Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry

Dingli, Alexiei; Marmara, Vincent; Fournier, Nicole Sant

doi:10.18178/ijmlc.2017.7.5.634

Cited by 32 publications

(29 citation statements)

References 9 publications

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“…For example, Paolanti et al [74] employed deep convolutional neural network (DCNN) to develop a mobile robot, so-called ROCKy, to analyze real-time store heat maps of retail store shelves for detection of the shelf-out-of-stock (SOOS) and promotional activities during working hours. Dingli, Marmara, and Fournier [75] investigated solutions to identify the patterns and features among transactional data to predict customer churn within the retail industry. To do so, they compared the performance of CNN and restricted Boltzmann machine (RBM), realizing the RBM outperformed in customer churn prediction.…”

Section: Marketingmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

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Section: Marketingmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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“…The activation function is used in the Neural Network to achieve non-linear behavior otherwise it acts like a simple linear regression model or the linear model. The role and softmax are used as activation function for the hidden layers, which is optimal [20] where the problem of vanishing gradient are avoided. The sigmoid activation function performs best in the output layer, hence the same activation function is applied to the output layer.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In [12] Customer attrition related to the retail sector are analyzed using deep learning based model. The profound learning models are shaped utilizing a limited Restricted Boltzmann machine(RBM) and convolution neural network(CNN).…”

Section: Related Workmentioning

confidence: 99%

Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector

Hegde¹,

Mundada²

2019

IJISA

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In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naï ve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.

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“…For example, Paolanti et al [68] employ deep convolutional neural network (DCNN) to develop a mobile robot, so called ROCKy, to analyze real-time store heat maps of retail store shelves for detection of shelf out of stock (SOOS) and promotional activities, during working hours. Dingli, Marmara, and Fournier [69] were looking for solutions to find the patterns and features among transactional data to predict customer churn within the retail industry. To do so, they compare the performance of CNN and restricted Boltzmann machine (RBM), and they found out the RBM attained outperformed in customer churn prediction.…”

Section: Other Algorithmsmentioning

confidence: 99%

“…Largescale deep learning (LSDL) and MACN are DL algorithms that are used to analyze financial time series data to predict stock price [49,55]. Ultimately, it is found that DCNN and RBM applied for analyzing primary data for respectively promotional activities [68] and customer behavior forecasting [69] (see Table 8). Hybrid deep neural networks are architectures that apply generative and discriminative components at the same time.…”

Section: Cryptocurrencymentioning

confidence: 99%

Data Science in Economics

Nosratabadi¹,

Mosavi²,

Duan³

et al. 2020

Preprint

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This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The novel data science methods and applications are investigated in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide range of economics research, from stock market, marketing, E-commerce, to corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on the advancement of hybrid models. On the other hand, based on the accuracy metric it is also reported that the hybrid models outperform other learning algorithms. It is further expected that the trends would go toward the advancements of sophisticated hybrid deep learning models.

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Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry

Cited by 32 publications

References 9 publications

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector

Data Science in Economics

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