Customer loyalty in the public transportation context

Abstract

Public transportation agencies, much like other service industries, have a constant churn of their customer base. New customers are entering and current customers are defecting every day. Traditionally, efforts to increase this customer base have focused on attracting more first-time users. However, preventing the loss of customers to competitive modes, such as the auto, has many added benefits that are not often realized. Loyal customers provide recommendations to others, increase and diversify their use of the service, and do not require the acquisition costs associated with new customers. This study aims to develop a strategy to identify the key drivers of customer loyalty to public transportation agencies, using the Chicago Transit Authority (CTA) as a case study. Once these influencing factors have been identified for the general population, loyalty differences between key market segments can be tested and analyzed. Based on these results, specific areas of service provision can be targeted for improvement and marketing campaigns can be developed so that customer segments can be targeted based on which areas are most important to them. Factor analysis and structural equation modeling were used to create a customer loyalty model for the CTA. Factors identified as contributing to a rider's loyalty were problem experience, perceptions of service quality', service value, perceptions of CTA, and customer satisfaction.

(cont.) The results for the general population showed that the average customer bases their decision to continue to use the service in the future fairly evenly on perceptions of service quality, service value, and customer satisfaction with the remaining two factors playing only indirect roles. The most important factor for a customer to recommend the service to others is their perceptions Qf service quality. The model results were then applied to key market segmentations (captive vs. choice riders, riders with low vs. high accessibility to transit, and bus riders vs. rail riders) using ANOVA, MIMIC, and multiple group analysis. It was found that captive riders are highly sensitive to problem experience; they report experiencing more problems and those problems more strongly influence the rest of the loyalty model. Riders with high accessibility generally rate all model factors higher than those with low accessibility and are, in turn, more loyal. Finally, bus riders' loyalty is more highly affected by their perceptions of service quality which could stem from the unpredictability of bus service resulting from exogenous factors. By developing a more thorough understanding of what keeps their customers coming back, public transportation agencies can more effectively use their limited resources by growing a base of loyal customers, and in turn, increasing their revenues.