Prediction and Causality analysis of churn using deep learning

Shah, Muzaffar Bashir; Adiga, Darshan; Bhat, Shabir Ahmad; Vyeth, Viveka

doi:10.5121/csit.2019.91312

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…The dynamic behavior Similarity Forest model outperforms all traditional methods (static behavior models) and is more effective at finding churn early stages [8]. Shah et al [9], proficient an idea to generate appropriate weights for attributes that foresee whether such a client will churn. They compared churn classifications utilized in business management, advertising, information technology, communications, newspaper articles, healthcare, and thinking.…”

Section: Why Is Attrition So Critical?mentioning

confidence: 99%

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Jajam¹,

Challa²

2023

Computers, Materials &Amp; Continua

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In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers' static behavior. This paper's objective is to calculate daily churn based on dynamic variations in client behavior. Training excellent models to further identify potential churning customers helps insurance companies make decisions to retain customers while also identifying areas for improvement. Thus, it is possible to identify and analyse clients who are likely to churn, allowing for a reduction in the cost 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 using various models such as RFM (Recency, Frequency, Monetary), Multivariate Time 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 problem of daily forecasting using this description. It can be concluded that all models produced better overall outcomes with only slight variations in performance measures. The proposed CELM outperforms all other models in terms of accuracy (96.4).

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Section: Why Is Attrition So Critical?mentioning

confidence: 99%

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Jajam¹,

Challa²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The set of general features can be derived from practically all non-subscription-oriented firms' revenue and transaction information and used to forecast customer churn. For prediction, "a neural network-based Multilayer perceptron" is often used [11].…”

Section: Customer Churn Predictionmentioning

confidence: 99%

“…A likelihood of churn anticipates, as are the driving factors on banking data. Subsequently, Shah et al [11] trained a model to produce suitable weights for features that predict whether a customer would churn. They contrasted churn definitions commonly used in business administration, marketing, IT, telecommunications, newspapers, insurance, and psychology.…”

Section: Studies On Causal Inference Of Churnmentioning

confidence: 99%

Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions

Rudd

Huo

2022

Hum-Cent Intell Syst

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Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.

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“…A likelihood of churn is anticipated, as are the driving factors on banking data. Subsequently, Shah et al [11] trained a model to produce suitable weights for features that predict whether a customer would churn. They contrasted churn definitions commonly used in business administration, marketing, IT, telecommunications, newspapers, insurance, and psychology.…”

Section: A Customer Churn Prediction and Causal Analysismentioning

confidence: 99%

Causal Analysis of Customer Churn Using Deep Learning

Rudd

Huo²,

Xu³

2021

2021 International Conference on Digital Society and Intelligent Systems (DSInS)

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Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar-value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feedforward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.

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Prediction and Causality analysis of churn using deep learning

Cited by 5 publications

References 11 publications

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions

Causal Analysis of Customer Churn Using Deep Learning

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