Improving customer retention in taxi industry using travel data analytics: A churn prediction study

A.L.D. Loureiro, Miguéis, V.L., à lvaro Costa and Michel Ferreira

Journal of Retailing and Consumer Services, 2025, vol. 85, issue C

Abstract: The retention of public transport users is widely acknowledged as a paramount challenge in the path towards the establishment of more sustainable cities and societies. In this setting, in which no contractual relationship with customers exists, an early and accurate prediction of whether a customer will remain with the company or leave, assumes great significance for businesses to develop effective retention strategies. This work focuses on this topic by identifying potential churners based on their past travel behavior. To achieve this, we developed a set of classification models using various machine learning techniques. These models were then employed as base learners within a stacking ensemble. All classifiers were developed with a profit-driven approach, optimizing for expected maximum profit. Finally, we calculated Shapley Additive Explanation values to enhance the interpretability of the proposed classifiers. The performance of the predictive models was evaluated using the data of taxi services recorded in a Portuguese city for 52 months. A broad range of predictors is proposed, including recency and frequency measures of taxi usage as well as others related to customers' satisfaction level. The predictive power of the models was also assessed for specific proportions of higher risk customers. All models have shown the capability to identify churners accurately. This study innovates in evaluating the one-to-one service provider company-customer relationship in the context of taxi industry. Retention actions to promote customers loyalty and enhance retention are also suggested.

Keywords: Traffic retention; Churn prediction; Non-contractual churn; LightGBM; Random forest; Artificial neural networks; Taxi industry (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:85:y:2025:i:c:s0969698925000670

DOI: 10.1016/j.jretconser.2025.104288

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