Abstract
Securing current customers is extremely necessary than earning new customers in a market that is expanding. To trace customer churn, a reliable churn prediction paradigm is required. Customer churn is the process through which people switch from one firm to another or break off contact with the company. This decision is driven by a variety of influences. It is critical for companies to acknowledge each one so that they can encourage customers to stay over. This is accomplished by regularly conducting surveys regarding customer satisfaction and analyzing the responses. Applying appropriate modelling approaches is a vital component of predicting customer churn. Predominantly, this study evaluates several machine learning models and also an incorporated model that aids in predicting customer churn where the data collected from Bengaluru regions in India about online food delivery is prioritized. In order to make better predictions using machine learning, a variety of general classifiers and ensemble classifiers are used and their degree of functionality are assessed by determining their accuracy and area under the ROC curve. According to the AUC scores obtained for the individual classifiers, the Naïve Bayes and random forest classifiers rank first with the same AUC score of 0.952. After dealing with this case, the results show that the random forest classifier outperforms all other models used.
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References
Raeisi S, Sajedi H (2020) E-commerce customer churn prediction by gradient boosted trees. In: 2020 10th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 55–59
Lalwani P, Mishra MK, Chadha JS, Sethi P (2022) Customer churn prediction system: a machine learning approach. Computing 104(2):271–294
Abbasimehr H, Setak M, Tarokh MJ (2014) A comparative assessment of the performance of ensemble learning in customer churn prediction. Int Arab J Inf Technol 11(6):599–606
Sudharsan R, Ganesh EN (2022) A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy. Connect Sci 34(1):1855–1876
Fathian M, Hoseinpoor Y, Minaei-Bidgoli B (2016) Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods. Kybernetes 45(5):732–743
Sharma T, Gupta P, Nigam V, Goel M (2020) Customer churn prediction in telecommunications using gradient boosted trees. In: Khanna A, Gupta D, Bhattacharyya S, Snasel V, Platos J, Hassanien A (eds) International conference on innovative computing and communications. Advances in intelligent systems and computing, vol 1059. Springer, Singapore, pp 235–246
Dhini A, Fauzan M (2021) Predicting customer churn using ensemble learning: case study of a fixed broadband company. Int J Technol 12(5):1030–1037
Jagadeesan AP (2020) Bank customer retention prediction and customer ranking based on deep neural networks. Int J Sci Dev Res (IJSDR) 5(9):444–449
Momin S, Bohra T, Raut P (2020) Prediction of customer churn using machine learning. In: EAI international conference on big data innovation for sustainable cognitive computing. EAI/Springer innovations in communication and computing. Springer, Cham, pp 203–212
Fujo SW, Subramanian S, Khder MA (2022) Customer churn prediction in telecommunication industry using deep learning. Inf Sci Lett 11(1):185–198
Domingos E, Ojeme B, Daramola O (2021) Experimental analysis of hyperparameters for deep learning-based churn prediction in the banking sector. Computation 9(34):1–19
Sree GMA, Ashika S, Karthi S, Sathesh V, Shankar M, Pamina J (2019) Churn prediction in telecom using classification algorithms. Int J Sci Res Eng Dev 2(1):1–16
Ahmad AK, Jafar A, Aljoumaa K (2019) Customer churn prediction in telecom using machine learning in big data platform. J Big Data 6(28):1–24
Dias J, Godinho P, Torres P (2020) Machine learning for customer churn prediction in retail banking. In: International conference on computational science and its applications. Springer, Cham, pp 576–589
Shirazi F, Mohammadi M (2019) A big data analytics model for customer churn prediction in the retiree segment. Int J Inf Manage 48:238–253
Khodabandehlou S, Rahman MZ (2017) Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. J Syst Inf Technol 19(1/2):65–93
Kumar AS, Chandrakala D (2016) A survey on customer churn prediction using machine learning techniques. Int J Comput Appl 154(10):13–16
Al-Najjar D, Al-Rousan N, Al-Najjar H (2022) Machine learning to develop credit card customer churn prediction. J Theor Appl Electron Commer Res 17:1529–1542
Xu T, Ma Y, Kim K (2021) Telecom churn prediction system based on ensemble learning using feature grouping. Appl Sci 11(4742):1–12
Tavassoli S, Koosha H (2022) Hybrid ensemble learning approaches to customer churn prediction. Kybernetes 51(3):1062–1088
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Gerald Manju, J., Dharini, A., Kiruthika, B., Malini, A. (2024). Online Food Delivery Customer Churn Prediction: A Quantitative Analysis on the Performance of Machine Learning Classifiers. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 785. Springer, Singapore. https://doi.org/10.1007/978-981-99-6544-1_8
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