Case Study on CRM: Detecting Likely Churners with Limited Information of Fixed-line Subscriber

Zhang, Yingying; Qi, Jiayin; Hu, Shu; Li, Yuanquan

doi:10.1109/icsssm.2006.320745

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Zhang et al (2006) also found that the overall revenues from the last 6 months affects the survival time of customers. The results of the present study also appear to indicate that survival time increases as the monthly average of off-peak calls increase.…”

Section: Model Estimationmentioning

confidence: 99%

“…In fact, the probability that a given mean customer cancels his relationship with the service provider increases as the customer lifetime increases (for relationships with less than approximately 3.5 years), and then decreases. Different studies have also obtained duration dependence (e.g., Kim and Yoon, 2004;Schweidel et al, 2008;Seo et al, 2007;Zhang et al, 2006).…”

Section: Exhibit 1 -Hazard Curve Exhibit 2 -Survival Curvementioning

confidence: 99%

“…We also provide evidence that the payment method and the number of invoices in debt do not influence the customer churn. Nevertheless, Zhang et al (2006) show that the payment method affects the customer churn.…”

Section: Model Estimationmentioning

confidence: 99%

“…In fact, the considerable increase of business competition in the Portuguese fixed telecommunications industry over the last decades has given rise to a phenomenon of customer switching behaviour, and, thus, high customer churn rates, which has serious consequences for the financial performance of the firms and, therefore, for the economy. Several researchers have mentioned that customer churn is the main reason of profitability losses in the telecommunications industry, due to losses on current and potential revenues, marketing costs, and brand image (e.g., Ahn et al, 2006;Qian et al, 2006;Zhang et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detecting Customer Defections: An Application of Continuous Duration Models

Portela¹,

Menezes²

2011

JGSM

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Section: Model Estimationmentioning

confidence: 99%

Section: Exhibit 1 -Hazard Curve Exhibit 2 -Survival Curvementioning

confidence: 99%

Section: Model Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Customer Defections: An Application of Continuous Duration Models

Portela¹,

Menezes²

2011

JGSM

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Churn prediction in Turkey's telecommunications sector: A proposed multiobjective–cost‐sensitive ant colony optimization

Özmen

Aydoğan

Delice

et al. 2019

WIREs Data Min & Knowl

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Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot be ignored. For this reason, studies on cost‐sensitive classification approaches have gained importance in recent years. The characteristics of telecommunications datasets such as high dimensionality and imbalance are making it difficult to achieve the desired performance for churn prediction. By taking this into consideration, we propose a multiobjective–cost‐sensitive ant colony optimization (MOC‐ACO‐Miner) approach which integrates the cost‐based nondominated sorted genetic algorithm feature selection and multiobjective ACO based cost‐sensitive learning. MOC‐ACO‐Miner is applied to one of Turkey's top 100 information technology companies for customer churn‐prediction. Finally, experiments find out that the model performs quite well with the area under receiver operating characteristic curve values of 0.9998 for predicting churners and therefore it can be beneficial for the highly competitive telecommunications sector. This article is categorized under: Algorithmic Development > Association Rules Application Areas > Industry Specific Applications Technologies > Prediction Technologies > Data Preprocessing

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