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