Intelligent process analytics for CRM

Many organisations use decision models in their processes such as tables or trees to provide decision support to their operational divisions. For example, in fault management, customer contact centre operators usually use a decision model in the form of prescribed interviews. Based on the answers given by customers, the operator navigates through the decision model to reach an assessment of the problem. In order to achieve customer satisfaction and operational excellence, it is very important to constantly monitor the performance of a decision model not only on an overall level, but also on the level of individual decisions. In this paper we present a configurable business process analytics tool, known as the intelligent Universal Service Management System, that constantly monitors decision data and is capable of optimising the decisions based on high-level business objectives. We explain the various features of the software and show how it can be used to optimise decision processes. We also show how we can easily provide a customised version to monitor the performance of provision processes.     

Adaptive user modelling in intelligent telephone and email assistants

With the burgeoning complexity and capabilities of modern information appliances and services, user modelling is becoming an increasingly important research area. Simple user profiles already personalise many software products and consumer goods such as digital TV recorders and mobile phones. A user model should be easy to initialise, and it must adapt in the light of interaction with the user. In many cases, a large amount of training data is needed to generate a user model, and adaptation is equivalent to retraining the system. This paper briefly outlines the user modelling problem and work done at BTexact on an intelligent personal assistant (IPA) which incorporates a user profile. We go on to describe FILUM, a more flexible method of user modelling, and show its application to the telephone assistant and email assistant components of the IPA, with tests to illustrate its usefulness.     

Towards real-time business intelligence

Analysing data to predict market trends of products and services and to improve performances of enterprise business systems has always been part of running a competitive business. But it is becoming essential nowadays that not only is the analysis done on real-time data, but also actions in response to analysis results can be performed in real time and instantaneously change parameters of business processes. This paper discusses issues and problems of current business intelligence systems, and then outlines our vision of future real-time business intelligence. We present a list of emerging technologies which could contribute to the realisation of real-time business intelligence and some examples of applying them to improve BT’s systems and services.     

Towards automated customer self-help

Customer self-help is a major driver for business today because it provides 24/7 services at a fraction of the cost of call centre agents. However, despite the proliferation of self-help sites, most customer self-help sites have attracted few customers. This paper discusses where advanced technologies could help to improve the situation. We also outline our vision of future customer contact centres and challenges. Finally we present two advanced proof-of-concept demonstrators towards automated customer self-help. The first one has now been under trial with BT field engineers.     

Fuzzy logic and the Internet (FLINT): Internet, World Wide Web, and search engines

 Retrieving relevant information is a crucial component of cased-based reasoning systems for Internet applications such as search engines. The task is to use user-defined queries to retrieve useful information according to certain measures. Even though techniques exist for locating exact matches, finding relevant partial matches might be a problem. It may not be also easy to specify query requests precisely and completely – resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for large repositories such as World Wide Web, a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise or fuzzy query specification or search.     

Towards Engineering Intelligent Systems

This paper sets the rational and historical background for this issue of the Journal. It also provides editorial overviews of the three artificial intelligence (AI) technology themes of 'constraint optimisation and scheduling', 'software agents' and 'soft computing' that are covered by the papers in this issue.     

Soft Computing for Intelligent Knowledge-based Systems

Knowledge-based systems are founded on the idea that knowledge should be declarative, so that it can be easily read, understood, and altered by a human user as well as by a machine. Logic fulfils these criteria, and logic programming has been widely used for implementing knowledge-based systems. One major shortcoming of logic programming is the lack of a mechanism to deal with the uncertainty inherent in many knowledge-based systems. Soft computing is a key technology for the management of uncertainty, although so far its major successes have been centred on fuzzy control rather than higher level information management. This paper outlines some of the issues related to the area of soft computing in knowledge-based systems, and suggests some simple problems to test the capabilities of software. Fril is discussed as an implementation language for knowledge-based systems involving uncertainty, and some of its applications are outlined.     

Estimating Travel Times of Field Engineers

BT's Work Manager platform uses a dynamic scheduler to plan the daily jobs of field engineers. In order to produce reliable schedules, Work Manager requires accurate estimates about the time an engineer spends on travelling from one job location to the next (inter-job time) and how much time is required to complete a job. We have developed a generic platform called Intelligent Travel Time Estimation and Management System (ITEMS) that we have used to derive a specialised version for modelling the behaviour of BT's mobile workforce. This travel time estimation system (TTE) receives job data every night and learns a new estimation model on a daily basis. When a new estimation model significantly differs from the currently used model, the new estimation model is automatically uploaded into Work Manager. The estimation algorithm has been used in a loosely coupled trial for about two years. Currently, we are undertaking trials of TTE — a tightly coupled Web-based system that fully automates the learning and management of estimation models, providing a graphical user interface that displays rich detail about the travel patterns of BT's mobile workforce. During the trial period we have observed that using TTE can quickly improve the accuracy of travel time estimates by up to 10%. This revised version was published online in July 2006 with corrections to the Cover Date.     

Intelligent Multi-modal Systems

Research in human/computer interaction has mainly focused on natural language, text, speech and vision primarily in isolation. Recently there have been a number of research projects that have concentrated on the integration of such modalities using intelligent reasoners. The rationale is that many inherent ambiguities in single modes of communication can be resolved if extra information is available.This paper describes an intelligent multi-modal system called the Smart Work Manager. The main characteristics of the Smart Work Manager are that it can process speech, text, face images, gaze information and simulated gestures using the mouse as input modalities, and its output is in the form of speech, text or graphics. The main components of the system are the reasoner, a speech system, a vision system, an integration platform and the application interface. The overall architecture of the system will be described together with the integration platform and the components of the system which include a non-intrusive neural network based gaze-tracking system. The paper concludes with a discussion on the applicability of such systems to intelligent human/computer interaction and lessons learnt in terms of reliability and efficiency.     

Fuzzy queries, search, and decision support system

 The process of ranking (scoring) has been used to make billions of financing decisions each year serving an industry worth hundreds of billion of dollars. To a lesser extent, ranking has also been used to process hundreds of millions of applications by U.S. Universities resulting in over 15 million college admissions in the year 2000 for a total revenue of over $250 billion. College admissions are expected to reach over 17 million by the year 2010 for total revenue of over $280 billion. In this paper, we will introduce fuzzy query and fuzzy aggregation as an alternative for ranking and predicting the risk for credit scoring and university admissions, which currently utilize an imprecise and subjective process. In addition we will introduce the BISC Decision Support System. The main key features of the BISC Decision Support System for the internet applications are (1) to use intelligently the vast amounts of important data in organizations in an optimum way as a decision support system and (2) to share intelligently and securely company's data internally and with business partners and customers that can be process quickly by end users.