Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Seymen, Omer Faruk; Ölmez, Emre; Doğan, Onur; Er, Orhan; HIZIROĞLU, Kadir

doi:10.35378/gujs.992738

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…In 2022, Seymen and Ölmez et al [19] presents a model that uses Ordinary Artificial Neural Network (ANN) and Convolution Neural Network (CNN), to predict whether customers in the retail industry will leave in the future or not. The data was used from the supermarket chain, including 27-month retail scanner data for 5747 customers.…”

Section: Related Workmentioning

confidence: 99%

Application of Classification Methods in Forecasting broadband internet subscribers leaving the network on Broadband Internet Subscribers Leaving the Network

2023

IJML

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The cancellation of subscribers is always a matter of special concern for service providers in general and VNPT An Giang in particular because customers are the ones who bring in revenue and bring value to service providers. To achieve growth and maintain profitability, service providers must find ways to develop new subscribers while also maintaining a stable number of existing subscribers. Therefore, it is very important to research solutions to identify and forecast subscribers who are likely to withdraw from the data network in order to have a customer care strategy to reduce leaving the network. In this paper, we present an approach to exploiting broadband internet subscriber data from the available data warehouse at VNPT An Giang, building a forecasting model for broadband internet subscribers leaving the network before 1 month and 3 months. First, collect data about the 12-month usage history of broadband internet subscribers including 102,920 active subscribers and 24,376 disconnected subscribers with 12 related attributes per subscriber; then the data is preprocessed to remove null data, negative numeric data, and duplicated data; In order to reduce the number of input attributes, select the attributes that are considered to be the most useful for the model, we use the SelectKBest method of the Sklearn library to evaluate and select 8 attributes with high scores. Based on historical data of 6 months/12 months, divide the data into 12 different data sets (in which 6 data sets are for building and evaluating the model to predict that subscribers leave the network before 1 month; 6 datasets for building and evaluating predictive models of subscribers leaving the network before 3 months) (see Table 6 and Table 7). To select the set of attributes and the most suitable model for the forecasting problem of broadband internet subscribers leaving the network, we propose to use 4 machine learning methods including Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Long short-term memory (LSTM). To evaluate the performance of machine learning methods on predictability, we use the Area Under the Curve measure (AUC).

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Section: Related Workmentioning

confidence: 99%

Application of Classification Methods in Forecasting broadband internet subscribers leaving the network on Broadband Internet Subscribers Leaving the Network

2023

IJML

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“…Customer churn analysis (CCA), a sub eld of customer analytics [25], aims to predict customer churn based on historical data and behavior patterns and to convict these customers by providing customized offerings [8].…”

Section: Customer Churn Analysismentioning

confidence: 99%

A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Başarslan,

ÜNAL,

KAYAALP

2023

Preprint

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Customer churn is an important issue in increasing both the long and short-term revenues of a company. If companies identify customers’ churn behavior, they can prevent customer loss, ensure customer loyalty, and in turn gain better financial returns. The telecommunication sector is a customer-oriented sector that requires customer retention to survive in the market and, in this sector customer loss is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customers’ churn behavior. In this study, a customer churn analysis was conducted with publicly shared Telco telecom data. Predictive models were built with machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Voting), and deep learning (LSTM) methods. Besides, a 3-layered LSTM model was proposed. Accuracy, F1, precision, and recall rates were used to evaluate the models. As a result, the novel 3-layered LSTM model achieved 91% accuracy, 87% precision, 84% recall, and 89% F1 values. The proposed model is competitive with the literature.

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“…In the realm of artificial intelligence (AI) and machine learning (ML), a plethora of methodologies and applications have emerged, showcasing the immense potential and versatility of these technologies. Methodologically, AI and ML encompass a wide spectrum of techniques, including feature selection and stability analysis [7], hybrid control systems involving artificial neural networks (ANNs) and fuzzy PI control [8], and comparative assessments of predictive algorithms, such as ordinary ANNs and convolutional neural networks (CNNs) for customer churn prediction [9]. These methods collectively form the foundation for addressing complex challenges across diverse domains.…”

Section: Introductionmentioning

confidence: 99%

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Öz,

Özarpa

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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This study uses neural networks to explore the intricate longitudinal progression of decommissioned geostationary satellites. The goal is to model and predict satellites' longitudinal dynamics across time dimensions. Historical satellite longitude data undergoes thorough preprocessing to train time series neural networks in both single-input and 3-input configurations for all six decommissioned satellites, yielding comprehensive longitudinal behavior insights. Results reveal impressive outcomes: average Mean Squared Error (MSE) between predicted and measured longitudes is 1.55x10-3, with regression close to unity. This convergence implies a strong alignment between the neural network methodology employed and the intricate problem domain. These results accentuate the suitability and effectiveness of the chosen neural network approach in addressing the challenges posed by decommissioned geostationary satellite trajectory modeling. The study's implications span various fields. Insight into long-term orbital shifts aids in understanding satellite behaviors, enhancing trajectory predictions and decision-making in satellite management and space technology advancement. Additionally the research emphasizes the importance of accurate predictions about satellite behavior after decommissioning. This contributes to better mission planning, resource optimization, and more efficient strategies for dealing with space debris.

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Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Cited by 11 publications

References 31 publications

Application of Classification Methods in Forecasting broadband internet subscribers leaving the network on Broadband Internet Subscribers Leaving the Network

Application of Classification Methods in Forecasting broadband internet subscribers leaving the network on Broadband Internet Subscribers Leaving the Network

A novel robust LSTM model on Churn and comparative analysis versus deep learning, machine learning and ensemble learning: Telecommunications Case

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

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