• Fosca Giannotti
  ISTI-CNR, Knowledge Discovery and Data Mining Lab
  fosca [dot] giannotti [at] isti [dot] cnr [dot] it

• Dino Pedreschi
  Università di Pisa, Knowledge Discovery and Data Mining Lab
  pedre [at] di [dot] unipi [dot] it

• Teaching Assistant: Riccardo Guidotti
  ISTI-CNR, Knowledge Discovery and Data Mining Lab
  riccardo [dot] guidotti [at] isti [dot] cnr [dot] it

• Tuesday 16 May 2017: install KNIME (http://www.knime.org).

Organizations and business are overwhelmed by the flood of data continuously collected into their data warehouses and arriving from external sources – the Web above all. Traditional exploratory techniques may fail to make sense of the data, due to its inherent complexity and size. Data mining and knowledge discovery techniques emerged as an alternative approach, aimed at revealing patterns, rules and models hidden in the data, and at supporting the analytical user to develop descriptive and predictive models for a number of business problems. This short course focusses on the main applications scenarios of data mining to challenging problems in the broad CRM domain - Customer Relationship Management.

• Clustering models for customer segmentation. Discussion of real cases. Hands-on project: segmentation of a base of anonymized customers from the retail industry. Clustering models for competitive intelligence.
• Patterns and association rule mining for market basket analysis. Hands-on project: mining association rules from sales data of the retail industry.
• Prediction models for promotion performance and churn analysis. Discussion of real cases. Hands-on project: churn prediction from a base of anonymized customers from the retail industry.
• Analysis of human mobility patterns by mobility data mining from big data. Mining official data for understanding of human behavior.
• Social network analysis for undestanding diffusion phenomena. Viral marketing.
• Application of data mining to geo-marketing. Analysis of innovators. Predictive models for fraud detection.

• Slides (see Calendar).

• Gordon S. Linoff e Michael J. Berry. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, 2011.

• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
  • http://www-users.cs.umn.edu/~kumar/dmbook/index.php

• Data, data everywhere. The Economist, Feb. 2010 download
• Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 link
• Welcome to the yotta world. The Economist, Sept. 2011 download

0. Iris. (for details see https://archive.ics.uci.edu/ml/datasets/iris)

1. Human Resources. (for details see https://www.kaggle.com/ludobenistant/hr-analytics)

2. Telco Churn. (for details see http://didawiki.di.unipi.it/doku.php/dm/mains.santanna.dm4crm.2016)

3. Adult. (for details see https://archive.ics.uci.edu/ml/datasets/Adult)

4. Titanic. (for details see https://www.kaggle.com/c/titanic)

Guidelines:

Each group (2-3 people) is required to deliver a report (max 20 pages including all figures) describing the methods adopted and the discussion of the most interesting achieved results with reference to the tasks listed below. Assume that the report is targeted to a marketing strategist, who is interested to learn the story inferred in the various data mining analyses and to receive suggestions on how to take appropriate actions as a consequence.

1. Data Understanding: useful as a preliminary step to capture basic data property. Distribution analysis, statistical exploration, correlation analysis, suitable transformation of variables and elimination of redundant variables, management of missing values.

2. Pattern Mining Analysis. Problem: prepare data and extract interesting association rules and frequent patterns. The report should discuss the parameters used for the analyses, justifying your findings related to the most interesting rules according to the different measure introduced in the course.

3. Customer Segmentation. Problem: find a high-quality clustering using clustering algorithms and discuss the profile of each found cluster (in terms of the properties that describe the properties of the customers of each cluster). The report should illustrate the adopted clustering methodology and the cluster interpretation. In particular, in case of k-means, it is necessary to discuss the identification of the best value of k and the characterisation of the obtained clusters by using both analysis of the k centroids and comparison of the statistics of variables within the clusters with that in the whole dataset.

4. Classification Analysis. Problem: find a high-quality decision tree for predicting a feature of a customer. The report should illustrate the adopted classification methodology and the decision tree validation and interpretation, describing also the process adopted to select the proposed tree, together with its quality evaluation.

Deadline: send the report by email to all instructors within 23 June 2017. Specify [MAINS] in the subject of the email.

The exam consists in the evaluation of the report of the proposed mining exercises.

• Data Mining for Customer Relationship Management 2016
• Data Mining for Customer Relationship Management 2015
• Data Mining for Customer Relationship Management 2014
• Data Mining for Customer Relationship Management 2013