Browsing by Author "Shah, Satya Ramesh"

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    Customised customer support using a soft computing approach
    (2005-11-01T00:00:00Z) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh; Majeed, Basim
    This paper describes the research and development of a methodology to identify the type of information required by the service advisor (CSA) within customer contact centre (CCC) environment. Data was collected through case studies carried out within five customer contact centres to derive the categories for customers and advisors based on demographic, experience, business value and behavioural attributes. We provide the methodology to develop a fuzzy expert system which assigns a new customer or advisor to the predefined categories. The authors have explained the steps which were followed for the development of the fuzzy expert system. A prototype system has been designed and developed to identify the type of customer and CSA based on the demographic, experience and behavioural attributes. The authors illustrate analysis with real data, based on the work with large scale customer contact centres. Validation of the information requirement model was carried out at the contact centres.
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    The development of an intelligent decision support framework in the contact centre environment
    (Cranfield University, 2007-12) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh
    In a time of fast growing technology and communication systems, it is very important for the industry and the corporations to develop new contact centre environment technologies for better customer contact requirements. The integration of contact centre (CC) into day-to-day organisational operations represents one of the most promising trends in the 21 st century economy. Whatever the nature or point of contact, customers want a seamless interaction throughout their experience with the company. Customers receive more personalised experience, while the company itself can now provide a consistent message across all customer interactions. Based on the literature studies and the research carried out within the contact centre industry through the case studies, the author identified the customer and advisor behavioural attributes along with demographic, experience and others that later are used to derive the categories. Clustering technique identified the categories for customers and advisors. From the initial set of categories, fuzzy expert system framework was derived which assigned a customer or advisor with the pre-defined set of categories. The thesis has proposed two novel frameworks for categorisation of customer and advisor within contact centres and development of intelligent decision support framework that displays the right amount of information to the advisor at the right time. Furthermore, the frameworks were validated with qualitative expert judgement from the experts at the contact centres and through a simulation approach. The research has developed a novel Soft Computing based fuzzy logic categorisation framework that categorises customer and advisor on the basis of their demographic, experience and behavioural attributes. The study also identifies the behavioural aspects of customer and advisor within CC environment and on the basis of categorisation framework, assigns each customer and advisor to that of a pre-defined category. The research has also proposed an intelligent decision support framework to identify and display the minimum amount of information required by an advisor to serve the customer in CC environment. The performance of the proposed frameworks is analysed through four case studies. In this way this research proposes a fully tested and validated set of categorisation and information requirement frameworks for dealing with customer and advisor and its challenges. The research also identifies future research directions in the relevant areas.
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    Development of fuzzy expert system for customer and service advisor categorisation within contact centre environment.
    (2004-09-01T00:00:00Z) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh
    In this paper, we describe the research and development of a fuzzy expert system methodology for categorising customer and customer service advisor (CSA) within customer contact centre (CCC) environment. On the basis of data collected through case studies carried out within customer contact centre, two step clustering analysis within SPSS was used to derive the categories for customers and advisors based on demographic, experience, business value and behavioural attributes. The fuzzy expert system assigns a new customer or advisor to the pre-defined categories and provides the corresponding membership values given into the system using fuzzy logic. The author has explained the steps which were followed for the development of the fuzzy expert system. A prototype system has been designed and developed to identify the type of customer and CSA based on the demographic, experience and behavioural attributes. Experimental results are provided and the methodology is validated within the case study approach.
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    Optimising customer support in contact centres using soft computing approach
    (2006-10-31T00:00:00Z) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh; Editor
    This paper describes the research and development of a methodology for optimising the customer support in contact centres (CC) using a soft computing approach. The methodology provides the categorisation of customer and customer service advisor (CSA) within CC. Within the current contact centre environment there is a problem of high staff turnover and lack of trained staff at the right place for the right kind of customer. Business needs to assign any available advisor to a customer and provide consistent and good quality of service. There is a need to identify the right amount of information to be displayed on the screen considering both the customer and the assigned advisor background. On the basis of data collected through case studies carried out within five customer contact centres, two step clustering analysis was used to derive the categories for customers and advisors based on demographic, experience, business value and behavioural attributes. We provide the methodology to develop a fuzzy expert system which assigns a new customer or advisor to the pre-defined categories. The authors have explained the steps which were followed for the development of the fuzzy expert system. A prototype system has been designed and developed to identify the type of customer and CSA based on the demographic, experience and behavioural attributes. The authors illustrate analysis with real data, based on the work with large scale customer contact centres. The CSA’s can play different roles and have different level of autonomy, but at the end they are humans with heart and voice. While product purchases, lifestyle information and billing data provide important information about customers, it is call detail records that describe a customer’s behavior and define their satisfaction with the services offered. Call detail records describe the transactions between customer and the company. This study describes the research and development of methodology for categorizing customer and customer service advisor within contact centre environment. On the basis of the categories derived for customer and service advisor; the minimum amount of information required by the CSA to serve the customer is analysed and discussed within the paper. The information requirement framework provides the amount of information which is required by the CSA on the basis of {customer, advisor} relationship. A promising area for future work is that of data mining the records within contact centres. The methodology for proposed fuzzy expert system and its application to CC setting should be of interest to many industry sectors including telecommunications and contact centre environme
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    Soft Computing in the Service Industry
    (2006-01-01T00:00:00Z) Roy, Rajkumar; Tiwari, Ashutosh; Shah, Satya Ramesh; Hadden, John
    Service 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
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    Technology selection for human behaviour modelling in contact centres
    (2006-01-01T00:00:00Z) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh; Editor
    Customer service advisors can play different roles and have different level of autonomy, but at the end they are humans with heart and voice. While product purchases, lifestyle information and billing data provide important information about customers, it is call detail records that describe a customer’s behaviour and define their satisfaction with the services offered. Call detail records describe the transactions between customer and the company. This study looks on different techniques that can be used to model customer and CSA (customer service advisor) behaviour within a contact centre environment. A brief overview of the contact centre environment is discussed focusing on issues of customer and service advisor and the need to categorise customer and advisor within contact centre environment. The findings from the case study analysis within the current contact centres, provides the authors with understanding of different behaviour observed for customer and CSA’s within contact centres. The study also examines different human behaviour modelling techniques which the authors are interested in using to develop a model which can categorise the human with respect to demographic, experience and behavioural attributes within the context. Through the study it can be seen that soft computing techniques provide a major role in modelling of human behaviour and thus providing better results where this technique can be applied. The authors have also carried out a comparative analysis of all the techniques discussed within the paper and as seen from the analysis that soft computing techniques are widely used to model the user/human behaviour and thus can provide a platform for future research. Soft computing represents a significant paradigm shift in the aim of computing, a shift that reflects the fact that the human mind, unlike state of the art computers, possesses a remarkable ability to store and process information, which is pervasively imprecise, uncertain, and lacking in categoric