MSCI 526: Data Mining for Marketing, Sales and Finance
(Phase 2 – Spring Term)
Tutor: Dr Nicos Pavlidis
How do financial institutions predict whether a person will pay back a mortgage, credit card or loan they grant? How do marketers predict the likelihood of a customer to react to a shopping catalogue or a special discount in a direct mailing? How does AMAZON determine which products to present to you in the hope of up-selling or cross-selling? At the heart of many real management problems are increasingly large data sets that need to be analysed efficiently in order to gain novel and potentially useful insights, and to build models to enable managers to make better informed decisions. Data mining has now been firmly established as a discipline of its own right in supporting managerial decision making using predictive and descriptive modelling. Data Mining brings together real, large sale datasets, and algorithms from statistics, machine learning and computational intelligence that often do not draw upon rigorous statistics, but work efficiently with impure data.
This module ‘Data Mining for Marketing, Sales and Finance’ develops further the students’ modelling skills on synthetic and empirical data by showing simple statistical methods and introducing novel methods from artificial intelligence and machine learning. The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. We will consider both Data Mining methods for descriptive modelling, exploration & data reduction that aim to simplify and add insights to large, complex data sets, and Data Mining methods for predictive modelling that aim to classify and cluster individuals into distinct, disjoint segments with different patterns of behaviour. Although an understanding of basic statistics may allow you to better appreciate the working of models in regression and classification tasks, the course is focussed on the application of algorithms on real datasets using software, and making real predictions. The skills and knowledge enable you to pursue jobs in the area of Business & Marketing Analytics in the role of a Business Analyst or Consultant who often apply these methods and techniques.
The course will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario.
Learning Outcomes
By the end of the course you should be able to:
- understand general modelling concepts in relation to complex models
- use a wide range of data mining methods to handle data of different types & applications.
- understand how these methods may be applied in practical management contexts
- use & apply SAS Enterprise Miner to deal with complexity and large datasets
Outline Lecture Plan
- Introduction to Data Mining
- SEMMA Process of Data Mining
- Methods for data exploration & manipulation
- Methods for data reduction & feature selection
- Evaluating Classification Accuracy
- Data Mining Methods for Classification
- Discriminant Analysis
- Logistic Regression
- Decision Trees
- Artificial Neural Networks
- Data Mining Methods for Clustering
- k-means clustering
- hierarchical clustering
- Data Mining applications in Credit Scoring
- Data Mining applications in Customer Relationship Management
Contact Time
There will be 20 hours of lectures (in 10 sessions) and 10 hours of workshops (in 5 sessions) held in an appropriate PC lab.
Assessment
Individual Coursework (60%)
Group Coursework (40%)
Both assignments will involve building multiple models following the complete data mining process to answer a practical management science application.
Core Texts
Books for MSc / management students
- Berry, M. J. A. and G. Linoff (2000). Mastering data mining : the art and science of customer relationship management. New York, NY [u.a.], Wiley Computer Publ.
- Berry, M. J. A. and G. Linoff (2004). Data mining techniques : for marketing, sales, and customer relationship management. Indianapolis, Ind., Wiley Pub.
- Linoff, G. and M. J. A. Berry (2001). Mining the Web : transforming customer data into customer value. New York, John Wiley & Sons.
- Miller T.W. (2005) Data and Text Mining – A Business Applications Aproach, Pearson, New Jersey
Books for technical / research students
- Tan, P.-N., M. Steinbach, et al. (2005). Introduction to data mining. Boston, Pearson Addison Wesley.
- Weiss, S. M. and N. Indurkhya (1998). Predictive data mining : a practical guide. San Francisco, Morgan Kaufmann Publishers.
