Seymen, Ömer FarukDoğan, OnurHızıroğlu, Abdülkadir2022-02-152022-02-15202197830307368802194-5357https://doi.org/10.1007/978-3-030-73689-7_50https://hdl.handle.net/20.500.14034/41512th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 -- 15 December 2020 through 18 December 2020 -- -- 258059Churn studies have been used for years to achieve profitability and to establish a sustainable customer-company relationship. Deep learning is one of the contemporary methods used in churn analysis due to its ability to process huge amounts of customer data. In this study, a deep learning model is proposed to predict whether customers in the retail industry will churn in the future. The model was compared with logistic regression and artificial neural network models, which are also frequently used in the churn prediction studies. The results of the models were compared with accuracy classification tools, which are precision, recall and AUC. The results showed that the deep learning model achieved better classification and prediction success than other compared models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessCustomer churn modellingDeep learningPredictive analyticsRFMForecastingLearning systemsLogistic regressionNeural networksPattern recognitionSalesSoft computingArtificial neural network modelsChurn analysisChurn predictionsClassification toolCustomer churn predictionCustomer dataLearning modelsRetail industryDeep learningConference Object10.1007/978-3-030-73689-7_501383 AISC5205292-s2.0-85105878020N/A