Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved

[[alternative]]On the Prediction of the Opponents Preferences in Negotiation

計畫編號：NSC91-2213-E032-017研究期間：200208~200307研究經費：408,000[[sponsorship]]行政院國家科學委員

[[alternative]]Computer-Aided Tactics Support in Negotiation

計畫編號：NSC92-2213-E032-028研究期間：200308~200407研究經費：414,000[[sponsorship]]行政院國家科學委員

Using Machine Learning Techniques to Customize the User\u27s Profile, Helps Intelligent TV Decoder’s Design

In today\u27s society due to the increase of the quantity of information is becoming more difficult to find the information we search. Data mining offers us the most important methods and techniques in data analysis. Through this work, we aim to study the several data mining techniques, methods and applications in specific areas. We experiment with an “open software WEKA, to perform some data analysis, presenting the reliability and advantages of data mining classification technique. We use the decision trees technique to achieve the task of classification, to customize user profiles based on their requirements and needs. This paper presents also how machine learning methods can be integrated with agent technology in building more intelligent agents. Using machine learning techniques makes it possible to develop agents able to learn from and adapt to their environment. So a TV decoder can be adapted to the demands of TV viewers. If the decoder initially trained by the demands and needs of viewers, it can display intelligent behavior, suggesting viewers, according to the profile created for each one, shows and movies. The paper concludes with our contributions concerning the application of data mining techniques to customize services according to the requirements and needs of users

A Framework for Profiling Prospective Students in Higher Education

Prospective student acquisition is a prominent issue in higher education marketing. Noel-Levitz (2012) estimated that higher education institutions are losing as high as 75% of the prospects after receiving an inquiry. Another study reported that 80% of the students who decide to apply to a program were influenced by the post-inquiry communications they had received from the higher education institutions (Aarinen, 2012). This chapter attempts to study the underlying concepts from literature and design a framework to extract prospective student profiles and further extend a discussion on how these profiles can be used to address the prospect engagement

Effective Internet Marketing: An Integrated Approach Used By Educational Institutions

The internet and its various applications have been found to be very effective in delivering marketing functions in general and advertising in particular. This paper presents an integrated approach about the way different internet marketing strategies can be used for advertising the degrees of an educational institution using a case study method. This is an important goal for educational institutions in Australia. The paper culminates with a recommendation and a discussion about the ways in which this goal can be accomplished. Challenges associated with the implementation of this integration, including spam, internet market research and target market identification are also discussed

Customer information systems for deregulated ASEAN countries

In similar fashion to western countries, ASEAN countries are also gearing up towards deregulation. Despite potentially different motivating drivers, the ultimate objectives are free market competition leading to efficient pricing signals as well as providing customers with the freedom to choose their electricity provider and benefit from competitive prices. This paper provides an ASEAN electricity market analysis and describes the development of electricity deregulation in ASEAN countries. By way of background it also highlights the objectives of deregulation, the potential challenges and also the impact areas focusing on existing Customer Information Systems (CIS) that have been developed by other utilities. In addition, this paper proposes a new framework for improving CIS for ASEAN utilities facing deregulation. The framework outlines a CIS, which has intelligent features enabling the utility to estimate and predict customer behaviour with respect to consumption patterns. It describes how these features can assist the utility companies to retain their existing customers as well as attract more customers