75838 - Business Intelligence and Customer Relationship Management

Learning outcomes

At the end of the course the student, starting from large data sources, will be able to process information on the environment internal and external to the company and to single out the relationships of interdependence and patterns. Moreover, the student will learn where transactional data originate in the CRM process, how to manage an information system, and use data to construct a data warehouse. In particular, this course poses methodological basis to enable the student: - to correctly make predictive analysis (time series, linear and non-linear regression)- to use results to support the decision-making process (statistical decision making) - to understand an ERP system (knowledge management).

 

Course contents

• Analytical CRM.
• Information sources internal to the company: the customer database.
• Data clenaning.
• Customer acquisition.
• Behavioral segmentation and the RFM metric.
• Methods for customer retention and customer churn: survival analysis and logistic regression.
• Psychographic segmentation: a tadem approach.
• The use of qualitative data for customer profiling: the correspondence analysis.
• Look-alike modeling for prospecting.
  

Readings/Bibliography

• Kumar, V. and Petersen, A. (2012) “Statistical Methods in Customer Relationship Management”, Wiley, capitoli 1,2,3.1,3.2,3.4, 4.1,4.2,4.3, 6
  
• Bolasco, S. (1999) “Analisi Multidimensionale dei dati”, Carocci, cap. 4,5, 6.4, 9.4, 9.6, 9.7
  
• Lecture notes
• Kumar and Reinartz (2018) “Customer Relationship Management Concept, Strategy, and Tools”, Springer, capitoli 1, 4.1, 5, 6,8 (recommended)

Teaching methods

Lectures involve the presentation of theoretical and applied issues of the various methods. After each theoretical session a practical tutorial is devoted to applications on real CRM problems. Applications are discussed and replicated during the computer laboratory session using SAS statistical software.

Students are invited to solve and discuss case studies. Home assignments will serve to reinforce class concepts and get familiarity with data analysis and interpretation.  Home assignments will be ungraded. However, solutions (or simply a feedback) will be provided for self-assessment.

Assessment methods

Attending and non attending students will have a written examination consisting in two open questions on theoretical issues (1/3 of grade) and a section requiring production and/or interpretation of statistical outputs (2/3 of grade). The open questions section aims at testing the student's knowledge of the theoretical topics. The practical section is targeted at testing the ability of producing and interpreting statistical outputs, and their translation into applied conclusions in a CRM context. Typical exam questions will be made available during the course. All the students are given to perform tasks of the same difficulty in the same time. It is a 75 minutes written exam with 2 open questions on theory and 2/3 practical exercises using the SAS software. Points awarded for correct answers to each question will be reported in the exam outline. The exam is "closed-book". Students are not allowed to consult references and theoretical information sources while performing the task.

The assessment of the mid-term and final exam will be based on the following grid:

<18 failed

18-23 sufficient

24-27 good

28-30 very good

30 e lode honors

Check the virtual space for details.

Teaching tools

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students. The teaching material includes:

• Lecture notes summarising theoretical topics explained in class
• Open data and lecture notes to follow the practical sessions
  
• Miscellanea: exercises, solutions to assignments, sample exams, follow-up materials

 

Software SAS on Demand for Academics.

Office hours

See the website of Ida D'Attoma