Research on a Customer Churn Combination Prediction Model Based on Decision Tree and Neural Network

Hu, Xin; Yang, Yibo; Chen, Lanhua; Zhu, Shufang

doi:10.1109/icccbda49378.2020.9095611

Cited by 35 publications

(16 citation statements)

References 2 publications

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“…[16]. Xin Hu, Lanhua Chen, Yanfei Yang, and Siru Zu developed a customer churn prediction model based on Decision Trees and Neural Networks last year, and their model had a 98.87 percent accuracy [17].…”

Section: Related Workmentioning

confidence: 99%

Customer Churn Prediction on E-Commerce Data using Stacking Classifier

Awasthi¹

2022

Preprint

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Section: Related Workmentioning

confidence: 99%

Customer Churn Prediction on E-Commerce Data using Stacking Classifier

Awasthi¹

2022

Preprint

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“…Although the decision tree technique has several alternative approaches, the most well-known C5 method splits the sample using the feature with the maximum information gain. It then repeats this process until the subset can no longer be divided [17]. An example decision tree model is shown in Figure 2.…”

Section: Figure 2 Decision Tree Modelmentioning

confidence: 99%

“…Neural networks are a method of explaining cognitive, decision-making, and other intelligent control behaviors by using the way the human brain operates as a kind of data processing and analysis [17]. A classic neural network consists of three layers: an input layer, a hidden layer, and an output layer, all connected by neurons, as seen in Figure 5.…”

Section: Neural Networkmentioning

confidence: 99%

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Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry

Çallı

KASIM>

2022

Academic Platform Journal of Engineering and Smart Systems

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Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP (enterprise resource planning) company with a software as a service (SaaS) business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

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“…(2) In terms of comprehensibility, it tends to be relatively better than "blackbox" models like neural network, which means it can interpret data structure more clearly and help readers understand the information involved. ese undoubtedly bring convenience to decision making in medical treatment [8][9][10], e-commerce, [11][12][13] and so on.…”

Section: Introductionmentioning

confidence: 99%

Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting

Zhao

Nie

2020

Complexity

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Renewable energy has become popular compared with traditional energy like coal. The relative demand for renewable energy compared to traditional energy is an important index to determine the energy supply structure. Forecasting the relative demand index has become quite essential. Data mining methods like decision trees are quite effective in such time series forecasting, but theory behind them is rarely discussed in research. In this paper, some theories are explored about decision trees including the behavior of bias, variance, and squared prediction error using trees and the prediction interval analysis. After that, real UK grid data are used in interval forecasting application. In the renewable energy ratio forecasting application, the ratio of renewable energy supply over that of traditional energy can be dynamically forecasted with an interval coverage accuracy higher than 80% and a small width around 22, which is similar to its standard deviation.

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Research on a Customer Churn Combination Prediction Model Based on Decision Tree and Neural Network

Cited by 35 publications

References 2 publications

Customer Churn Prediction on E-Commerce Data using Stacking Classifier

Customer Churn Prediction on E-Commerce Data using Stacking Classifier

Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry

Prediction Error and Forecasting Interval Analysis of Decision Trees with an Application in Renewable Energy Supply Forecasting

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