Browsing by Author "Hadden, John"
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Item Open Access A Customer Profiling Methodology for Churn Prediction(Cranfield University, 2008-04) Hadden, John; Tiwari, Ashutosh; Roy, RajkumarAs markets have become increasingly saturated, companies have acknowledged that their business strategies need to focus on identifying those customers who are most likely to churn. To address this, a method is required that can identify these customers, so that proactive retention campaigns can be deployed in a bid to retain them. To further complicate this, retention campaigns can be costly. To reduce cost and maximise effectiveness, churn prediction has to be as accurate as possible to ensure that only the customers who are planning to switch their service providers are being targeted for retention. Current techniques and research as identified by literature focus primarily on the instantaneous prediction of customer churn. Much work has been invested in this method of churn prediction and significant advancement has been made. However one of the major drawbacks of current research is that the methods available do not provide adequate time for companies to identify and retain the predicted churners. There is a lack of time element in churn prediction. Current research also fails to acknowledge the expensive problem of misclassifying non-churners as churners. In addition, most research efforts base their analysis on customer demographic and usage data that can breach governing regulations. It is proposed in this research that customer complaints and repairs data could prove a suitable alternative. The doctoral research presented in this thesis aims to develop a customer profiling methodology for predicting churn in advance, while keeping the misclassification levels to a minimum. The proposed methodology incorporates time element in the prediction of customer churn for maximising future churn capture by identifying a potential loss of customer at the earliest possible point. Three case studies are identified and carried out for validating the proposed methodology using repairs and complaints data. Finally, the results from the proposed methodology are compared against popular churn prediction techniques reported in literature. The research demonstrates that customers can be placed into one of several profiles clusters according to their interactions with the service provider. Based on this, an estimate is possible regarding when the customer can be expected to terminate his/her service with the company. The proposed methodology produces better results compared to the current state-of-the-art techniques.Item Open Access Soft Computing in the Service Industry(2006-01-01T00:00:00Z) Roy, Rajkumar; Tiwari, Ashutosh; Shah, Satya Ramesh; Hadden, JohnService industries have recently witnessed several innovations, one of which is the widespread use of contact centres in the front of customer service management. Service encounters based on contact centres have raised new issues about the management of services. Customer contact centres allow a company to build, maintain, and manage customer relationships by solving problems and resolving complaints quickly, having information, answering questions, and being available usually 24 hours a day, seven days a week, 365 days of the year (Prabhaker, Sheehan and Coppett, 1997). Application of the technologies involved in contact center operations can play a key role in accessing more customers, and in providing better quality services especially where additional or extended services become available. It is necessary to understand individual customers from all levels to enable the advisor to help them more efficiently and thus providing better customer satisfaction. Within the current CCC environment there is a problem of high staff turnover and lack of suitably trained staff at the right place for the right kind of customer. Thus from a business point of view any available advisor should be able to handle a customer with consistent and good quality service (Azarmi, et al., 1998). There is also a shortage of good quality skilled staff due to retention problem that exists within current environment. This is supported by Doganis et al., (2005) who state “due to strong competition that exists today, most manufacturing organisations are in a continuous effort for increasing their profits and reducing their costs”. More and more effort is going into customer behaviour modelling and customer retention in a bid to prevent valuable customers from moving to competing companies. This section will discuss the identified research and progress that has been made in the ongoing process of improving company’s business strategies using soft computing techn