Using Neural Networks in Modeling Customer Loyalty in Passenger Cars Maintenance and Repair Services

Abstract

Background: In the study, we researched if it is possible to identify the segments of customers of auto repair shops with the use of neural networks? The study attempts to contribute to literature by way of classifying customers of the auto repair shops, accordingly to their loyalty, with the use of advanced statistical methods. Methods: The paper covers classification trees, neural networks, and cluster analyses that were applied in order to classify customers according to their loyalty. Results: It has been proved that three groups of factors influence loyalty: (1) social and demographic factors concerning customer characteristics; (2) technical factors concerning the vehicle and the manner of its use; and (3) factors defining the evaluation of the service quality provided by the auto repair shops. Four segments of customers which vary in terms of formal characteristics and assessment of the quality of services were obtained. Conclusions: (1) identification of factors shaping customers’ loyalty; (2) possibility of checking which changes of the quality of services may influence the loyalty of the customers; and (3) identification of the segments of the customers in the case of which the marketing strategies applied to increase the loyalty, must be treated with the utmost priority.

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