Predicting Fitness Centre Dropout

Pedro Sobreiro, Pedro Guedes-Carvalho, Abel Santos, Paulo Pinheiro and Celina Gonçalves
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Pedro Sobreiro: Sport Sciences School of Rio Maior (ESDRM), Polytechnic Institute of Santarém, 2001-904 Santarém, Portugal
Pedro Guedes-Carvalho: Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), 5001-801 Vila Real, Portugal
Abel Santos: Sport Sciences School of Rio Maior (ESDRM), Polytechnic Institute of Santarém, 2001-904 Santarém, Portugal
Paulo Pinheiro: Departamento de Ciências e Tecnologia, Universidade Aberta, 1269-001 Lisbon, Portugal
Celina Gonçalves: Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), 5001-801 Vila Real, Portugal

IJERPH, 2021, vol. 18, issue 19, 1-11

Abstract: The phenomenon of dropout is often found among customers of sports services. In this study we intend to evaluate the performance of machine learning algorithms in predicting dropout using available data about their historic use of facilities. The data relating to a sample of 5209 members was taken from a Portuguese fitness centre and included the variables registration data, payments and frequency, age, sex, non-attendance days, amount billed, average weekly visits, total number of visits, visits hired per week, number of registration renewals, number of members referrals, total monthly registrations, and total member enrolment time, which may be indicative of members’ commitment. Whilst the Gradient Boosting Classifier had the best performance in predicting dropout (sensitivity = 0.986), the Random Forest Classifier was the best at predicting non-dropout (specificity = 0.790); the overall performance of the Gradient Boosting Classifier was superior to the Random Forest Classifier (accuracy 0.955 against 0.920). The most relevant variables predicting dropout were “non-attendance days”, “total length of stay”, and “total amount billed”. The use of decision trees provides information that can be readily acted upon to identify member profiles of those at risk of dropout, giving also guidelines for measures and policies to reduce it.

Keywords: sports management; sports services; fitness; machine learning algorithm; dropout prediction; gradient boost classifier (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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