Optimising customer support in contact centres using soft computing approach
| dc.contributor.author | Shah, Satya Ramesh | - |
| dc.contributor.author | Roy, Rajkumar | - |
| dc.contributor.author | Tiwari, Ashutosh | - |
| dc.contributor.editor | Editor | - |
| dc.date.accessioned | 2011-10-11T07:15:20Z | |
| dc.date.available | 2011-10-11T07:15:20Z | |
| dc.date.issued | 2006-10-31T00:00:00Z | - |
| dc.description.abstract | 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 | en_UK |
| dc.identifier.isbn | 1-86194-126-9 | - |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/1211 | |
| dc.subject | Customer behaviour modelling | en_UK |
| dc.subject | Categorisation | en_UK |
| dc.subject | Customer Service Advisor | en_UK |
| dc.subject | Soft Computing | en_UK |
| dc.subject | Intelligent Information Modelling | en_UK |
| dc.subject | Contact/Call Centre environment | en_UK |
| dc.subject | DEG report | en_UK |
| dc.title | Optimising customer support in contact centres using soft computing approach | en_UK |
| dc.type | Report | - |