satisfaction are both symmetrical and linear. That is to say, improvements in attribute performance will inevitably result in increased customer satisfaction. However, this is not always the case. Certain attributes have been shown not to create satisfaction even when improved, and others do not create dissatisfaction even when their performance ratings become negative. Understanding this relationship is crucial not only to researchers, but also to service managers. Service managers can arrange their priorities with regard to which attributes must be improved or promoted first, in an environment of limited technical, financial, and human resources. Many studies into this asymmetric and non-linear relationship have recently been conducted, beginning with Herzberg's motivation-hygiene theory (1976) and the disconfirmation theory, which was eventually developed into Kano's model (1984). This study attempted to determine the impact level of service attributes on incidents of satisfaction or dissatisfaction. It used 30 service attributes generated by Park (2008) in the CIT research into family restaurants. The data were collected from 600 participants, 300 incidences of satisfaction and 300 incidents of dissatisfaction, via an online survey. The t-test was used to confirm the difference between the satisfaction group's and dissatisfaction group's attributes. 11 attributes were found to be significant at a level of p>0.05. This indicates that the 11 attributes exerted different impacts on satisfaction and dissatisfaction, which confirmed the asymmetric and non-linear relationship. 14 attributes were categorized into the core service, 1 attribute into the quality service, 7 attributes into the basic service, and 8 attributes into the neutral service. Strategic customer service management was recommended for the 'A' family restaurant as an example, on the basis of the asymmetric and non-linear relationship and the characteristics of the four service factors.