Learning Customer Pro les to Generate Cash over the Internet Christophe Giraud-Carrier1 and Mike Ward2
1
Department of Computer Science, University of Bristol Bristol, BS8 1UB, England 2
[email protected]
BT Laboratories, Martlesham Heath Ipswich, IP5 7RE, England
[email protected]
Abstract
This paper describes a customer handling system that combines the World-Wide Web and Machine Learning techniques to generate cash over the Internet. The use of the World-Wide Web increases exibility in human-system interaction, while the use of machine learning improves targeting and customer satisfaction, thereby enhancing sales potential. O -line testing based on a number of realistic scenarios demonstrates promise and on-line trials are now under consideration.
1 Introduction In an increasingly competitive market, customer handling has become a major priority for telecommunications companies. This is particularly true of large, well-established, traditional companies for which providing the infrastructure, technology and basic services is no longer sucient. To gain, maintain and improve their position in the market, companies are nding it essential to further invest in and upgrade their customer handling practises to secure the loyalty of existing customers and meet the needs of demanding new ones. This paper reports on experimental work conducted with British Telecom (BT) to increase the versatility and e ectiveness of current customer
handling practises. The focus here is on one of the key competences of people (or machines) involved in customer handling, namely, the ability to generate sales. Most existing customer service centres at BT work on the basis of customers telephoning their enquiries on a dedicated line and human operators answering the queries. To maximise bene ts for both the customers and BT, there is pressure on the operators to attempt to sell additional equipments and services to the caller. Although there are some advantages to this human-driven approach (e.g., \feel good" factor), there are also a number of important limitations. The information available to operators is extremely restricted and of little value for a suitable, well-targeted o er. As a result, o ers of (usually free) equipment or services are made to all callers who do not have them already. This approach is akin to random targeting and is likely to yield only about 50% acceptance on average. Hence, customer satisfaction is low, and time and money are wasted on a large number of uninterested callers. Current practises also rely on the operators' wit to spot sales opportunities and win them. This approach has limited reliability and is probably con ned to current, well-advertised discount packages. It is dicult to expect the human operators to do more than that while carrying out a generally unrelated conversation focused on the caller's query. Finally, BT has the objective of moving its customer service people up the value chain. Hence, BT is extremely interested in automated systems that can handle day to day simple situations, leaving BT people free to build relationships with customers and to deal with customers' more demanding and urgent needs. In this paper, a machine learning (ML)-driven approach to customer handling, which fosters the generation of sales over the Internet, is taken. The proposed system has a front-end, HTML-based interface and a backend, ML-based processing unit. As customers interact with the system, pro les are learned automatically and used to target o ers at those who are most likely to accept them. O -line testing based on a number of scenarios has proved successful and on-line trials are now under consideration. The paper is organised as follows. Section 2 outlines the contribution of ML, highlights the main features of FLARE, the learning algorithm used by the system, and describes the learning task. Section 3 discusses the interface and its integration with FLARE. Section 4 summarises the results of o -line, scenario-based experiments. Finally, section 5 concludes the paper.
2 Back-End Machine Learning Inductive machine learning is concerned with automatically generating rules from examples or extracting patterns and critical features from observations. In particular, with sucient information about customers, ML may
be used to construct pro les automatically, i.e., to discover patterns that link customers' circumstances to preferred equipment and services. This information can in turn be used to improve the match between targeted customers and o ers made. That is, the current \blind" targeting can be replaced by more informed targeting, based on experience with other customers. In addition, pro les need not remain static. ML techniques o er enough exibility to adapt to changes in circumstances, behaviour and attitudes in the customer base. Hence, ML greatly increases the reliability, robustness and consistency of the current, ad hoc practises. FLARE [1, 2] is used here for learning. A complete description of FLARE is beyond the scope of this paper. Only its main features are highlighted here. FLARE integrates inductive learning using prior knowledge together with reasoning in a non-recursive, propositional setting. FLARE learns incrementally by continually revising its knowledge base in the light of new evidence. Prior knowledge is generally given by a teacher and takes the form of pre-encoded rules. FLARE implements a simple form of rulebased reasoning, combined with similarity-based reasoning. Hence, if a new situation fails to satisfy all existing rules, its outcome is predicted based on similarity with recorded \cases." Because it supports prior knowledge, FLARE need not always \reinvent the wheel." Rather, it may be primed with expertise available from human operators. The inductive mechanisms are then used to re ne, update or modify that expertise over time. Similarly, if no expertise is available (e.g., new products), FLARE can still cope through induction only. A full account of FLARE is in [1]. Here, FLARE's learning task is described as follows. A customer's circumstances are encoded as a set of attribute-value pairs. Attributes consist of equipment that may already be owned by the customer, information about phone bills and a variety of other possibly relevant questions, including: Do you work from home? Is your house built on more than one-level? Do you or your partner spend a lot of time away from home? etc. The equipment and services that the system may suggest to the customer include such things as a fax machine, an additional telephone, a mobile phone and special discount packages. Learning is e ected as customers enter in their current information and give feedback on FLARE's suggestions. Note that FLARE is a supervised learner and thus can only learn when feedback is provided. FLARE uses the customer's feedback to update its knowledge base. Such updates include creating new pro les, generalising existing pro les and removing obsolete or redundant pro les (details in [2]).
3 Front-End HTML Interface The explosion of the Internet o ers tremendous opportunities for telecommunications companies, both to deliver new technology and to improve existing services. In particular, The Internet is a global, distributed resource. It becomes irrelevant where people are or what they want as anyone can be connected to anything in theory. The need for expensive telephone switching and redirection disappears. The Internet broadens the customer base to casual browsers. People may simply stumble across BT while \sur ng the net." Customers who ring BT's dedicated customer service line have a speci c query or purpose and may not wish to be solicited about new equipments or services. The Internet tends to disperse such concerns. The Internet is an informal communication medium. Questions that may be perceived as intrusive when asked by another human being may seem less so when presented in print as part of a computer session. Electronic commerce is becoming a reality. For example, BT Shop is a one-stop, electronic shop where customers may order new equipment from BT without leaving their home. To capitalise on such advantages, and eventually link up to, for example BT Shop, FLARE is placed behind a front-end HTML-based interface. The complete system thus becomes fully interactive. It is run simply by pointing a Web browser to an appropriate URL. A typical session proceeds as follows. 1. A form is displayed containing the questions regarding the customer's personal circumstances and current usage of telecommunications (see section 2). Default values are set for each question. 2. The customer answers all of the question and submits the form for processing. 3. Using its current knowledge base, FLARE reasons about the information given by the customer in an attempt to make useful, well-targeted suggestions as to which products or services the customer ought to get and which are deemed unnecessary. 4. An HTML page is displayed containing FLARE's suggestions to the customer. If no product or service can be suggested, a hyperlink to BT Shop is provided for the customer to peruse.
5. The customer may stop at this stage. However, the customer is encouraged to provide the system with feedback by \ticking" the suggestions to get he/she agrees with, as well as the products or services that he/she would actually consider getting despite the system's suggestion that they are unnecessary. 6. An HTML page is displayed thanking the customer for his/her visit. As mentioned in section 2, the feedback provided by the customer is used by FLARE to update its knowledge base. By default, if the customer submits the page without any ticks, the assumption is that he/she disagrees with the suggestions made to get certain products or services and agrees with the suggestions made to ignore the others. To further assist the customer, a set of HTML pages describing each product and service used in the system has also been designed. Hyperlinks to these pages are automatically set up when the corresponding product or service appears as a suggestion of the system. Rudimentary on-line help is also available via a hyperlink to an HTML page that explains what the system does and how to use it.
4 Experiments A set of well-designed scenarios has been used to validate the system o -line. Scenarios consist of a number of ctitious customers. Some of the customers are used to train the system, whilst others are used to test it. Each scenario is constructed to highlight one or more of the main features of the system. Relevant performance measures, such as hit ratio (i.e., % of o ers made that are accepted by the customer), have been collected. Results demonstrate the following. Increase in customer satisfaction as the system learns. Adaptation to changing environments (e.g., switching from phonecards to mobile telephones). Improved targeting after learning (i.e., most o ers made are accepted by the caller). Ability to arbitrarily force the system to make unconditional o ers to all customers during, for example, temporary special o ers or trial periods.
5 Conclusion This paper describes an experimental system that combines the World-Wide Web with ML techniques to generate sales over the Internet. The use of the Web increases versatility, while the use of ML improves targeting. Ofline testing with a number of scenarios demonstrate promise. Live, on-line trials of the system are now under consideration. Similar trials, involving the use of Case-Based Reasoning for fault-diagnosis, have been carried out successfully by BT and the technology is being assessed for company-wide deployment. This work demonstrates the potential of emerging technologies (e.g., ML, WWW) in improving current practises in customer handling. Whilst the system described here needs improvement, the ideas and techniques discussed are very general and may thus be brought to bear on a number of other related practical business problems, such as: Maintenance (e.g., fault diagnosis, repairs), Intelligent agents for home users (e.g., monitoring of telephone usage, automatic dial, etc.), and Intelligent call routing.
Acknowledgements
This research was funded by a BT Short-Term Research Fellowship held by the rst author.
References
[1] C.G. Giraud-Carrier and T.R. Martinez, \An Integrated Framework for Learning and Reasoning," Journal of Arti cial Intelligence Research, vol. 3, pp. 147{185, 1995. [2] C. Giraud-Carrier, \FLARE: Induction with Prior Knowledge," Research and Development in Expert Systems XIII (Proceedings of Expert Systems'96), pp. 11{24, 1996.
