Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

Thakkar, Hiren Kumar; Desai, Ankit; Ghosh, Subrata; Singh, Priyanka; Sharma, Gajendra

doi:10.1155/2022/9028580

Cited by 19 publications

(9 citation statements)

References 28 publications

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“…Customer churn is defined as the percentage of customers that decide to exit, i.e the customer stops buying products or services from a company. All businesses eventually reach a point where market saturation makes acquiring new customers more expensive than retaining existing customers [5], [32]. Businesses operating in new markets or startups place less emphasis on churn rate while growth is still cheap.…”

Section: Churnmentioning

confidence: 99%

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Ahlstrand¹,

Boldt²,

Borg³

et al. 2023

Linköping Electronic Conference Proceedings

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During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However, implementing and generating value from AI in the customer life cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both the model performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer life cycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regarding outcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating both policy outputs and outcomes to ensure reliable and trustworthy AI.

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Section: Churnmentioning

confidence: 99%

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Ahlstrand¹,

Boldt²,

Borg³

et al. 2023

Linköping Electronic Conference Proceedings

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“…Zhu et al (2020) proposed a trajectorybased deep sequential method TR-LSTM and utilized the long short-term memory neural network (LSTM) to conduct sequential modeling. Thakkar's et al (2022) study proposes AdaBoostWithCost algorithm, which improves the discrete AdaBoost algorithm for churn prediction on telecom market. Research by Al-Shatnwai et al (2020) proposes one of the most powerful machine learning classifiers XGBoost as a customer retention model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The marketing literature proves that acquiring a new customer is 5-10 times more expensive than retaining an existing one. Thus, effective management of customer churn and understanding the reasons for customer outflows have become a quite crucial task for mobile operators (Thakkar et al, 2022;. Customer relations and customer knowledge have positive influence on company's performance and service quality of the telecom enterprises (Abd-Elrahman et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Customer churn prediction model: a case of the telecommunication market

et al. 2022

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The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

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“…In [12], a class-dependent cost-sensitive boosting approach, AdaBoostWithCost, is presented to reduce the churn cost. It illustrates the empirical evaluation of the proposed method that reliably outperforms the discrete AdaBoost approach concerning telecom churn prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Haridasan¹,

Muthukumaran²,

Hariharanath³

2023

Intelligent Automation &Amp; Soft Computing

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Customer retention is one of the challenging issues in different business sectors, and various firms 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 telecommunication 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 performs pre-processing to transform the original data into a useful format. Besides, the SBLSTM model is employed to categorize data into churners and non-churners. To improve the CCP outcomes of the SBLSTM model, an optimal hyperparameter 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.

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Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

Cited by 19 publications

References 28 publications

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Customer churn prediction model: a case of the telecommunication market

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

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