The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

P2P网络中Churn问题研究

Churn问题是P2P网络面临的基本问题之一.通过系统地归纳现有文献,从Churn问题产生的机理出发,总结出解决Churn问题的主要步骤,依次是准确度量Churn,分析Churn对P2P网络性能的影响,给出应对Churn的具体策略.以此为主线对Churn问题的研究进展进行综述,全面、深入、系统地总结了每个步骤中涉及的关键问题以及解决这些问题的具体方法与最新成果.讨论了存在的问题并指出未来可能的研究方向.

Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).

Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

Customer retention is one of the challenging issues in different business sectors,and variousfirms utilize customer churn prediction(CCP)process to retain existing customers.Because of the direct impact on the company revenues,particularly in the telecommunication sector,firms are needed to design effective CCP models.The recent advances in machine learning(ML)and deep learning(DL)models enable researchers to introduce accurate CCP models in the telecom-munication sector.CCP can be considered as a classification problem,which aims to classify the customer into churners and non-churners.With this motivation,this article focuses on designing an arithmetic optimization algorithm(AOA)with stacked bidirectional long short-term memory(SBLSTM)model for CCP.The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry.Initially,the AOA-SBLSTM model per-forms pre-processing to transform the original data into a useful format.Besides,the SBLSTM model is employed to categorize data into churners and non-chur-ners.To improve the CCP outcomes of the SBLSTM model,an optimal hyper-parameter tuning process using AOA is developed.A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features.The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been built.This framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost customers.The improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost customers.The framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature selection.Logistic regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational awareness.The prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and F-measure.The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.

对等网络Churn问题评估模型与分析

提出一个Churn问题评估模型,以及基于该模型的2种比较模式和一个公式化描述。使用模型对3种常用对等网络分析得出以下重要结论:Chord算法在Churn环境下性能最优,影响Churn问题众多因素中最重要的是节点平均生存时间,Churn带来的两类影响不可能同时有效解决,二者存在平衡制约关系。评估模型和相关结论可为Churn环境下研究现有对等网络和设计新对等网协议提供分析平台。

移动自组网上结构化P2P网络Churn问题分析

分析研究了在三种用户行为的指数分布模型、Pareto分布模型和Weibull分布模型下,Churn问题对移动自组网上结构化P2P网络性能的影响.仿真实验研究表明,影响结构化P2P网络在移动自组网上工作性能的主要因素是节点会话时长、网络规模和节点密度、网络层路由协议以及节点移动性,并分析了这些主要因素之间的相互关系.

Experimental Study of Churning Losses in Swash Plate Axial Piston Pump

The problem of the churning loss in swash plate axial piston machines is investigated through experimental measurement and theoretical analysis. Several works surrounding churning loss in hydraulic components have been proposed in the past, but few have conducted experimental studies and accounted for both dry and wet housing conditions. In this study,a specialized experimental setup is established, which includes a transparent test pump diligently designed for performing various functions of tests. The test pump can work as a real pump without losing any actual features of pump operation. The torque loss in both the dry housing pump and wet housing pump is measured in terms of the shaft speed and its predictive model is also developed analytically. The comparisons between measured and calculated torque loss are presented, showing how speed influences torque loss in both conditions. The advantage/disadvantages of the two cases are summarized. The significance of the test setup is highlighted by verifying the proposed model, which can advance the understanding of energy losses of high speed pumps in future.

Developing a prediction model for customer churn from electronic banking services using data mining

Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention.Methods:Being based on existing information technologies which allow one to collect data from organizations’databases,data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data.In this research,the decision tree technique was applied to build a model incorporating this knowledge.Results:The results represent the characteristics of churned customers.Conclusions:Bank managers can identify churners in future using the results of decision tree.They should be provide some strategies for customers whose features are getting more likely to churner’s features.

CT-Cycloid:一个基于Cycloid的抗Churn的P2P系统

Cycloid是一种常数度结构化P2P覆盖网,它具有高扩展性、自组织、自适应开销低等优点,然而像所有其它的常数度P2P覆盖网一样,它不能很好地适应高churn环境。为解决这一问题,在一个简单而新奇的设想的启发下,基于生存期的策略和角色划分机制被应用来对Cycloid的设计进行改造,构造了一个基于Cycloid的具有高抗churn能力的结构化P2P系统——CT-Cycloid。在CT-Cycloid系统中进行路由和定位的开销是O(logS),远低于一般系统中的O(logN)(S=N/logN)。大多数节点到来和离开时仅仅需要发送一条一跳到达的信息,节点的失效会很快被检测到,网络很快得以恢复稳定。理论分析和仿真实验都证明了CT-Cycloid与Cycloid相比查询路径更短,适应高churn环境的能力更强。

Influence of Piston Number on Churning Losses in Axial Piston Pumps

Raising the rotational speed of an axial piston pump is useful for improving its power density;however,the churning losses of the piston increase significantly with increasing speed,and this reduces the performance and efficiency of the axial piston pump.Currently,there has been some research on the churning losses of pistons;however,it has rarely been analyzed from the perspective of the piston number.To improve the performance and efficiency of the axial piston pump,a computational fluid dynamics(CFD)simulation model of the churning loss was established,and the effect of piston number on the churning loss was studied in detail.The simulation analysis results revealed that the churning losses initially increased as the number of pistons increased;however,when the number of pistons increased from six to nine,the torque of the churning losses decreased because of the hydrodynamic shadowing effect.In addition,in the analysis of cavitation results,it was determined that the cavitation area of the axial piston pump was mainly concentrated around the piston,and the cavitation became increasingly severe as the speed increased.By comparing the simulation results with and without the cavitation model,it was observed that the cavitation phenomenon is beneficial for the reduction of churning losses.In this study,a piston churning loss test rig that can eliminate other friction losses was established to verify the accuracy of the simulation results.A comparative analysis indicated that the simulation results were consistent with the actual situation.In addition,this study also conducted a simulation study on seven and nine piston pumps with the same displacement.The simulation results revealed that churning losses of the seven pistons were generally greater than those of the nine pistons under the same displacement.In addition,regarding the same piston number and displacement,reducing the pitch circle radius of piston bores is effective in reducing the churning loss.This research analyzes the effect of piston number on the churning loss,which has certain guiding significance for the structural design and model selection of axial piston pumps.

Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms

Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Na?ve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models’ performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.

Key-Attributes-Based Ensemble Classifier for Customer Churn Prediction

Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example,the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding.Furthermore, the experimental results based on the real data collected from China Mobile show that the keyattributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.

Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

Presently,customer retention is essential for reducing customer churn in telecommunication industry.Customer churn prediction(CCP)is important to predict the possibility of customer retention in the quality of services.Since risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer behavior.Besides,deep learning(DL)models help in prediction of the customer behavior based characteristic data.Since the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business people.In this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application sector.In addition,the O-DCCAEP method purposes for determining the churning nature of the customers.The O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter optimization.Additionally,the DCCAE model is employed to classify the churners or non-churner.Furthermore,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

Telecom industry relies on churn prediction models to retain their customers.These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers.Retention not only contributes to the profit of an organization,but it is also important for upholding a position in the competitive market.In the past,numerous churn prediction models have been proposed,but the current models have a number of flaws that prevent them from being used in real-world largescale telecom datasets.These schemes,fail to incorporate frequently changing requirements.Data sparsity,noisy data,and the imbalanced nature of the dataset are the other main challenges for an accurate prediction.In this paper,we propose a hybrid model,name as“A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning(HCPRs)”that used Synthetic Minority Over-Sampling Technique(SMOTE)and Particle Swarm Optimization(PSO)to address the issue of imbalance class data and feature selection.Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities.Substantial experiments are performed to test and validate the model on Random Forest(RF),Linear Regression(LR),Naïve Bayes(NB)and XG-Boost.Results show that the proposed model when used with XGBoost classifier,has greater Accuracy Under Curve(AUC)of 98%as compared with other methods.

Numerical Simulation of the Churning Power Losses in the Automotive Hypoid Gear Reducer

Improving vehicle transmission efficiency and reducing vehicle fuel consumption is currently one of the main objectives in the automotive field. Reducing gear churning power losses has significant influence on the decreasing vehicle fuel consumption. Based on the two phase flow theory, a 2D two-phase model of the simplified hypoid gear is established to predict the churning losses in different conditions, the VOF method is introduced to track the volume fraction of the free surface, a standard k-ε model is also built to calculate complex turbulence. The oil distributions at the different rotational speed, the different immersion depth and the different viscosity as well as the churning losses of the hypoid gear are obtained and discussed in detail. In general, the churning power losses increase with the increase of the speed, the immersion depth and the viscosity, while the rotational speed shows the greatest influence on the churning losses. It is hoped that this investigation will be helpful in automotive industry applications.

Churn Forecast Based on Two-step Classification in Security Industry

Customer is a determinant factor that decides whether a security company will be alive. As a result, the competition for customers is more and more intense between security companies. In order to avoid profit decrease caused by churn, security companies must find those customers who have the loss risk and make measures to maintain loyal customers. Now it is the question that how to find and analyze those customers. In this paper, a two-step classification method about churn Analysis is proposed and the problem of churn in security is analyzed.

Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data

Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.

Hybrid Data Mining Models for Predicting Customer Churn

The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases;the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.